From 746a19fa8896a209dee15b3a423294b10c8bbcb4 Mon Sep 17 00:00:00 2001
From: HoiM <hoiming0730@gmail.com>
Date: Fri, 21 Feb 2025 22:22:43 +0800
Subject: [PATCH 1/3] add chinese translation

---
 zh-cn/1_instruction_tuning/README.md          |    30 +
 zh-cn/1_instruction_tuning/chat_templates.md  |   120 +
 .../notebooks/.env.example                    |     2 +
 .../notebooks/chat_templates_example.ipynb    |  5735 +++++
 .../notebooks/sft_finetuning_example.ipynb    |   273 +
 .../supervised_fine_tuning.md                 |    41 +
 zh-cn/2_preference_alignment/README.md        |    40 +
 zh-cn/2_preference_alignment/dpo.md           |    72 +
 .../notebooks/dpo_finetuning_example.ipynb    |  5496 +++++
 .../notebooks/orpo_finetuning_example.ipynb   |  8643 +++++++
 zh-cn/2_preference_alignment/orpo.md          |    83 +
 .../README.md                                 |    39 +
 .../images/lora_adapter.png                   |   Bin 0 -> 1239572 bytes
 .../lora_adapters.md                          |   126 +
 .../notebooks/finetune_sft_peft.ipynb         |   519 +
 .../notebooks/load_lora_adapter.ipynb         |  5061 ++++
 .../prompt_tuning.md                          |    74 +
 zh-cn/4_evaluation/README.md                  |    40 +
 zh-cn/4_evaluation/automatic_benchmarks.md    |   137 +
 zh-cn/4_evaluation/custom_evaluation.md       |   135 +
 ...hteval_evaluate_and_analyse_your_LLM.ipynb | 19422 ++++++++++++++++
 zh-cn/4_evaluation/project/README.md          |    81 +
 .../4_evaluation/project/annotate_dataset.py  |   129 +
 zh-cn/4_evaluation/project/create_dataset.py  |    72 +
 zh-cn/4_evaluation/project/evaluation_task.py |    86 +
 .../4_evaluation/project/generate_dataset.py  |   168 +
 .../images/domain_eval_argilla_view.png       |   Bin 0 -> 197693 bytes
 .../images/domain_eval_dataset_viewer.png     |   Bin 0 -> 89114 bytes
 zh-cn/5_vision_language_models/README.md      |    40 +
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 .../images/VLM_Process.png                    |   Bin 0 -> 848749 bytes
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 .../notebooks/vlm_sft_sample.ipynb            |   461 +
 .../notebooks/vlm_usage_sample.ipynb          |   473 +
 .../vlm_finetuning.md                         |   102 +
 zh-cn/5_vision_language_models/vlm_usage.md   |    80 +
 zh-cn/6_synthetic_datasets/README.md          |    39 +
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 .../instruction_datasets.md                   |   204 +
 .../notebooks/instruction_sft_dataset.ipynb   |   123 +
 .../notebooks/preference_dpo_dataset.ipynb    |   118 +
 .../preference_datasets.md                    |   155 +
 zh-cn/7_inference/README.md                   |    36 +
 zh-cn/7_inference/inference_pipeline.md       |   170 +
 .../7_inference/text_generation_inference.md  |   138 +
 zh-cn/8_agents/README.md                      |    36 +
 zh-cn/8_agents/code_agents.md                 |    68 +
 zh-cn/8_agents/custom_functions.md            |    71 +
 zh-cn/8_agents/notebooks/agents.ipynb         |   197 +
 zh-cn/8_agents/retrieval_agents.md            |   122 +
 51 files changed, 49257 insertions(+)
 create mode 100644 zh-cn/1_instruction_tuning/README.md
 create mode 100644 zh-cn/1_instruction_tuning/chat_templates.md
 create mode 100644 zh-cn/1_instruction_tuning/notebooks/.env.example
 create mode 100644 zh-cn/1_instruction_tuning/notebooks/chat_templates_example.ipynb
 create mode 100644 zh-cn/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb
 create mode 100644 zh-cn/1_instruction_tuning/supervised_fine_tuning.md
 create mode 100644 zh-cn/2_preference_alignment/README.md
 create mode 100644 zh-cn/2_preference_alignment/dpo.md
 create mode 100644 zh-cn/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
 create mode 100644 zh-cn/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
 create mode 100644 zh-cn/2_preference_alignment/orpo.md
 create mode 100644 zh-cn/3_parameter_efficient_finetuning/README.md
 create mode 100644 zh-cn/3_parameter_efficient_finetuning/images/lora_adapter.png
 create mode 100644 zh-cn/3_parameter_efficient_finetuning/lora_adapters.md
 create mode 100644 zh-cn/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
 create mode 100644 zh-cn/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
 create mode 100644 zh-cn/3_parameter_efficient_finetuning/prompt_tuning.md
 create mode 100644 zh-cn/4_evaluation/README.md
 create mode 100644 zh-cn/4_evaluation/automatic_benchmarks.md
 create mode 100644 zh-cn/4_evaluation/custom_evaluation.md
 create mode 100644 zh-cn/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
 create mode 100644 zh-cn/4_evaluation/project/README.md
 create mode 100644 zh-cn/4_evaluation/project/annotate_dataset.py
 create mode 100644 zh-cn/4_evaluation/project/create_dataset.py
 create mode 100644 zh-cn/4_evaluation/project/evaluation_task.py
 create mode 100644 zh-cn/4_evaluation/project/generate_dataset.py
 create mode 100644 zh-cn/4_evaluation/project/images/domain_eval_argilla_view.png
 create mode 100644 zh-cn/4_evaluation/project/images/domain_eval_dataset_viewer.png
 create mode 100644 zh-cn/5_vision_language_models/README.md
 create mode 100644 zh-cn/5_vision_language_models/images/VLM_Architecture.png
 create mode 100644 zh-cn/5_vision_language_models/images/VLM_Process.png
 create mode 100644 zh-cn/5_vision_language_models/images/VLM_Usage.png
 create mode 100644 zh-cn/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
 create mode 100644 zh-cn/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
 create mode 100644 zh-cn/5_vision_language_models/vlm_finetuning.md
 create mode 100644 zh-cn/5_vision_language_models/vlm_usage.md
 create mode 100644 zh-cn/6_synthetic_datasets/README.md
 create mode 100644 zh-cn/6_synthetic_datasets/images/pipeline.png
 create mode 100644 zh-cn/6_synthetic_datasets/images/taxonomy-synthetic-data.png
 create mode 100644 zh-cn/6_synthetic_datasets/instruction_datasets.md
 create mode 100644 zh-cn/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb
 create mode 100644 zh-cn/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb
 create mode 100644 zh-cn/6_synthetic_datasets/preference_datasets.md
 create mode 100644 zh-cn/7_inference/README.md
 create mode 100644 zh-cn/7_inference/inference_pipeline.md
 create mode 100644 zh-cn/7_inference/text_generation_inference.md
 create mode 100644 zh-cn/8_agents/README.md
 create mode 100644 zh-cn/8_agents/code_agents.md
 create mode 100644 zh-cn/8_agents/custom_functions.md
 create mode 100644 zh-cn/8_agents/notebooks/agents.ipynb
 create mode 100644 zh-cn/8_agents/retrieval_agents.md

diff --git a/zh-cn/1_instruction_tuning/README.md b/zh-cn/1_instruction_tuning/README.md
new file mode 100644
index 00000000..43f9639e
--- /dev/null
+++ b/zh-cn/1_instruction_tuning/README.md
@@ -0,0 +1,30 @@
+# 指令微调
+
+本章内容主要聚焦大语言模型指令微调部分。指令微调会把预训练模型在特定领域的数据集上进一步训练，来适配特定的任务。这一过程能有效地提升模型在目标任务上的性能。
+
+具体而言，本章将会重点探索两个主题：聊天模板和有监督微调。
+
+## 1️⃣ 聊天模板
+
+聊天模板的主要作用是把用户和 AI 模型之间的交互信息结构化，确保模型能够稳定输出且根据上下文作出回答。一个聊天模板包含系统提示词和人机两个角色发送的消息。本章的[聊天模板教程](./chat_templates.md)将会详细讲述这一内容。
+
+## 2️⃣ 有监督微调
+
+有监督微调（SFT）是你将预训练模型往特定任务迁移时的一个重要过程。SFT 通过在特定领域的有标注数据集上进一步训练，来提升模型在这里应用领域的性能。本章[有监督微调教程](./supervised_fine_tuning.md)将会详细讲解相关内容，包括其中的重要步骤和最佳实践。
+
+## 实践练习
+
+| 标题 | 简介 | 习题 | 链接 | Colab |
+|-------|-------------|----------|------|-------|
+| 聊天模板 | 学习使用 SmolLM2 的聊天模板，并将数据集转换为 ChatML 聊天模板的格式 | 🐢 将 `HuggingFaceTB/smoltalk` 数据集转换为 ChatnML 格式 <br> 🐕 将 `openai/gsm8k` 转换为 ChatML 格式 | [Notebook](./notebooks/chat_templates_example_cn.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/1_instruction_tuning/notebooks/chat_templates_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+| 有监督微调 | 学习用 SFTTrainer 去微调 SmolLM2 模型 | 🐢 使用 `HuggingFaceTB/smoltalk` 数据集训练模型<br>🐕 使用 `bigcode/the-stack-smol` 数据集训练模型<br>🦁 针对一个实际场景选取数据集去训练 | [Notebook](./notebooks/sft_finetuning_example_cn.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+
+## 参考资料
+
+- [transformers 文档中关于聊天模板的介绍](https://huggingface.co/docs/transformers/main/en/chat_templating)
+- [使用 TRL 进行有监督微调的示例脚本](https://github.com/huggingface/trl/blob/main/examples/scripts/sft.py)
+- [TRL 官方文档关于 `SFTTrainer` 的介绍](https://huggingface.co/docs/trl/main/en/sft_trainer)
+- [DPO 算法论文](https://arxiv.org/abs/2305.18290)
+- [TRL 官方文档中关于有监督微调的教程](https://huggingface.co/docs/trl/main/en/tutorials/supervised_finetuning)
+- [博客：用 ChatML 和 TRL 微调 Google Gemma 模型](https://www.philschmid.de/fine-tune-google-gemma)
+- [教程：微调大语言模型使其输出 JSON 格式的内容](https://huggingface.co/learn/cookbook/en/fine_tuning_llm_to_generate_persian_product_catalogs_in_json_format)
diff --git a/zh-cn/1_instruction_tuning/chat_templates.md b/zh-cn/1_instruction_tuning/chat_templates.md
new file mode 100644
index 00000000..ed6fe292
--- /dev/null
+++ b/zh-cn/1_instruction_tuning/chat_templates.md
@@ -0,0 +1,120 @@
+# 聊天模板
+
+如果想将模型与用户的交互信息结构化，那么一个聊天模板（chat template）就是必需的。它为对话提供了一个固定格式，让模型能够知道上下文信息以及每条消息是由谁发出的，只有这样模型才能生成恰当的回答。
+
+## 基础模型 vs 指令模型
+
+基础模型（base model）指的是在未经整理的文本数据上训练、用于预测下一个 token 的模型，而指令模型（instruct model）则是通过微调来跟随指令、参与对话的模型。举例来说，`SmolLM2-135M` 就是基础模型，而 `SmolLM2-135M-Instruct` 则是前者经指令调优得到的指令模型。
+
+为了让基础模型成为指令模型，我们需要对我们的输入提示词进行规范化，用一种固定的格式输入给模型，以便于模型理解。这就用到**聊天模板**了。举例来说，ChatML 就是一个这样的模板，它将对话过程完全结构化，清晰地指明了每段信息是由哪个角色（系统、用户、助手）说出的。
+
+需要注意，一个基础模型可以往不同的聊天模板上微调。所以当我们使用训练好的指令模型时，我们也需要注意不要用错聊天模板。
+
+## 聊天模板简介
+
+聊天模板定义了当用户和语言模型对话时，对话信息应该遵循什么样的格式。这其中包含来自三个角色的信息：系统级的指令、用户发出的信息、AI 助手的回答。这使得每次人机交互的信息格式都是一致的，确保模型针对不同问题都能恰当回答。下面就是一个聊天模板示例：
+
+```sh
+<|im_start|>user
+Hi there!<|im_end|>
+<|im_start|>assistant
+Nice to meet you!<|im_end|>
+<|im_start|>user
+Can I ask a question?<|im_end|>
+<|im_start|>assistant
+```
+
+实际上，如果你使用 `transformers` 库的 tokenizer，它将会为我们将对话信息转化为聊天模板形式。你可以在[这里](https://huggingface.co/docs/transformers/en/chat_templating#how-do-i-use-chat-templates)查看相关文档。我们仅需将对话信息结构化，后面的事情交给 tokenizer 即可。比如，你可以把聊天信息写成这样：
+
+```python
+messages = [
+    {"role": "system", "content": "You are a helpful assistant focused on technical topics."},
+    {"role": "user", "content": "Can you explain what a chat template is?"},
+    {"role": "assistant", "content": "A chat template structures conversations between users and AI models..."}
+]
+```
+
+接下来，我们将分解聊天信息的组成：系统信息和对话部分。
+
+### 系统消息
+
+系统消息从基本层面定义了模型应有的行为。它会影响接下来所有交互。看下列示例就能明白：
+
+```python
+system_message = {
+    "role": "system",
+    "content": "You are a professional customer service agent. Always be polite, clear, and helpful."
+}
+```
+
+### 对话部分
+
+聊天模板也需要保留对话历史记录，将之前发生的人机对话保存下来，作为后续对话的参考。只有这样，我们才能实现多轮交互式对话。
+
+```python
+conversation = [
+    {"role": "user", "content": "I need help with my order"},
+    {"role": "assistant", "content": "I'd be happy to help. Could you provide your order number?"},
+    {"role": "user", "content": "It's ORDER-123"},
+]
+```
+
+## 使用 Transformers 构建聊天模板 
+
+使用 `transformers` 构建聊天模板的示例如下： 
+
+```python
+from transformers import AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-135M-Instruct")
+
+messages = [
+    {"role": "system", "content": "You are a helpful coding assistant."},
+    {"role": "user", "content": "Write a Python function to sort a list"},
+]
+
+# Apply the chat template
+formatted_chat = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+```
+
+上述代码运行完后，`formatted_chat` 应该是这样：
+```
+'<|im_start|>system\nYou are a helpful coding assistant.<|im_end|>\n<|im_start|>user\nWrite a Python function to sort a list<|im_end|>\n<|im_start|>assistant\n'
+```
+
+### 自定义聊天模板格式
+
+你也可以自定义聊天模板格式，比如为不同角色的信息添加特殊的 token 来作为标识：
+
+```python
+template = """
+<|system|>{system_message}
+<|user|>{user_message}
+<|assistant|>{assistant_message}
+""".lstrip()
+```
+
+### 对多轮对话的支持
+
+聊天模板可以处理复杂多轮对话，同时保留上下文信息：
+
+```python
+messages = [
+    {"role": "system", "content": "You are a math tutor."},
+    {"role": "user", "content": "What is calculus?"},
+    {"role": "assistant", "content": "Calculus is a branch of mathematics..."},
+    {"role": "user", "content": "Can you give me an example?"},
+]
+```
+
+⏭️ [下一节课程：有监督微调](./supervised_fine_tuning.md)
+
+## 其它学习资源
+
+- [Hugging Face 聊天模板使用指南](https://huggingface.co/docs/transformers/main/en/chat_templating)
+- [Transformers 官方文档](https://huggingface.co/docs/transformers)
+- [包含各种聊天模板的代码仓库](https://github.com/chujiezheng/chat_templates) 
diff --git a/zh-cn/1_instruction_tuning/notebooks/.env.example b/zh-cn/1_instruction_tuning/notebooks/.env.example
new file mode 100644
index 00000000..f0e16a46
--- /dev/null
+++ b/zh-cn/1_instruction_tuning/notebooks/.env.example
@@ -0,0 +1,2 @@
+# Duplicate this file to .env and fill in secret values
+HF_TOKEN=
diff --git a/zh-cn/1_instruction_tuning/notebooks/chat_templates_example.ipynb b/zh-cn/1_instruction_tuning/notebooks/chat_templates_example.ipynb
new file mode 100644
index 00000000..a6d98a62
--- /dev/null
+++ b/zh-cn/1_instruction_tuning/notebooks/chat_templates_example.ipynb
@@ -0,0 +1,5735 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vZAvFVIAtFlq"
+   },
+   "source": [
+    "# 借助 SmolLM2 探索聊天模板\n",
+    "\n",
+    "这个教程演示如何在 `SmolLM2` 下使用聊天模板。借助聊天模板，人机之间的交互信息可以被结构化，这样我们才能确保模型的回答和上下文信息保持一致性。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "K-lZu8JvtwUN",
+    "outputId": "c3871418-15bc-4265-ae8d-6d6036036d0e"
+   },
+   "outputs": [],
+   "source": [
+    "# Install the requirements in Google Colab\n",
+    "# !pip install transformers datasets trl huggingface_hub\n",
+    "\n",
+    "# Authenticate to Hugging Face\n",
+    "from huggingface_hub import login\n",
+    "\n",
+    "login()\n",
+    "\n",
+    "# for convenience you can create an environment variable containing your hub token as HF_TOKEN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "tnHzBR7vtFlr"
+   },
+   "outputs": [],
+   "source": [
+    "# Import necessary libraries\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "from trl import setup_chat_format\n",
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XTVOqbuetFlr"
+   },
+   "source": [
+    "## SmolLM2 的聊天模板\n",
+    "\n",
+    "我们先看看怎样使用 `SmolLM2` 的聊天模板。这里我们先定义一个简单的对话，然后用聊天模板转换它。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 397,
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+      "17023310de9b4c3ebd8cc03758d59ef9",
+      "f3e23f781bce4429954d76bfea97aff4",
+      "530fc4c2bf1244628af7dea3e4b35cdf",
+      "96c2aae9198441569362135ad4bcbc98",
+      "76d306c21214412ab44e542d82e547aa",
+      "b9e41ef9e9c54fa7b71bc333604af74e"
+     ]
+    },
+    "id": "Nrxh0oX6tFls",
+    "outputId": "953e1527-8168-4346-9338-6e188ca31a1a"
+   },
+   "outputs": [],
+   "source": [
+    "# Dynamically set the device\n",
+    "device = (\n",
+    "    \"cuda\"\n",
+    "    if torch.cuda.is_available()\n",
+    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
+    ")\n",
+    "\n",
+    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    pretrained_model_name_or_path=model_name\n",
+    ").to(device)\n",
+    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
+    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "zkJwILrbtFls"
+   },
+   "outputs": [],
+   "source": [
+    "# Define messages for SmolLM2\n",
+    "messages = [\n",
+    "    {\"role\": \"user\", \"content\": \"Hello, how are you?\"},\n",
+    "    {\n",
+    "        \"role\": \"assistant\",\n",
+    "        \"content\": \"I'm doing well, thank you! How can I assist you today?\",\n",
+    "    },\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Ve4dgtjstFls"
+   },
+   "source": [
+    "# 先不进行 tokenize，应用一下聊天模板\n",
+    "\n",
+    "先把 `tokenize` 这个入参设为 `False`。可以看到，下面例子增加了额外的角色信息。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "pbAg-5x-tFls",
+    "outputId": "5f9482db-1fcf-4c13-ccaa-ef3f6eff7f76"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Conversation with template: <|im_start|>user\n",
+      "Hello, how are you?<|im_end|>\n",
+      "<|im_start|>assistant\n",
+      "I'm doing well, thank you! How can I assist you today?<|im_end|>\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "input_text = tokenizer.apply_chat_template(messages, tokenize=False)\n",
+    "\n",
+    "print(\"Conversation with template:\", input_text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "sfvdglOqtFls"
+   },
+   "source": [
+    "# 对已经 tokenize 过的对话进行解码\n",
+    "\n",
+    "如果已经把 `tokenize` 设为 `True`，可以通过解码的方式查看对话内容。可以看到，这里多了一个 assistant 信息。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mXUVdPeytFls",
+    "outputId": "80870e53-7bc1-426e-ac33-ba6748e030fc"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Conversation decoded: <|im_start|>user\n",
+      "Hello, how are you?<|im_end|>\n",
+      "<|im_start|>assistant\n",
+      "I'm doing well, thank you! How can I assist you today?<|im_end|>\n",
+      "<|im_start|>assistant\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "input_text = tokenizer.apply_chat_template(\n",
+    "    messages, tokenize=True, add_generation_prompt=True\n",
+    ")\n",
+    "\n",
+    "print(\"Conversation decoded:\", tokenizer.decode(token_ids=input_text))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "UcZQpspEtFlt"
+   },
+   "source": [
+    "# tokenize 过后的对话是什么形式\n",
+    "\n",
+    "对对话应用聊天模板并进行 tokenize 之后，我们发现，整个对话，包括特殊 token，都被转化为了一系列整数。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "jc2PLxAMtFlt",
+    "outputId": "d2098780-b3f4-41ec-a1f3-b6da2b593c62"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Conversation tokenized: [1, 4093, 198, 19556, 28, 638, 359, 346, 47, 2, 198, 1, 520, 9531, 198, 57, 5248, 2567, 876, 28, 9984, 346, 17, 1073, 416, 339, 4237, 346, 1834, 47, 2, 198, 1, 520, 9531, 198]\n"
+     ]
+    }
+   ],
+   "source": [
+    "input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)\n",
+    "\n",
+    "print(\"Conversation tokenized:\", input_text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "m3eNp9a0tFlt"
+   },
+   "source": [
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>练习：为 SFT 准备数据集</h2>\n",
+    "    <p>从 HuggingFace 上找一个数据集，然后为后续 SFT 转化它</p>\n",
+    "    <p><b>难度等级</b></p>\n",
+    "    <p>🐢 将 `HuggingFaceTB/smoltalk` 数据集转换为 ChatML 形式</p>\n",
+    "    <p>🐕 将 `openai/gsm8k` 数据集转换为 ChatML 形式</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 381
+    },
+    "id": "qbkXV2_ItFlt",
+    "outputId": "06deadc3-2c63-4660-d2bd-05096ef07c9f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe\n",
+       "  src=\"https://huggingface.co/datasets/HuggingFaceTB/smoltalk/embed/viewer/all/train?row=0\"\n",
+       "  frameborder=\"0\"\n",
+       "  width=\"100%\"\n",
+       "  height=\"360px\"\n",
+       "></iframe>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.core.display import display, HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"<iframe\n",
+    "  src=\"https://huggingface.co/datasets/HuggingFaceTB/smoltalk/embed/viewer/all/train?row=0\"\n",
+    "  frameborder=\"0\"\n",
+    "  width=\"100%\"\n",
+    "  height=\"360px\"\n",
+    "></iframe>\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 241,
+     "referenced_widgets": [
+      "c2d74a42fb574b8892d0a288fd92f0a6",
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+      "951f60cddcb84dfdbbdf2058369f0541",
+      "646484cf7a36444daebe1dfe4a0e4150",
+      "e2f0c39ce1c046e8acb150dfbfaf5aa8",
+      "7eb12d70d2b542a7b651c7680f590279",
+      "ea1f9cb22abf4e7d9f6e76fc86c03387",
+      "00c9f5ca71b84df4b26acee72c97fefb",
+      "505f96bc0c7843bcb1498ba1c1ba5f06",
+      "635cc2881a1e4b8788bb26c356740e04",
+      "a6ee323c13904525a99c6f092ba96e18",
+      "67fffe7d6f8c4963972b408529e05532",
+      "0055b6b628934affaf88bc58a1572bb6",
+      "aafbbb9fc5164fa3a88193bfd33d2f79",
+      "606e39d53ed64967a60337418c71c595",
+      "26b15fa18b1b4963a1ba76a76675e7ee",
+      "db09ab1f79db4f3a8de77f0348eca0f7",
+      "de04f344a8d4428e8ba1836a563d8aa1",
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+      "ff60308921f9432683acbcd6d29fb78f",
+      "3bc8f6339f4e4a3b961d810255c5573e",
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+      "488feef55878426bbf1c753c6d58735b",
+      "560ba45d70ca431dadeb327d234c330a",
+      "04d0a6f74af346f7bc696951949063c8",
+      "2a18ce941b0f4cef8307988ef898b47f",
+      "194e3fda3635466b998f96e3dc22746a",
+      "e2ab3cb38b5a41f68d18ed5f0e6ae22c",
+      "f0b271bcac6c43a9aaddac54259bb514",
+      "0dc93d50a283472f9ca64fd0a4c6ff15",
+      "dd1a50d4497144388a1809b78bb38f58",
+      "6b72a856e5bd4812a5e0dd0c3bfb8455",
+      "4e21a567d1f6461985727823b37166e1",
+      "ec1efb7598fd496bb170673ae1b8a1df",
+      "84f393468aa74baa903243d238b2d387",
+      "a54ce365be104d27aaa15cf8c63b5ebe",
+      "1791220377d141ac9b307246177d0712",
+      "fa330d4f0fb241aebd065f6ef4a6892c",
+      "cfa1cc6eed8a4f7791a7959308456b6b",
+      "b50c9c4433854cf7a6b2593e946b7faa",
+      "7557cd24ba9b4aa3955866d59db94519",
+      "cc608dfb880c49d4bc5acf2d691b8ec6",
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+      "76bbe8c2beba4c0594085d32a68d2ee7",
+      "c9836c952b07472880649b82e2347e8d",
+      "383db57f997140d482b82b123080837a",
+      "182abc7ec4d944d9bb2ec1281c98b4c8",
+      "6934c6d1cbac44dbb08f3fffe3056edb",
+      "05fa0f6eb78b4c56b219b0e57521bd2e",
+      "012aa94e3cf24e32833c6bbca23c52f7",
+      "76c1a1cdc9054bbe90d0d3b662cf0ed1",
+      "e453f1672772400a851735ba64f42c8b",
+      "d1358f6b16644cb3a2328ca639a4a77a",
+      "c19f60d4028045399c62004027eaafd9",
+      "8055588a1fa940239c801ef66f3ecf3b",
+      "7468a9bc8bda44e5b44574c64fdc6803",
+      "a13a8f8b702e44ed88c7d358a0a8b4b4",
+      "13367fbb763747fa8de94cde40ffae32",
+      "b1fcf477db664ccdade4096fb79de327",
+      "9d1c06ac6b774d82adca58773f389161",
+      "31910159cf30463b8246ec47ffd8ab5b",
+      "72220420f9d340eabec13a01caebc92c",
+      "55b14c03a41c495aacf8ac2d0f96ba0b"
+     ]
+    },
+    "id": "4p3atw4_tFlu",
+    "outputId": "62ee9812-3819-4a9c-9e24-5687368ffcd8"
+   },
+   "outputs": [],
+   "source": [
+    "from datasets import load_dataset\n",
+    "\n",
+    "ds = load_dataset(\"HuggingFaceTB/smoltalk\", \"everyday-conversations\")\n",
+    "\n",
+    "\n",
+    "def process_dataset(sample):\n",
+    "    # TODO: 🐢 Convert the sample into a chat format\n",
+    "    # use the tokenizer's method to apply the chat template\n",
+    "    return sample\n",
+    "\n",
+    "\n",
+    "ds = ds.map(process_dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 381
+    },
+    "id": "81fQeazltFlu",
+    "outputId": "36cf7148-9881-4f13-d0ce-76c82c4ab219"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe\n",
+       "  src=\"https://huggingface.co/datasets/openai/gsm8k/embed/viewer/main/train\"\n",
+       "  frameborder=\"0\"\n",
+       "  width=\"100%\"\n",
+       "  height=\"360px\"\n",
+       "></iframe>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"<iframe\n",
+    "  src=\"https://huggingface.co/datasets/openai/gsm8k/embed/viewer/main/train\"\n",
+    "  frameborder=\"0\"\n",
+    "  width=\"100%\"\n",
+    "  height=\"360px\"\n",
+    "></iframe>\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true,
+    "id": "bWUSv7NMtFlu",
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ds = load_dataset(\"openai/gsm8k\", \"main\")\n",
+    "\n",
+    "\n",
+    "def process_dataset(sample):\n",
+    "    # TODO: 🐕 Convert the sample into a chat format\n",
+    "\n",
+    "    # 1. create a message format with the role and content\n",
+    "\n",
+    "    # 2. apply the chat template to the samples using the tokenizer's method\n",
+    "\n",
+    "    return sample\n",
+    "\n",
+    "\n",
+    "ds = ds.map(process_dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qlXCuRKotFlu"
+   },
+   "source": [
+    "## 总结\n",
+    "\n",
+    "本文讲解了在 `SmolLM2` 模型中如何使用聊天模板。通过聊天模板来结构化人机交互信息，我们可以确保 AI 模型可以给出和上下文保持一致性的回答。\n",
+    "\n",
+    "在练习中，你将尝试将数据集转为 ChatML 格式。但实际情况中，TRL 会为你做这些事情。不过，充分理解背后的处理逻辑还是很有必要的。"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "0055b6b628934affaf88bc58a1572bb6": {
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diff --git a/zh-cn/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb b/zh-cn/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb
new file mode 100644
index 00000000..7d259da3
--- /dev/null
+++ b/zh-cn/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb
@@ -0,0 +1,273 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 用 SFTTrainer 实现有监督微调\n",
+    "\n",
+    "本教程将展示如何使用 `trl` 代码库中的 `SFTTrainer` 来微调 `HuggingFaceTB/SmolLM2-135M`。文中代码模块将会展示具体步骤。你可以根据难度选取在不同数据集上微调。\n",
+    "\n",
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>练习：用 SFTTrainer 微调 SmolLM2</h2>\n",
+    "    <p>从 HuggingFace 上选取一个数据集然后微调模型 </p> \n",
+    "    <p><b>难度等级</b></p>\n",
+    "    <p>🐢 使用 `HuggingFaceTB/smoltalk` 数据集</p>\n",
+    "    <p>🐕 尝试 `bigcode/the-stack-smol` 数据集，在 `data/python` 这个子集上微调一个代码生成模型</p>\n",
+    "    <p>🦁 选取一个你感兴趣的领域，使用真实世界数据集，为你的特定应用微调模型</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install the requirements in Google Colab\n",
+    "# !pip install transformers datasets trl huggingface_hub\n",
+    "\n",
+    "# Authenticate to Hugging Face\n",
+    "\n",
+    "from huggingface_hub import login\n",
+    "login()\n",
+    "\n",
+    "# for convenience you can create an environment variable containing your hub token as HF_TOKEN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import necessary libraries\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "from datasets import load_dataset\n",
+    "from trl import SFTConfig, SFTTrainer, setup_chat_format\n",
+    "import torch\n",
+    "\n",
+    "device = (\n",
+    "    \"cuda\"\n",
+    "    if torch.cuda.is_available()\n",
+    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
+    ")\n",
+    "\n",
+    "# Load the model and tokenizer\n",
+    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    pretrained_model_name_or_path=model_name\n",
+    ").to(device)\n",
+    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
+    "\n",
+    "# Set up the chat format\n",
+    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n",
+    "\n",
+    "# Set our name for the finetune to be saved &/ uploaded to\n",
+    "finetune_name = \"SmolLM2-FT-MyDataset\"\n",
+    "finetune_tags = [\"smol-course\", \"module_1\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 用基准模型生成\n",
+    "\n",
+    "我们首先看看不使用聊天模板的基础模型是什么效果。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's test the base model before training\n",
+    "prompt = \"Write a haiku about programming\"\n",
+    "\n",
+    "# Format with template\n",
+    "messages = [{\"role\": \"user\", \"content\": prompt}]\n",
+    "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n",
+    "\n",
+    "# Generate response\n",
+    "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
+    "outputs = model.generate(**inputs, max_new_tokens=100)\n",
+    "print(\"Before training:\")\n",
+    "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 准备数据集\n",
+    "\n",
+    "接下来我们载入一个简单数据集，并转换格式。这个数据集需要结构化为“输入-输出”这样成对的形式，其中输入是一个提示语，输出就是我们期待的模型回答。\n",
+    "\n",
+    "**TRL 会自动根据聊天模板转换输入信息。**这些信息需要写成一个字典（dict）的列表，其中 key 是 `role` 和 `content`，分别代表哪个角色以及对应的话语。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load a sample dataset\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "# TODO: 稍后你可以使用你自己的数据集，替换以下的路径、名字等参数配置\n",
+    "ds = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: 🦁 如果你的数据集不是 TRL 能转换成聊天模板的格式，你还需要额外处理一下。参考[聊天模板](../chat_templates_cn.md)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 配置 SFTTrainer\n",
+    "\n",
+    "我们还需要配置 `SFTTrainer` 的各项参数，来控制训练过程。这包括训练步数、batch size、学习率，以及评估策略。你需要根据你自己的情况和计算资源来调节。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure the SFTTrainer\n",
+    "sft_config = SFTConfig(\n",
+    "    output_dir=\"./sft_output\",\n",
+    "    max_steps=1000,  # Adjust based on dataset size and desired training duration\n",
+    "    per_device_train_batch_size=4,  # Set according to your GPU memory capacity\n",
+    "    learning_rate=5e-5,  # Common starting point for fine-tuning\n",
+    "    logging_steps=10,  # Frequency of logging training metrics\n",
+    "    save_steps=100,  # Frequency of saving model checkpoints\n",
+    "    evaluation_strategy=\"steps\",  # Evaluate the model at regular intervals\n",
+    "    eval_steps=50,  # Frequency of evaluation\n",
+    "    use_mps_device=(\n",
+    "        True if device == \"mps\" else False\n",
+    "    ),  # Use MPS for mixed precision training\n",
+    "    hub_model_id=finetune_name,  # Set a unique name for your model\n",
+    ")\n",
+    "\n",
+    "# Initialize the SFTTrainer\n",
+    "trainer = SFTTrainer(\n",
+    "    model=model,\n",
+    "    args=sft_config,\n",
+    "    train_dataset=ds[\"train\"],\n",
+    "    tokenizer=tokenizer,\n",
+    "    eval_dataset=ds[\"test\"],\n",
+    ")\n",
+    "\n",
+    "# TODO: 🦁 🐕 根据你自己的数据集调节 SFTTrainer 的参数。假如你用的是 `bigcode/the-stack-smol` 数据集，那你就需要选择 `content` 这一列。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 训练模型\n",
+    "\n",
+    " 当上述配置完成后，我们就可以训练了。这个过程将遍历数据集、计算损失函数、更新模型参数等。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "trainer.train()\n",
+    "\n",
+    "# Save the model\n",
+    "trainer.save_model(f\"./{finetune_name}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer.push_to_hub(tags=finetune_tags)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>额外练习：用微调过的模型来生成</h2>\n",
+    "    <p>🐕 使用微调过的模型来生成回答，就像之前用基准模型一样。对比一下效果</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Test the fine-tuned model on the same prompt\n",
+    "\n",
+    "# Let's test the base model before training\n",
+    "prompt = \"Write a haiku about programming\"\n",
+    "\n",
+    "# Format with template\n",
+    "messages = [{\"role\": \"user\", \"content\": prompt}]\n",
+    "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n",
+    "\n",
+    "# Generate response\n",
+    "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
+    "\n",
+    "# TODO: 用微调过的模型生成相同的回答，看看有没有变化改进"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💐 完成了！\n",
+    "\n",
+    "本文提供了使用 `SFTTrainer` 微调 `HuggingFaceTB/SmolLM2-135M` 的指南。依照这些步骤，你可以将模型适配到各种特殊任务场景上。如果你觉得这个课程还不错，你还可以尝试这些：\n",
+    "\n",
+    "- 使用这个 notebook 完成难点分级更高的任务\n",
+    "- 在 GitHub 上评审别人的 pull request\n",
+    "- 通过提出 Issue 或 PR 进一步改进我们的课程资料"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/zh-cn/1_instruction_tuning/supervised_fine_tuning.md b/zh-cn/1_instruction_tuning/supervised_fine_tuning.md
new file mode 100644
index 00000000..e7250c38
--- /dev/null
+++ b/zh-cn/1_instruction_tuning/supervised_fine_tuning.md
@@ -0,0 +1,41 @@
+# 有监督微调
+
+有监督微调（Supervised Fine-Tuning），简称 SFT，是将预训练模型往特定领域或任务迁移的一个重要过程。虽然预训练模型总体性能也很不错，但如果需要应对特定场景，就必须针对场景进行定制化。SFT 通过在人工筛选的高质量数据上进一步训练，将预训练模型往特定任务上迁移。
+
+## SFT 原理简介
+
+SFT 核心思想是让预训练模型学习标注过的、特定领域的数据。这个过程会向模型提供很多我们想要的输入-输出，让模型去学习我们想要的特定回答模式。
+
+由于利用了预训练模型学到的基础知识，通过 SFT 将模型进一步适配到特定领域的训练还是非常高效的。
+
+## 什么时候使用 SFT
+
+SFT 通常在你当前模型的能力和你的特殊需求还存在差距时使用，尤其是当你想要精细控制模型输出，或是想让模型在特定领域发挥作用时。
+
+举例来说，如果你在开发客户服务相关的应用，那你可能需要让模型严格遵循公司规定、用标准化的流程去处理技术性的咨询。或者，在医疗或法律领域，准确表达特定领域的专业术语也很重要。在这些情况下，SFT 就能将模型性能对齐到领域内专家的水平，使得模型的回答符合专业标准。
+
+## 微调的过程
+
+SFT 主要就是在特定任务的数据集上训练模型。
+
+首先，你需要准备一个能反映任务类型的数据集，这个数据集需要涵盖尽可能广泛的领域内问答场景。数据的质量非常重要，每条数据都应该向模型展示你希望得到的回答类型。接下来就是实际微调阶段了，你可以使用 HuggingFace 的 `transformers` 和 `trl` 在数据集上训练模型。
+
+整个过程中，不断地对模型进行评测也是很重要的。你可以找一个验证集，然后实时监控模型性能，来确保模型学到了特定领域内的回答，同时又不丧失它原有的通用能力。在[第四章](../4_evaluation)，我们将讲解如何评测模型。
+
+## 通过 SFT 对齐特定偏好
+
+此外，SFT 也广泛用于将语言模型对齐到特定的人类偏好上。如 RLHF 和 DPO 等技术依靠 SFT 来形成对任务的基本理解，然后再进一步对模型的响应进行优化，以达到预期效果。预训练模型，尽管在通用的语言能力上效果很强，但它的回答可能不符合人类的偏好。SFT 通过引入专业领域的数据进行指导，可以改善模型生成与人类期望的匹配程度。
+
+## 使用 Transformer Reinforcement Learning 进行 SFT
+
+TRL（Transformer Reinforcement Learning）是常用于 SFT 的一个重要的软件包。如果你使用强化学习训练 transformer 系列的语言模型，TRL 将为你提供有用工具。
+
+TRL 基于 HuggingFace 的 `transformers` 构建，允许用户直接载入预训练模型，并支持大部分 decoder 或 encoder-decoder 的架构。这个库涵盖了针对语言模型的主流强化学习算法，包括 SFT、奖励模型、PPO、DPO 等。在本课程中，我们将大量使用 TRL。
+
+# 接下来的学习
+
+请通过下列 notebook 上手 SFT：
+
+⏭️ [聊天模板教程](./notebooks/chat_templates_example_cn.ipynb)
+
+⏭️ [有监督微调教程](./notebooks/supervised_fine_tuning_tutorial_cn.ipynb)
\ No newline at end of file
diff --git a/zh-cn/2_preference_alignment/README.md b/zh-cn/2_preference_alignment/README.md
new file mode 100644
index 00000000..48232423
--- /dev/null
+++ b/zh-cn/2_preference_alignment/README.md
@@ -0,0 +1,40 @@
+# 偏好对齐
+
+本章将学习如何将语言模型的输出和人类偏好对齐。虽然有监督微调（SFT）已经将模型适配到特定的任务领域了，但偏好对齐（Prefenrece Alignment）将会迫使模型的输出更加匹配人类的期望、符合人类的价值观。
+
+## 概览
+
+典型的偏好对齐方法一般都包含这几个步骤：
+1. 使用 SFT 将模型适配到特定的领域
+2. 使用偏好对齐（如 RLHF 或 DPO 算法）进一步提升模型回答的质量
+
+其它偏好对齐算法还包括 ORPO，这个算法将指令微调和偏好对齐结合进了一个单一步骤中。本章我们将重点学习 DPO 和 ORPO 算法。
+
+如果你还想进一步学习相关对齐算法，你可以阅读[这篇博客](https://argilla.io/blog/mantisnlp-rlhf-part-8)。
+
+### 1️⃣ 直接偏好优化（DPO）
+
+直接偏好优化（Direct Preference Optimization），简称 DPO，直接使用偏好数据对模型进行参数更新。这简化了偏好对齐的过程。这个方法无需额外设置激励模型、无需复杂强化学习步骤，比基于人类反馈的强化学习（RLHF）更高效更稳定。本章中对应的学习资料在[这里](./dpo.md)。
+
+### 2️⃣ 基于优势比的偏好优化（ORPO）
+
+基于优势比的偏好优化（Odds Ratio Preference Optimization），简称 ORPO，是一种将指令微调和偏好对齐结合在一起的方法。通过在 token 层面定义一个优势比（Odds），并在优势比上使用负对数似然损失函数，ORPO 改变了传统的语言建模的目标函数。ORPO 训练步骤简单、无需 DPO 中的参考模型，计算效率也更高。该方法在多项评测基准上展现了优秀的效果，尤其在 AlpacaEval 超越了传统方法。本章中对应的学习资料在[这里](./orpo.md)。
+
+## 实践练习
+
+| 标题 | 简介 | 习题 | 链接 | Colab |
+|-------|-------------|----------|------|-------|
+| DPO 训练 | 学习用 DPO 训练模型 | 🐢 在 Anthropic HH-RLHF 数据集上训练模型<br>🐕 使用你自己的偏好数据集<br>🦁 对不同的偏好数据集和不同大小的模型进行实验 | [Notebook](./notebooks/dpo_finetuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+| ORPO 训练 | 学习用 ORPO 训练模型 | 🐢 用指令数据和偏好数据训练模型<br>🐕 对不同的损失权重进行实验<br>🦁 对比 ORPO 和 DPO 的结果 | [Notebook](./notebooks/orpo_finetuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+
+
+## 参考资料
+
+- [TRL 官方文档](https://huggingface.co/docs/trl/index) - TRL 是一个基于 Transformers 的强化学习库，这里实现了包括 DPO 在内的各种对齐算法。
+- [DPO 论文](https://arxiv.org/abs/2305.18290) - 该论文针对当时已有的 RLHF 方法，提出了新的对齐方法，可以直接使用偏好数据优化模型参数。
+- [ORPO 论文](https://arxiv.org/abs/2403.07691) - ORPO 算法将指令微调和偏好优化和并进一个训练步骤中。
+- [RLHF 相关博客](https://argilla.io/blog/mantisnlp-rlhf-part-8/) - 这篇博客介绍了包括 RLHF、DPO 在内的对齐算法，同时也介绍了具体实现方法。
+- [DPO 相关博客](https://huggingface.co/blog/dpo-trl) - 介绍了使用 TRL 实现 DPO 的具体步骤，包括示例代码和其它最佳实践经验。
+- [DPO 示例训练脚本](https://github.com/huggingface/trl/blob/main/examples/scripts/dpo.py) - 完整的基于 TRL 的 DPO 训练代码。
+- [ORPO 示例训练脚本](https://github.com/huggingface/trl/blob/main/examples/scripts/orpo.py) - 完整的基于 TRL 的 ORPO 训练代码。
+- [Hugging Face 关于对齐训练的资料](https://github.com/huggingface/alignment-handbook) - 包括 SFT、DPO、RLHF 的语言模型对齐算法介绍，包括理论指导和实践代码。
\ No newline at end of file
diff --git a/zh-cn/2_preference_alignment/dpo.md b/zh-cn/2_preference_alignment/dpo.md
new file mode 100644
index 00000000..ea3b23d8
--- /dev/null
+++ b/zh-cn/2_preference_alignment/dpo.md
@@ -0,0 +1,72 @@
+# 直接偏好优化（DPO）
+
+直接偏好优化（Direct Preference Optimization），简称 DPO，是一种非常简洁的使用人类偏好数据对齐模型的算法。DPO 直接使用偏好数据优化模型，无需 RLHF 的激励模型和强化学习步骤。
+
+## 理解 DPO
+
+DPO 将偏好对齐任务转化为了一个在偏好数据上训练的分类任务。传统的 RLHF 需要训练一个额外的激励模型，并使用强化学习方法（如 PPO）去对齐模型输出。DPO简化了这个步骤，通过定义一个损失函数，直接在“倾向的输出”和“不倾向的输出”上进行训练。
+
+这个方法在实践中十分高效，Llama 模型的训练就使用了 DPO。同时，没有了激励模型了强化学习，DPO 训练也更简单、更稳定。
+
+## DPO 工作原理
+
+在 DPO 之前，我们需要使用 SFT 微调模型，用指令跟随的数据集先把模型适配到特定任务领域中，让模型在这个领域具备基本能力。
+
+接下来才是偏好学习。模型将在“倾向的输出”和“不倾向的输出”这样成对的数据上训练，学习哪种类型的回答更符合人类的喜好。
+
+DPO 的关键原理在于它直接使用偏好数据进行优化。不同于 RLHF，DPO 使用了二分类的交叉墒损失函数，这里的损失直接在“倾向的输出”和“不倾向的输出”这样的成对数据上计算。这使得模型训练更稳定、更高效，同时效果甚至还比 RLHF 好。
+
+## DPO 数据集
+
+构造 DPO 专用数据集，一般需要对回答进行“倾向”和“不倾向”的标注。使用人工标注或自动化方法都可以实现这一步骤。下表就是一个示例数据集：
+
+| Prompt | Chosen | Rejected |
+|--------|--------|----------|
+| ...    | ...    | ...      |
+| ...    | ...    | ...      |
+| ...    | ...    | ...      |
+
+`Prompt` 这一栏提供问题， `Chosen` 和 `Rejected` 分别代表针对这个问题我们倾向的回答和不倾向的回答。`chosen` 和 `rejected` 也可以是一个列表形式，包含多个不同的回答。
+
+你可以在 Hugging Face 的[这个地方](https://huggingface.co/collections/argilla/preference-datasets-for-dpo-656f0ce6a00ad2dc33069478)找到很多 DPO 数据集。
+
+## 用 TRL 实现 DPO
+
+使用 TRL 实现 DPO 非常简单直接，仅需配置 `DPOConfig` 和 `DPOTrainer` 即可。这两个类遵循 `transformers` 的 API 风格。
+下面就是一个简单的例子：
+
+```python
+from trl import DPOConfig, DPOTrainer
+
+# Define arguments
+training_args = DPOConfig(
+    ...
+)
+
+# Initialize trainer
+trainer = DPOTrainer(
+    model,
+    train_dataset=dataset,
+    tokenizer=tokenizer,
+    ...
+)
+
+# Train model
+trainer.train()
+```
+
+我们还将在 [DPO 教程](./notebooks/dpo_finetuning_example.ipynb) 中详细讲解 `DPOConfig` 和 `DPOTrainer` 的配置。
+
+## 最佳实践
+
+数据质量对 DPO 的成败至关重要。偏好数据集必须足够多样，涵盖不同的想要的回答。在数据标注过程中，需要制定清晰明确的标注指导。通过提高数据集质量一般都可以提升模型性能，可能的做法包括对大规模数据集进行过滤，仅保留高质量数据，或仅保留和应用领域相关的数据。
+
+训练过程中，仔细监视损失的收敛情况、及时验证性能也很重要。及时调节 $\beta$ 参数，在偏好学习和通用能力间找到平衡。有规律地在多样的问题上做验证测试，确保模型不过你和。这些也都很重要。
+
+同时，也要对比一下原模型和优化后模型针对同一问题的回答，看看 模型是否学到了偏好。在包括极端情况下的问题集上测试，确保模型健壮性。
+
+## 接下来的学习
+
+⏩ 在 [DPO 教程](./notebooks/dpo_finetuning_example.ipynb)中，你可以直接上手实践。该教程将会带你实践 DPO 的整个过程，从数据准备指导模型训练和验证。
+
+⏭️ 之后，你还可以学习 [ORPO](./orpo.md)，了解更多偏好优化算法。
\ No newline at end of file
diff --git a/zh-cn/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb b/zh-cn/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
new file mode 100644
index 00000000..e3d9869b
--- /dev/null
+++ b/zh-cn/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
@@ -0,0 +1,5496 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 使用直接偏好优化（DPO）进行偏好对齐\n",
+    "\n",
+    "本教程将会带你使用直接偏好优化（DPO）的方法去微调一个语言模型。这里我们使用的是 SmolLM2-135M-Instruct 模型，它已经经过了 SFT 这一训练步骤，所以可以接着进行 DPO。当然你也可以使用你在[第一章](../../1_instruction_tuning/notebooks/sft_finetuning_example_cn.ipynb)训练好的模型。\n",
+    "\n",
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "     <h2 style='margin: 0;color:blue'>练习：用 DPOTrainer 对 SmolLM2 做对齐训练</h2>\n",
+    "     <p>从 Hugging Face hub 上找一个数据集并在上面对齐模型。</p> \n",
+    "     <p><b>难度等级</b></p>\n",
+    "     <p>🐢 使用 `trl-lib/ultrafeedback_binarized` 数据集</p>\n",
+    "     <p>🐕 使用 `argilla/ultrafeedback-binarized-preferences` 数据集</p>\n",
+    "     <p>🦁 选用你感兴趣的真实世界数据集，或者使用你在<a href=\"../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb\">上一章</a> SFT 训练好的模型</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install the requirements in Google Colab\n",
+    "# !pip install transformers datasets trl huggingface_hub\n",
+    "\n",
+    "# Authenticate to Hugging Face\n",
+    "\n",
+    "from huggingface_hub import login\n",
+    "\n",
+    "login()\n",
+    "\n",
+    "# for convenience you can create an environment variable containing your hub token as HF_TOKEN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 准备好需要的 Python 库\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "_zIBL8IssExG",
+    "outputId": "90d70bd2-30e7-49a4-d5bf-20d74deb6cec"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import os\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "from datasets import load_dataset\n",
+    "from trl import DPOTrainer, DPOConfig\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "d8CvUgROUDw-"
+   },
+   "source": [
+    "## 配置数据集及其格式"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "MCD77GZ60DOT"
+   },
+   "outputs": [],
+   "source": [
+    "# Load dataset\n",
+    "\n",
+    "# TODO: 🦁🐕 你也可以使用你感兴趣的其他数据集\n",
+    "dataset = load_dataset(path=\"trl-lib/ultrafeedback_binarized\", split=\"train\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: 🐕 如果你的数据集不是对话列表形式，你可以使用 `process_dataset` 函数进行转化"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 选择一个经 SFT 训练过模型\n",
+    "\n",
+    "我们这里使用 SmolLM2-135M-Instruct 模型，因为它已经经过 SFT 了，所以可以接着进行 DPO 训练。你也可以使用你[第一章](../../1_instruction_tuning/notebooks/sft_finetuning_example_cn.ipynb) 训练好的模型。\n",
+    "\n",
+    "\n",
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; width:80%; color:black'>\n",
+    "     <p>🦁 如果你想使用<a href=\"../../1_instruction_tuning/notebooks/sft_finetuning_example_cn.ipynb\">第一章</a>你自己训练的模型，你需要修改下面代码的 model_name 到指定的路径</p>\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_name = \"HuggingFaceTB/SmolLM2-135M-Instruct\"\n",
+    "\n",
+    "device = (\n",
+    "    \"cuda\"\n",
+    "    if torch.cuda.is_available()\n",
+    "    else \"mps\"\n",
+    "    if torch.backends.mps.is_available()\n",
+    "    else \"cpu\"\n",
+    ")\n",
+    "\n",
+    "# Model to fine-tune\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    pretrained_model_name_or_path=model_name,\n",
+    "    torch_dtype=torch.float32,\n",
+    ").to(device)\n",
+    "model.config.use_cache = False\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+    "tokenizer.pad_token = tokenizer.eos_token\n",
+    "\n",
+    "# Set our name for the finetune to be saved &/ uploaded to\n",
+    "finetune_name = \"SmolLM2-FT-DPO\"\n",
+    "finetune_tags = [\"smol-course\", \"module_1\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DeT5eUK_UJgK"
+   },
+   "source": [
+    "## 开始 DPO 训练"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000,
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+   "outputs": [],
+   "source": [
+    "# Training arguments\n",
+    "training_args = DPOConfig(\n",
+    "    # 单 GPU 上 batch size 的大小\n",
+    "    per_device_train_batch_size=4,\n",
+    "    # 每经过多少步才进行一次反向传播、参数更新\n",
+    "    # 每个 GPU 实际起作用的 batch size 等于 per_device_train_batch_size 乘 gradient_accumulation_steps\n",
+    "    gradient_accumulation_steps=4,\n",
+    "    # 这个操作在前向传播时不保存中间结果激活值，可以节省内存\n",
+    "    # 在反向传播时，模型重新计算前向过程，获取计算梯度所需要的激活值\n",
+    "    gradient_checkpointing=True,\n",
+    "    # 训练时的基本学习率\n",
+    "    learning_rate=5e-5,\n",
+    "    # 训练过程中学习率变化策略：\"cosine\" 策略根据余弦函数的曲线形状逐步递减学习率\n",
+    "    lr_scheduler_type=\"cosine\",\n",
+    "    # 总的训练部数\n",
+    "    max_steps=200,\n",
+    "    # 在模型训练过程中不去保存 ckeckpoint\n",
+    "    save_strategy=\"no\",\n",
+    "    # 打印 log 的频率\n",
+    "    logging_steps=1,\n",
+    "    # 保存结果的目录\n",
+    "    output_dir=\"smol_dpo_output\",\n",
+    "    # 学习率 warmup 的步数\n",
+    "    warmup_steps=100,\n",
+    "    # 是否使用 bfloat16 精度来加速训练\n",
+    "    bf16=True,\n",
+    "    # 这里我们不使用 wandb 或 tensorboard \n",
+    "    report_to=None,\n",
+    "    # 这里我们保留数据集里没用到的数据列\n",
+    "    remove_unused_columns=False,\n",
+    "    # 如果你不使用 Mac 电脑，这里就是 False\n",
+    "    use_mps_device=device == \"mps\",\n",
+    "    # 如果你要将训练好的模型上传到 HuggingFace Hub，这里就是你的模型名字 \n",
+    "    hub_model_id=finetune_name,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer = DPOTrainer(\n",
+    "    # The model to be trained\n",
+    "    model=model,\n",
+    "    # Training configuration from above\n",
+    "    args=training_args,\n",
+    "    # Dataset containing preferred/rejected response pairs\n",
+    "    train_dataset=dataset,\n",
+    "    # Tokenizer for processing inputs\n",
+    "    processing_class=tokenizer,\n",
+    "    # DPO 特有的 temperature 参数，控制参考模型的强度\n",
+    "    # 较小的值可以让模型更保守、更缓慢地学习偏好\n",
+    "    beta=0.1,\n",
+    "    # 提示语的最大长度\n",
+    "    max_prompt_length=1024,\n",
+    "    # 提示语 + 回答 的序列最大长度\n",
+    "    max_length=1536,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "trainer.train()\n",
+    "\n",
+    "# Save the model\n",
+    "trainer.save_model(f\"./{finetune_name}\")\n",
+    "\n",
+    "# Save to the huggingface hub if login (HF_TOKEN is set)\n",
+    "if os.getenv(\"HF_TOKEN\"):\n",
+    "    trainer.push_to_hub(tags=finetune_tags)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💐 完成\n",
+    "\n",
+    "本教程带你一步步地学习了用 `DPOTrainer` 训练 `HuggingFaceTB/SmolLM2-135M`的过程。利用这些步骤，你可以高效快速地进行特定任务领域的偏好优化训练。感兴趣的话，你还可以：\n",
+    "\n",
+    "- 尝试本教程难度等级更高的任务\n",
+    "- 在 GitHub 上评审别人的 Pull Request\n",
+    "- 通过 Issue 或 PR 提出课程改进的意见"
+   ]
+  }
+ ],
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diff --git a/zh-cn/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb b/zh-cn/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
new file mode 100644
index 00000000..39c1bd9a
--- /dev/null
+++ b/zh-cn/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
@@ -0,0 +1,8643 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 使用基于优势比的偏好优化（ORPO）进行偏好对齐\n",
+    "\n",
+    "本教程将会带你使用基于优势比的偏好优化（ORPO）算法去微调一个语言模型。这里我们使用 SmolLM2-135M 模型，因为它还**没有**经过 SFT 的训练（所以这个模型是不能直接进行 DPO 训练的）。这里你就不能使用你在[第一章](../../1_instruction_tuning/notebooks/sft_finetuning_example_cn.ipynb)训练好的模型了。\n",
+    "\n",
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "     <h2 style='margin: 0;color:blue'>练习：借助 ORPOTrainer 对齐 SmolLM2 模型</h2>\n",
+    "     <p>从 Hugging Face hub 上找一个数据集并在上面对齐模型。</p> \n",
+    "     <p><b>难度等级</b></p>\n",
+    "     <p>🐢 使用 `trl-lib/ultrafeedback_binarized` 数据集</p>\n",
+    "     <p>🐕 尝试使用 `argilla/ultrafeedback-binarized-preferences` 数据集</p>\n",
+    "     <p>🦁 尝试使用 `mlabonne/orpo-dpo-mix-40k` 数据集的一个子集进行训练</p>\n",
+    "</div>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 准备好需要的 Python 库\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "EwNxh0pCQhfI",
+    "outputId": "e4c288e1-2fa3-4717-d9dd-ae3ec0b91e3b"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/ben/code/smol-course/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import os\n",
+    "from datasets import load_dataset\n",
+    "from transformers import (\n",
+    "    AutoModelForCausalLM,\n",
+    "    AutoTokenizer,\n",
+    ")\n",
+    "from trl import ORPOConfig, ORPOTrainer, setup_chat_format\n",
+    "\n",
+    "# Authenticate to Hugging Face\n",
+    "from huggingface_hub import login\n",
+    "\n",
+    "login()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 配置数据集及其格式"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Generating train split: 100%|██████████| 62135/62135 [00:00<00:00, 450465.27 examples/s]\n",
+      "Generating test split: 100%|██████████| 1000/1000 [00:00<00:00, 382866.64 examples/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load dataset\n",
+    "\n",
+    "# TODO: 🦁🐕 你也可以使用你感兴趣的其他数据集\n",
+    "dataset = load_dataset(path=\"trl-lib/ultrafeedback_binarized\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: 🐕 如果你的数据集不是对话列表形式，你可以使用 `process_dataset` 函数进行转化"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 定义好你要使用的模型"
+   ]
+  },
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+   "source": [
+    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
+    "\n",
+    "device = (\n",
+    "    \"cuda\"\n",
+    "    if torch.cuda.is_available()\n",
+    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
+    ")\n",
+    "\n",
+    "# Model to fine-tune\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    pretrained_model_name_or_path=model_name,\n",
+    "    torch_dtype=torch.float32,\n",
+    ").to(device)\n",
+    "model.config.use_cache = False\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+    "model, tokenizer = setup_chat_format(model, tokenizer)\n",
+    "\n",
+    "# Set our name for the finetune to be saved &/ uploaded to\n",
+    "finetune_name = \"SmolLM2-FT-ORPO\"\n",
+    "finetune_tags = [\"smol-course\", \"module_1\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 开始 ORPO 训练"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
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+   "outputs": [],
+   "source": [
+    "orpo_args = ORPOConfig(\n",
+    "    # 使用较小的学习率可以预防“灾难性遗忘”\n",
+    "    learning_rate=8e-6,\n",
+    "    # 学习率衰减策略\n",
+    "    lr_scheduler_type=\"linear\",\n",
+    "    # 提示词 + 回答 的最大长度（最大 token 数量）\n",
+    "    max_length=1024,\n",
+    "    # 输入的提示词的最大长度\n",
+    "    max_prompt_length=512,\n",
+    "    # 优势比损失函数的权重（论文中的 λ 超参数）\n",
+    "    beta=0.1,\n",
+    "    # 单个 GPU 的 batch size\n",
+    "    per_device_train_batch_size=2,\n",
+    "    per_device_eval_batch_size=2,\n",
+    "    # 每多少步前向过程才更新一次参数，调节这里可以稳定训练\n",
+    "    gradient_accumulation_steps=4,\n",
+    "    # 使用 paged_adamw_8bit 优化器可以为 CUDA 节省内存，如果是 CPU 或 MPS 设备，则退回到使用 adamw_torch 优化器\n",
+    "    optim=\"paged_adamw_8bit\" if device == \"cuda\" else \"adamw_torch\",\n",
+    "    # 训练的 epoch 数量\n",
+    "    num_train_epochs=1,\n",
+    "    # 训练过程中验证的策略\n",
+    "    evaluation_strategy=\"steps\",\n",
+    "    # 每完成 20% 训练就验证一次\n",
+    "    eval_steps=0.2,\n",
+    "    # log 打印频率\n",
+    "    logging_steps=1,\n",
+    "    # 学习率 warmup 的步数\n",
+    "    warmup_steps=10,\n",
+    "    # 这里我们不使用 wandb 或 tensorboard，不使用外部打印 log 的功能\n",
+    "    report_to=None,\n",
+    "    # 保存训练结果的目录\n",
+    "    output_dir=\"./results/\",\n",
+    "    # 如果是苹果电脑，将会使用 MPS 硬件加速\n",
+    "    use_mps_device=device == \"mps\",\n",
+    "    hub_model_id=finetune_name,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer = ORPOTrainer(\n",
+    "    model=model,\n",
+    "    args=orpo_args,\n",
+    "    train_dataset=dataset[\"train\"],\n",
+    "    eval_dataset=dataset[\"test\"],\n",
+    "    processing_class=tokenizer,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer.train()  # Train the model\n",
+    "\n",
+    "# Save the model\n",
+    "trainer.save_model(f\"./{finetune_name}\")\n",
+    "\n",
+    "# Save to the huggingface hub if login (HF_TOKEN is set)\n",
+    "if os.getenv(\"HF_TOKEN\"):\n",
+    "    trainer.push_to_hub(tags=finetune_tags)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💐 完成\n",
+    "\n",
+    "本教程带你一步步地学习了用 `ORPOTrainer` 去训练 `HuggingFaceTB/SmolLM2-135M` 模型。利用这些步骤，你可以高效快速地进行特定任务领域的偏好优化训练。感兴趣的话，你还可以：\n",
+    "\n",
+    "- 尝试本教程难度等级更高的任务\n",
+    "- 在 GitHub 上评审别人的 Pull Request\n",
+    "- 通过 Issue 或 PR 提出课程改进的意见"
+   ]
+  }
+ ],
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diff --git a/zh-cn/2_preference_alignment/orpo.md b/zh-cn/2_preference_alignment/orpo.md
new file mode 100644
index 00000000..a8808871
--- /dev/null
+++ b/zh-cn/2_preference_alignment/orpo.md
@@ -0,0 +1,83 @@
+# 基于优势比的偏好优化（ORPO）
+
+基于优势比的偏好优化（Odds Ratio Preference Optimization）或 ORPO，是一种更新颖的偏好对齐方法，它把微调和偏好对齐结合，组成一个统一的过程。这个算法相比于 RLHF 和 DPO 有着更高的效率和更好的性能。
+
+## 理解 ORPO
+
+诸如 DPO 的对齐方法一般包含两个步骤：使用 SFT 先让模型适配如这个领域或回答格式，然后进行偏好对齐训练。虽然 SFT 已经将模型对齐到了特定任务领域，但模型不可避免可能会产生我们不期望的回答，所以我们还需要进行下一步的偏好对齐。ORPO 则合并了这两个步骤。下图取自 ORPO 论文，对比了 RLHF、DPO 和 ORPO 的差异：
+
+![Alignment Techniques Comparison](https://argilla.io/images/blog/mantisnlp-rlhf/part-8-alignments.png)
+*三种对齐算法的对比*
+
+## ORPO 工作原理
+
+ORPO 训练使用的数据集和 DPO 相似：针对一个输入的问题包含两个可能输出：一个是“倾向的输出”，另一个是“不倾向的输出”。不同的是，ORPO 直接将偏好对齐加入到 SFT 中。这一整体性方法使得它无需 DPO 中的参考模型，同时也更高效、节省内存。
+
+ORPO 的损失函数包含两个部分：
+
+1. **SFT Loss**：这里使用标准的负对数似然函数，和 DPO 中的类似，用于扩大想要的 token 的生成概率。这个损失函数也有助于模型保持通用的语言能力。
+2. **Odds Ratio Loss**：这是新提出的损失函数。由于上述 SFT Loss 不能惩罚不想要的输出，所以这个函数在激励倾向的输出的同时，也惩罚不倾向的输出。具体来说，这里定义了计算优势比（Odds Ratio）的公式，通过抬高倾向输出和不倾向输出两者的优势比比值，在奖励倾向输出的同时，也压低不倾向输出的生成概率。
+
+在两个损失函数共同作用下，模型不仅被适配进了相应的任务领域，也压低了不倾向的回答的生成概率。其中，优势比这个机制提供了一个很直观的方法，模拟了倾向回答和不倾向回答之间的差异程度。你也可以阅读 [ORPO 的论文](https://arxiv.org/abs/2403.07691)，进一步了解其中的数学理论。如果你对具体的实现感兴趣，你可以阅读 [TRL 中关于这部分的实现](https://github.com/huggingface/trl/blob/b02189aaa538f3a95f6abb0ab46c0a971bfde57e/trl/trainer/orpo_trainer.py#L660)。
+
+## 训练结果
+
+ORPO 在很多测试基准上都取得了不错的效果。以下是它在 MT-Bench 测试基准上的结果（根据任务类别划分）：
+
+![MT-Bench Results](https://argilla.io/images/blog/mantisnlp-rlhf/part-8-mtbench.png)
+*Mistral-ORPO 模型在 MT-Bench 不同任务领域的结果*
+
+在 AlpacaEval 2.0 上，ORPO 展现了超越其它对齐算法的效果：
+
+![AlpacaEval Results](https://argilla.io/images/blog/mantisnlp-rlhf/part-8-winrate.png)
+*不同对齐算法在 AlpacaEval 2.0 的得分*
+
+相较于 SFT 加 DPO 的做法，ORPO 通过去除参考模型、减半前向推理的策略，大大降低了计算资源的要求。同时，训练过程也更稳定，需要调节的超参数也更少。在性能上，ORPO 对人类偏好的适配做得也更好。
+
+## 代码实现
+
+成功训练 ORPO 也极度依赖高质量数据集。所以标注训练数据时，我们也需要清晰明确的标注标准，确保对话场景的多样性，同时倾向和不倾向的回答需要分布均匀。
+
+### 用 TRL 实现 ORPO
+
+以下代码提供了用 TRL 实现 ORPO 的基本示例：
+
+```python
+from trl import ORPOConfig, ORPOTrainer
+
+# Configure ORPO training
+orpo_config = ORPOConfig(
+    learning_rate=1e-5,
+    per_device_train_batch_size=4,
+    gradient_accumulation_steps=4,
+    max_steps=1000,
+    orpo_alpha=1.0,  # Controls strength of preference optimization
+    orpo_beta=0.1,   # Temperature parameter for odds ratio
+)
+
+# Initialize trainer
+trainer = ORPOTrainer(
+    model=model,
+    args=orpo_config,
+    train_dataset=dataset,
+    tokenizer=tokenizer,
+)
+
+# Start training
+trainer.train()
+```
+
+关键参数：
+- `orpo_alpha`：用来控制偏好优化部分的权重
+- `orpo_beta`：计算优势比（Odds Ratio）时的 Temperature 参数
+- `learning_rate`：这里需要用较小的学习率，用来防治灾难性遗忘（catastrophic forgetting）
+- `gradient_accumulation_steps`：调节这个也能稳定训练
+
+## 接下来的学习
+
+⏩ 学习 [ORPO 教程](./notebooks/orpo_finetuning_example.ipynb) 来实践 ORPO 算法。
+
+## 学习资源
+- [ORPO 论文](https://arxiv.org/abs/2403.07691)
+- [TRL 官方文档](https://huggingface.co/docs/trl/index)
+- [Argilla RLHF Guide](https://argilla.io/blog/mantisnlp-rlhf-part-8/) 
\ No newline at end of file
diff --git a/zh-cn/3_parameter_efficient_finetuning/README.md b/zh-cn/3_parameter_efficient_finetuning/README.md
new file mode 100644
index 00000000..15d13900
--- /dev/null
+++ b/zh-cn/3_parameter_efficient_finetuning/README.md
@@ -0,0 +1,39 @@
+# 高效的参数微调（PEFT）
+
+随着语言模型越来越大，使用传统的模型微调方法去微调 LLM 已经变得越来越难了。举例来说，微调一个 1.7B 参数量的模型需要把所有参数都放进 GPU 显存、保存模型优化和状态信息，甚至还需要保存模型备份，这就需要很大的 GPU 显存使用了。同时，微调所有参数还有很大的“灾难性遗忘”风险，可能损失模型原有的能力。针对此问题，高效的参数微调（Parameter-efficient fine-tuning 或 PEFT）被提出。它在微调模型时，保留大部分参数不变，只微调一小部分参数，大大节省了计算资源的需求。
+
+传统微调需要更新所有模型参数，对大模型很不现实。而 PEFT 相关方法则发现，仅更新一小部分参数，就足以对模型进行适配，达到微调所期待的效果。这部分需要更新的参数甚至还不到总参数量的 1%。这一重大改进使得以下操作成为可能：
+
+- 在 GPU 显存受限的消费级显卡上微调 LLM
+- 高效地为不同任务领域保存不同的微调结果
+- 在数据量不足的微调场景下也可保持很好的泛化性
+- 微调训练耗时更少
+  
+## PEFT 相关算法
+
+在本章教程中，我们主要讲解两种比较常用的 PEFT 算法：
+
+### 1️⃣ LoRA (Low-Rank Adaptation)
+
+LoRA 可以说是最常用的 PEFT 算法了，它为高效模型微调提供了一个非常优雅的解决方案。LoRA 在需要更新的参数（一般是 attention layers 的参数）上插入可以训练的参数矩阵，训练时仅训练这部分参数。当模型训练好后，我们会利用这部分参数对原有模型进行重参数化（re-parameterization）。这样可以不改变 LLM 的整体结构和参数数量。通过这种方法，需要更新的参数量能至少减少 90%，同时性能也不差于全量参数微调。我们将在 [LoRA 低秩分解](./lora_adapters_cn.md)部分进一步讲解。
+
+### 2️⃣ Prompt Tuning
+
+Prompt Tuning 则更加轻量化。它通过在输入部分加入**可训练的 token** 来微调，而不是改变模型的参数。Prompt Tuning 没有 LoRA 那么常用，但对于适配模型到新的任务领域来说，是个非常有用的技术。我们将在 [Prompt Tuning](./prompt_tuning_cn.md) 部分进一步讲解。
+
+## 实践练习
+
+| 标题 | 简介 | 习题 | 链接 | Colab |
+|-------|-------------|----------|------|-------|
+| LoRA 微调 | 学习使用 LoRA adapters 微调模型 | 🐢 用 LoRA 训练一个模型 <br>🐕 试验不同低秩值的效果 <br>🦁 与全量参数微调的效果进行对比 | [Notebook](./notebooks/finetune_sft_peft.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+| 载入 LoRA Adapter | 学习如何加载并使用 LoRA adapters | 🐢 加载训练好的 adapter<br>🐕 将 adapter 融入原有模型中 <br>🦁 实现不同 adapter 的切换 | [Notebook](./notebooks/load_lora_adapter.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+<!-- | Prompt Tuning | Learn how to implement prompt tuning | 🐢 Train soft prompts<br>🐕 Compare different initialization strategies<br>🦁 Evaluate on multiple tasks | [Notebook](./notebooks/prompt_tuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/prompt_tuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | -->
+
+## 参考资料
+- [PEFT 代码库官方文档](https://huggingface.co/docs/peft)
+- [LoRA 论文](https://arxiv.org/abs/2106.09685)
+- [QLoRA 论文](https://arxiv.org/abs/2305.14314)
+- [Prompt Tuning 论文](https://arxiv.org/abs/2104.08691)
+- [Hugging Face PEFT 代码库相关博客](https://huggingface.co/blog/peft)
+- [文章：How to Fine-Tune LLMs in 2024 with Hugging Face](https://www.philschmid.de/fine-tune-llms-in-2024-with-trl) 
+- [TRL 代码库官方文档](https://huggingface.co/docs/trl/index)
diff --git a/zh-cn/3_parameter_efficient_finetuning/images/lora_adapter.png b/zh-cn/3_parameter_efficient_finetuning/images/lora_adapter.png
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diff --git a/zh-cn/3_parameter_efficient_finetuning/lora_adapters.md b/zh-cn/3_parameter_efficient_finetuning/lora_adapters.md
new file mode 100644
index 00000000..147cd872
--- /dev/null
+++ b/zh-cn/3_parameter_efficient_finetuning/lora_adapters.md
@@ -0,0 +1,126 @@
+# LoRA（低秩分解）
+
+LoRA 是最常用的 PEFT 算法。它针对 attention 层的权重加入参数量较少、低秩分解过的参数矩阵，用模型原有参数和低秩分解参数计算出的激活值之和代表微调过后的激活值。这样当我们只更新低秩分解过的参数矩阵时，我们需要训练的参数量能减少大约 90%。
+
+## 理解 LoRA
+
+LoRA 全称是 Low-Rank Adaptation，或叫做“低秩分解”。它的基本做法是，在微调时，冻结所有预训练模型的参数，同时为需要微调的模型层注入额外的可训练的参数矩阵（通常称之为 Adapter）。通过对需要微调的层的参数矩阵进行低秩分解，可以得到两个参数量较小的新参数矩阵；而这一层的前向计算激活值则可以用“原有参数矩阵计算出的激活值”加上“低秩分解出的两个矩阵计算出的激活值”而得到。训练时，我们只需要训练两个低秩分解的矩阵即可，这样极大减少了所需微调的参数量，同时也能保持原有模型性能。例如，如果我们用 LoRA 微调 GPT-3 175B 模型，相比于全量参数的微调，LoRA 需要参与训练的参数量可减少至万分之一、GPU 现存需求可减少至三分之一。感兴趣的读者可以阅读 [LoRA 的论文](https://arxiv.org/pdf/2106.09685)。
+
+一般而言，LoRA 都是对 transformer 层的参数进行低秩分解的，尤其是在与注意力机制相关的参数上。在推理过程中，这些 adapter 的参数可以被直接融合进模型中，得到与原模型结构完全一致的新模型，无需增加新的层。得益于此，LoRA 尤其适合在低计算资源情况下，将大模型适配进入特定的任务领域。
+
+## 如何载入 LoRA 的 Adapters
+
+如果你使用 `peft` 库，你可以用 `load_adapter()` 载入 LoRA Adapters。这对你尝试不同的 adapter 非常管用，因为它还不会将参数融合进原模型。你可以使用 `set_adapter()` 指定哪个 LoRA Adapter 在生效。如果想返回原模型，你可以使用 `unload()` 卸载所有 LoRA 参数。这种设定使得在不同任务领域间切换模型变得非常容易。
+
+```python
+from transformers import AutoModelForCausalLM
+from peft import PeftModel
+
+base_model = AutoModelForCausalLM.from_pretrained("<base_model_name>")
+peft_model_id = "<peft_adapter_id>"
+model = PeftModel.from_pretrained(base_model, peft_model_id)
+```
+
+![lora_load_adapter](./images/lora_adapter.png)
+
+## 将 LoRA 参数融入原模型
+
+如果在 LoRA 微调结束后，你想直接获取一套新的模型参数，而不是每次使用的时候都需要加载 LoRA 的 Adapter，你可以直接将 LoRA 的参数融入原模型中。
+
+融合的时候，我们首先需要注意内存的管理以及参数的精度。因为我们要同时载入原模型和 LoRA 参数，需要注意 GPU 或 CPU 的内存是否够用。在 `transformers` 中使用 `device_map="auto"` 可以替我们自动进行内存管理。同时，要注意原模型、LoRA 参数的精度需保持一致。融合后，检查模型输出是否和未融合是一致也很重要。
+
+## 代码实现
+
+在 `notebooks/` 目录下，有 PEFT 相关方法的实践教程以及练习题。我们首先会在 `load_lora_adapter_example.ipynb` 学习加载 LoRA Adapter 相关的内容，然后在 `lora_finetuning.ipynb` 中，我们将学习如果用 LoRA 进行 SFT。
+
+一个比较好的 LoRA 训练流程应该是，首先从较低的秩开始，一般是 4 到 8，同时观察训练损失值。使用验证集及时查看避免过拟合。不同任务可能有较大差异，所有还是要以实际实验现象为准。
+
+## OLoRA
+
+[OLoRA](https://arxiv.org/abs/2406.01775) 使用 QR 分解来初始化 LoRA 的 Adapter。该算法对原有的参数矩阵 W 分解为 Q 和 R 两个矩阵，其中 Q 矩阵包含 W 矩阵的 r 个正交向量，使得优化能够在一个较好的子空间进行。这样可以很大地提升收敛速度，同时也达到了非常好的效果。
+
+##  TRL 与 PEFT 结合使用
+
+PEFT 也可以和 TRL 库一起使用，这对 RLHF（Reinforcement Learning from Human Feedback）尤其实用。
+
+```python
+from peft import LoraConfig
+from transformers import AutoModelForCausalLM
+
+# Load model with PEFT config
+lora_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM"
+)
+
+# Load model on specific device
+model = AutoModelForCausalLM.from_pretrained(
+    "your-model-name",
+    load_in_8bit=True,  # Optional: use 8-bit precision
+    device_map="auto",
+    peft_config=lora_config
+)
+```
+
+在上述代码中，我们用 `device_map="auto"` 自动分配模型到正确的计算设备上。关于具体计算设备，你也可以手动修改：`device_map={"": device_index}`；当然你也可以扩大训练规模，如实用多 GPU 训练等。
+
+## 基本的参数融合实现
+
+
+
+训练好 LoRA adapter 后，将权重融合回原模型的方法如下：
+
+
+```python
+import torch
+from transformers import AutoModelForCausalLM
+from peft import PeftModel
+
+# 1. Load the base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    "base_model_name",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# 2. Load the PEFT model with adapter
+peft_model = PeftModel.from_pretrained(
+    base_model,
+    "path/to/adapter",
+    torch_dtype=torch.float16
+)
+
+# 3. Merge adapter weights with base model
+try:
+    merged_model = peft_model.merge_and_unload()
+except RuntimeError as e:
+    print(f"Merging failed: {e}")
+    # Implement fallback strategy or memory optimization
+
+# 4. Save the merged model
+merged_model.save_pretrained("path/to/save/merged_model")
+```
+
+保存的时候，你可能也需要保存 tokenizer 到相应目录。
+
+```python
+# Save both model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("base_model_name")
+merged_model.save_pretrained("path/to/save/merged_model")
+tokenizer.save_pretrained("path/to/save/merged_model")
+```
+
+## 接下来
+
+⏩ 继续学习 [Prompt Tuning](prompt_tuning_cn.md)，了解这种微调方式如何运作。
+
+⏩ 实践 [加载 LoRA Adapters 的教程](./notebooks/load_lora_adapter_cn.ipynb) 练习加载 LoRA adapters。
+
+# 学习资源
+
+- [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/pdf/2106.09685)
+- [PEFT 官方文档](https://huggingface.co/docs/peft)
+- [Hugging Face 有关 PEFT 的博客](https://huggingface.co/blog/zh/peft)
diff --git a/zh-cn/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb b/zh-cn/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
new file mode 100644
index 00000000..4e0e0bd2
--- /dev/null
+++ b/zh-cn/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
@@ -0,0 +1,519 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "z-6LLOPZouLg"
+   },
+   "source": [
+    "# 在 TRL 框架下用 LoRA 微调大语言模型\n",
+    "\n",
+    "这个 notebook 展示如何用 LoRA 高效微调大语言模型。LoRA 是一种高效的参数微调方法，有如下优点：\n",
+    "- 不更新预训练模型权重\n",
+    "- 仅在注意力层添加少量低秩分解矩阵作为训练参数\n",
+    "- 基本能减少 90% 训练参数\n",
+    "- 能保留模型原有的能力\n",
+    "\n",
+    "本文涵盖这些步骤：\n",
+    "1. 配置开发环境、设定 LoRA 相关配置\n",
+    "2. 准备数据集\n",
+    "3. 使用 `trl` 框架下的 `SFTTrainer` 进行 LoRA 微调\n",
+    "4. 测试模型性能、学习加载 adapter\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fXqd9BXgouLi"
+   },
+   "source": [
+    "## 1. 配置开发环境\n",
+    "\n",
+    "我们首先需要安装 PyTorch 和 Hugging Face 相关的库，这包括 `trl`、`transformers`、`datasets`。其中 `trl` 基于 `transformers` 和 `datasets`，用以微调模型、进行 RLHF、对齐 LLM 等。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "tKvGVxImouLi"
+   },
+   "outputs": [],
+   "source": [
+    "# Install the requirements in Google Colab\n",
+    "# !pip install transformers datasets trl huggingface_hub\n",
+    "\n",
+    "# Authenticate to Hugging Face\n",
+    "\n",
+    "from huggingface_hub import login\n",
+    "\n",
+    "login()\n",
+    "\n",
+    "# for convenience you can create an environment variable containing your hub token as HF_TOKEN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XHUzfwpKouLk"
+   },
+   "source": [
+    "## 2. 载入数据集"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "id": "z4p6Bvo7ouLk"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['full_topic', 'messages'],\n",
+       "        num_rows: 2260\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['full_topic', 'messages'],\n",
+       "        num_rows: 119\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load a sample dataset\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "# TODO: 你也可以用自己的数据集\n",
+    "dataset = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")\n",
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9TOhJdtsouLk"
+   },
+   "source": [
+    "## 3. 在 `trl` 框架下用 `SFTTrainer` 实现大语言模型的 LoRA 微调\n",
+    "\n",
+    "在 `trl` 中，[SFTTrainer](https://huggingface.co/docs/trl/sft_trainer) 通过 [PEFT](https://huggingface.co/docs/peft/en/index) 提供了 LoRA Adapter 的集成。这样的设定有以下几个好处：\n",
+    "\n",
+    "1. **高效利用内存**：\n",
+    "   - 仅 Adapter 的参数会保存在 GPU 显存中\n",
+    "   - 原模型参数被冻结，所以可以用低精度载入\n",
+    "   - 这使得在消费级显卡上也可以微调大模型\n",
+    "2. **训练层面**：\n",
+    "   - 原生 PEFT/LoRA 集成，用户开发所需代码量少\n",
+    "   - 支持 QLoRA（量化版 LoRA），可以进一步减少内存使用\n",
+    "\n",
+    "3. **Adapter 管理**：\n",
+    "   - 可以方便地训练过程中保存 Adapter\n",
+    "   - 可以方便地把 Adapter 融合进原模型\n",
+    "\n",
+    "本文将会进行 LoRA 微调，当然你也可以尝试 4-bit 量化来进一步减少内存使用。配置步骤包含以下几步：\n",
+    "1. 定义好 LoRA 的相关参数（主要是 rank、alpha、dropout）\n",
+    "2. 创建一个 SFTTrainer 的实例\n",
+    "3. 训练模型、保存 adapter 的参数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import necessary libraries\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "from datasets import load_dataset\n",
+    "from trl import SFTConfig, SFTTrainer, setup_chat_format\n",
+    "import torch\n",
+    "\n",
+    "device = (\n",
+    "    \"cuda\"\n",
+    "    if torch.cuda.is_available()\n",
+    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
+    ")\n",
+    "\n",
+    "# Load the model and tokenizer\n",
+    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
+    "\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    pretrained_model_name_or_path=model_name\n",
+    ").to(device)\n",
+    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
+    "\n",
+    "# Set up the chat format\n",
+    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n",
+    "\n",
+    "# Set our name for the finetune to be saved &/ uploaded to\n",
+    "finetune_name = \"SmolLM2-FT-MyDataset\"\n",
+    "finetune_tags = [\"smol-course\", \"module_1\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ZbuVArTHouLk"
+   },
+   "source": [
+    "由于 `SFTTrainer`  原生支持 `peft`，使用 LoRA 训练 LLM 就变得非常简单。我们需要配置的只有 `LoraConfig`。\n",
+    "\n",
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>练习：为微调定义好 LoRA 相关参数</h2>\n",
+    "    <p>从 Hugging Face hub 找一个合适的数据然后微调模型 </p> \n",
+    "    <p><b>难度等级</b></p>\n",
+    "    <p>🐢 使用默认的 LoRA 超参数直接微调</p>\n",
+    "    <p>🐕 改变一些超参数，学习通过 weights & biases 平台查看</p>\n",
+    "    <p>🦁 改变一些超参数，训练后查看这些改变是否影响了推理性能</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "blDSs9swouLk"
+   },
+   "outputs": [],
+   "source": [
+    "from peft import LoraConfig\n",
+    "\n",
+    "# TODO: 配置 LoRA 参数\n",
+    "# r: 低秩分解的秩，越小则需要训练的参数量越少\n",
+    "rank_dimension = 6\n",
+    "# lora_alpha: 将训练好的参数加到原模型上时的缩放倍数，越大则微调参数作用越明显\n",
+    "lora_alpha = 8\n",
+    "# lora_dropout: LoRA 相关层的 dropout，可以用来应对过拟合\n",
+    "lora_dropout = 0.05\n",
+    "\n",
+    "peft_config = LoraConfig(\n",
+    "    r=rank_dimension,  # 一般选择 4 到 32\n",
+    "    lora_alpha=lora_alpha,  # 一般是 rank 的 2 倍\n",
+    "    lora_dropout=lora_dropout,  # Dropout probability for LoRA layers\n",
+    "    bias=\"none\",  # Bias type for LoRA. the corresponding biases will be updated during training.\n",
+    "    target_modules=\"all-linear\",  # 模型哪些层会添加 LoRA Adapter\n",
+    "    task_type=\"CAUSAL_LM\",  # Task type for model architecture\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "l5NUDPcaouLl"
+   },
+   "source": [
+    "此外我们还需定义训练的超参数（`TrainingArguments`）。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "NqT28VZlouLl"
+   },
+   "outputs": [],
+   "source": [
+    "# Training configuration\n",
+    "# Hyperparameters based on QLoRA paper recommendations\n",
+    "args = SFTConfig(\n",
+    "    # Output settings\n",
+    "    output_dir=finetune_name,  # Directory to save model checkpoints\n",
+    "    # Training duration\n",
+    "    num_train_epochs=1,  # Number of training epochs\n",
+    "    # Batch size settings\n",
+    "    per_device_train_batch_size=2,  # Batch size per GPU\n",
+    "    gradient_accumulation_steps=2,  # Accumulate gradients for larger effective batch\n",
+    "    # Memory optimization\n",
+    "    gradient_checkpointing=True,  # Trade compute for memory savings\n",
+    "    # Optimizer settings\n",
+    "    optim=\"adamw_torch_fused\",  # Use fused AdamW for efficiency\n",
+    "    learning_rate=2e-4,  # Learning rate (QLoRA paper)\n",
+    "    max_grad_norm=0.3,  # Gradient clipping threshold\n",
+    "    # Learning rate schedule\n",
+    "    warmup_ratio=0.03,  # Portion of steps for warmup\n",
+    "    lr_scheduler_type=\"constant\",  # Keep learning rate constant after warmup\n",
+    "    # Logging and saving\n",
+    "    logging_steps=10,  # Log metrics every N steps\n",
+    "    save_strategy=\"epoch\",  # Save checkpoint every epoch\n",
+    "    # Precision settings\n",
+    "    bf16=True,  # Use bfloat16 precision\n",
+    "    # Integration settings\n",
+    "    push_to_hub=False,  # Don't push to HuggingFace Hub\n",
+    "    report_to=None,  # Disable external logging\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cGhR7uFBouLl"
+   },
+   "source": [
+    "配置完毕，我们可以创建 `SFTTrainer` 来训练模型了。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "M00Har2douLl"
+   },
+   "outputs": [],
+   "source": [
+    "max_seq_length = 1512  # max sequence length for model and packing of the dataset\n",
+    "\n",
+    "# Create SFTTrainer with LoRA configuration\n",
+    "trainer = SFTTrainer(\n",
+    "    model=model,\n",
+    "    args=args,\n",
+    "    train_dataset=dataset[\"train\"],\n",
+    "    peft_config=peft_config,  # LoRA configuration\n",
+    "    max_seq_length=max_seq_length,  # Maximum sequence length\n",
+    "    tokenizer=tokenizer,\n",
+    "    packing=True,  # Enable input packing for efficiency\n",
+    "    dataset_kwargs={\n",
+    "        \"add_special_tokens\": False,  # Special tokens handled by template\n",
+    "        \"append_concat_token\": False,  # No additional separator needed\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zQ_kRN24ouLl"
+   },
+   "source": [
+    "通过启动 `train()` 函数，我们开始训练。本次训练包含 3 个 epoch。由于我们用了 PEFT，我们可以在训练过程中或结束后，只保存 adapter 的参数，无需保存原模型参数。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Tq4nIYqKouLl"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "300e5dfbb4b54750b77324345c7591f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/72 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "TrainOutput(global_step=72, training_loss=1.6402628521124523, metrics={'train_runtime': 195.2398, 'train_samples_per_second': 1.485, 'train_steps_per_second': 0.369, 'total_flos': 282267289092096.0, 'train_loss': 1.6402628521124523, 'epoch': 0.993103448275862})"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# start training, the model will be automatically saved to the hub and the output directory\n",
+    "trainer.train()\n",
+    "\n",
+    "# save model\n",
+    "trainer.save_model()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "y4HHSYYzouLl"
+   },
+   "source": [
+    "我们模型使用了 Flash Attention 加速训练。在当前数据集（15k 的样本量）训练了 3 轮，在一个 `g5.2xlarge` 机器上用了 4 小时 14 分钟 36 秒。该机器报价 `1.21$/h`，所以我们总花费仅 `5.3$`。\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C309KsXjouLl"
+   },
+   "source": [
+    "### 将 LoRA Adapter 融入原模型\n",
+    "\n",
+    "训练 LoRA 时，我们只训练 adapter 里的参数，而不训练原模型。所以保存的参数也只有 adapter 里的参数（可能也就 2MB 到 10MB）。然而在部署阶段，你可能需要把 Adapter 融合进原模型：\n",
+    "1. **简化的部署流程**：仅载入一个模型参数文件即可，无需额外载入 adapter 参数文件\n",
+    "2. **推理速度提升**：adapter 引入的计算已经融合进了模型中\n",
+    "3. **框架的兼容性**：更能和服务框架适配"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from peft import AutoPeftModelForCausalLM\n",
+    "\n",
+    "\n",
+    "# Load PEFT model on CPU\n",
+    "model = AutoPeftModelForCausalLM.from_pretrained(\n",
+    "    pretrained_model_name_or_path=args.output_dir,\n",
+    "    torch_dtype=torch.float16,\n",
+    "    low_cpu_mem_usage=True,\n",
+    ")\n",
+    "\n",
+    "# Merge LoRA and base model and save\n",
+    "merged_model = model.merge_and_unload()\n",
+    "merged_model.save_pretrained(\n",
+    "    args.output_dir, safe_serialization=True, max_shard_size=\"2GB\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-yO6E9quouLl"
+   },
+   "source": [
+    "## 3. 测试模型、进行推理\n",
+    "\n",
+    "训练结束后，我们可能需要测试模型。可以从数据集找一些样本，然后看看模型在这些样本上的性能。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "id": "I5B494OdouLl"
+   },
+   "outputs": [],
+   "source": [
+    "# free the memory again\n",
+    "del model\n",
+    "del trainer\n",
+    "torch.cuda.empty_cache()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "P1UhohVdouLl"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from peft import AutoPeftModelForCausalLM\n",
+    "from transformers import AutoTokenizer, pipeline\n",
+    "\n",
+    "# Load Model with PEFT adapter\n",
+    "tokenizer = AutoTokenizer.from_pretrained(finetune_name)\n",
+    "model = AutoPeftModelForCausalLM.from_pretrained(\n",
+    "    finetune_name, device_map=\"auto\", torch_dtype=torch.float16\n",
+    ")\n",
+    "pipe = pipeline(\n",
+    "    \"text-generation\", model=merged_model, tokenizer=tokenizer, device=device\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "99uFDAuuouLl"
+   },
+   "source": [
+    "现在我们找些样本来测试："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "id": "-shSmUbvouLl",
+    "outputId": "16d97c61-3b31-4040-c780-3c4de75c3824"
+   },
+   "outputs": [],
+   "source": [
+    "prompts = [\n",
+    "    \"What is the capital of Germany? Explain why thats the case and if it was different in the past?\",\n",
+    "    \"Write a Python function to calculate the factorial of a number.\",\n",
+    "    \"A rectangular garden has a length of 25 feet and a width of 15 feet. If you want to build a fence around the entire garden, how many feet of fencing will you need?\",\n",
+    "    \"What is the difference between a fruit and a vegetable? Give examples of each.\",\n",
+    "]\n",
+    "\n",
+    "\n",
+    "def test_inference(prompt):\n",
+    "    prompt = pipe.tokenizer.apply_chat_template(\n",
+    "        [{\"role\": \"user\", \"content\": prompt}],\n",
+    "        tokenize=False,\n",
+    "        add_generation_prompt=True,\n",
+    "    )\n",
+    "    outputs = pipe(\n",
+    "        prompt,\n",
+    "    )\n",
+    "    return outputs[0][\"generated_text\"][len(prompt) :].strip()\n",
+    "\n",
+    "\n",
+    "for prompt in prompts:\n",
+    "    print(f\"    prompt:\\n{prompt}\")\n",
+    "    print(f\"    response:\\n{test_inference(prompt)}\")\n",
+    "    print(\"-\" * 50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>额外练习：载入自己训练的 LoRA Adapter</h2>\n",
+    "    <p>结束本教程后，你可以使用学到的技术，自己训练一个 LoRA，然后载入 LoRA Adapter</p> \n",
+    "</div>"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/zh-cn/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb b/zh-cn/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
new file mode 100644
index 00000000..b879831b
--- /dev/null
+++ b/zh-cn/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
@@ -0,0 +1,5061 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "otj46qRbtpnd",
+        "outputId": "0fa81296-44b7-479d-e03b-7ef196cc89e4"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q bitsandbytes datasets accelerate loralib\n",
+        "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 446,
+          "referenced_widgets": [
+            "a65941572c1845afb75e39553d89a217",
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+            "573ac3fd009e49c889365a66f29fa387",
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+            "4373238d27e54113bb9344f134ad3c5b",
+            "4454423035964544a7b6799ae7cb8cba",
+            "3fb6936673454d0f91a1c50f411ad5d7",
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+            "27069e667ea24c1896ad413d1fbec939",
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+            "2d47b153a9e347f0881a1475efea68b6",
+            "be8afa280ba84d56b377e45ce48e9f6c",
+            "768c6d8f7a8a4409a30dcbadd2c2396b",
+            "8e2b70c864bd432fa0a7e426ab81744c",
+            "4f1b47e2d7d64765b4f12dcc16878f1f",
+            "993fcaf75d47402d9f9f7c2b094295f1",
+            "e6787d84d8234d368dc48f8d43bdb2a1",
+            "5ecb188fa26a499ea67954aa90108f6e",
+            "1d3184ff92b7465da9f36eeb5726f021",
+            "74af354462ec4d4a83654c5b8ba7a2f9"
+          ]
+        },
+        "id": "cg3fiQOvmI3Q",
+        "outputId": "92fcd520-ad2c-4488-8f66-664ffc566b89"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import bitsandbytes as bnb\n",
+        "from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "S65GcxNGA9kz"
+      },
+      "source": [
+        "## 从 HuggingFace Hub 加载 adapters\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 517,
+          "referenced_widgets": [
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+        "outputId": "83ecbf31-3f84-4b8e-d667-10317f7adcec"
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n",
+        "import transformers\n",
+        "from datasets import load_dataset\n",
+        "from peft import LoraConfig, get_peft_model\n",
+        "from peft import PeftModel, PeftConfig\n",
+        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+        "\n",
+        "peft_model_id = \"ybelkada/opt-6.7b-lora\"\n",
+        "config = PeftConfig.from_pretrained(peft_model_id)\n",
+        "model = AutoModelForCausalLM.from_pretrained(\n",
+        "    config.base_model_name_or_path,\n",
+        "    return_dict=True,\n",
+        "    load_in_8bit=True,\n",
+        "    device_map=\"auto\",\n",
+        ")\n",
+        "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
+        "\n",
+        "# 载入 Lora \n",
+        "model = PeftModel.from_pretrained(model, peft_model_id)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "MDqJWba-tpnv",
+        "outputId": "dd65644f-6921-46e1-920c-400bd7d888cf"
+      },
+      "outputs": [],
+      "source": [
+        "batch = tokenizer(\"Machine learning is: \", return_tensors='pt')\n",
+        "\n",
+        "with torch.cuda.amp.autocast():\n",
+        "  output_tokens = model.generate(**batch, max_new_tokens=50)\n",
+        "\n",
+        "print('\\n\\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True))"
+      ]
+    }
+  ],
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+    "accelerator": "GPU",
+    "colab": {
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diff --git a/zh-cn/3_parameter_efficient_finetuning/prompt_tuning.md b/zh-cn/3_parameter_efficient_finetuning/prompt_tuning.md
new file mode 100644
index 00000000..2372354c
--- /dev/null
+++ b/zh-cn/3_parameter_efficient_finetuning/prompt_tuning.md
@@ -0,0 +1,74 @@
+# Prompt Tuning
+
+Prompt tuning 也是一个高效的微调手段，不同于微调模型参数，它改变的是输入模型的表征。具体来说，prompt tuning 在训练前会添加几个额外的 token，训练过程中，token 的 embedding 被更新，而模型参数一直保持不变。
+
+## 理解 Prompt Tuning
+
+Prompt tuning 通常把可以训练的连续向量（也称为soft prompts）接在输入文本的前面。与离散的文本提示词不同，这些 soft prompts 是通过训练过程中经反向传播更新的，而语言模型则在训练中不变。该方法在 [The Power of Scale for Parameter-Efficient Prompt Tuning](https://arxiv.org/abs/2104.08691) 中提出，展示了其在模型尺寸增大时的有效性：当模型尺寸在 10B 参数量左右时，prompt tuning 只更新 soft prompts 的几百个参数，即可达到模型全量微调的效果。
+
+Soft prompts 是模型的 embedding 空间中的一些连续数值的向量，它们会在微调过程中被更新。传统的 prompts 是一些离散的 tokens，在自然语言层面代表某些语义信息；soft prompt 则没有这些内在含义，经过参数更新后，它被用来从模型中引出一些特定行为。这种方法在多任务领域尤其有效，因为每个任务仅需保存一个 soft prompt 的向量（通常仅几百个参数），而不是对整个模型复制。这种做法不仅现存占用少，而且还能支持快速的任务切换，无需模型重新加载。
+
+## 训练过程
+
+Soft prompts 一般包含 8 到 32 个 tokens，它的初始化可以是随机初始化，也可以是来自现有的文本。初始化过程很影响训练，后者的方法通常效果更好。
+
+训练过程中，仅这些 soft prompts 的参数会被更新；训练用的损失函数也没有变化；但学习率需要我们认真调节，同时也建议观察 soft prompts 上面的梯度信息，以免训练失败。
+
+## 基于 PEFT 的代码实现
+
+使用 PEFT 库实现 prompt tuning 非常简单直接，以下是一个简单的例子：
+
+```python
+from peft import PromptTuningConfig, TaskType, get_peft_model
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load base model
+model = AutoModelForCausalLM.from_pretrained("your-base-model")
+tokenizer = AutoTokenizer.from_pretrained("your-base-model")
+
+# Configure prompt tuning
+peft_config = PromptTuningConfig(
+    task_type=TaskType.CAUSAL_LM,
+    num_virtual_tokens=8,  # soft prompts 的 token 数量
+    prompt_tuning_init="TEXT",  # 初始化方法为从文本初始化
+    prompt_tuning_init_text="Classify if this text is positive or negative:",
+    tokenizer_name_or_path="your-base-model",
+)
+
+# Create prompt-tunable model
+model = get_peft_model(model, peft_config)
+```
+
+## 与其它方法的对比
+
+对比与其它 PEFT 方法，prompt tuning 胜在它的高效性。虽然 LoRA 也减少了训练参数以及所需显存，但反复加载 adapter 来切换任务就很麻烦。Prompt tuning 需要的训练参数更少，且任务切换更加方便。而全量参数微调则既需要超大的训练资源，也需要通过全量参数重新载入来切换任务。
+
+| 方法 | 训练参数量 | 显存需求 | 任务切换难度 |
+|--------|------------|---------|----------------|
+| Prompt Tuning | 很低 | 很低 | 非常简单 |
+| LoRA | 低 | 低 | 需要加载 Adapter |
+| 全量参数微调 | 很高 | 很高 | 加载所有参数 |
+
+在实际 prompt tuning 训练过程中，建议先从较小的 virtual tokens 数量开始（如 8 到 16 个），仅当任务复杂度增加时，再增加 virtual tokens 数量。从文本初始化通常比随机初始化更好，尤其是你使用和任务相关的文本时。初始化方法需要和你的任务复杂度匹配。
+
+训练过程中，你还需注意学习率。如果使用较大的学习率，你需要时刻观察 soft prompt 更新时的梯度信息，以防训练崩溃。训练过程中，定期的验证也是确保性能的良好手段。
+
+## 应用
+
+Prompt tuning 在这些场景下优势明显：
+
+1. 多任务下的大语言模型部署
+2. 计算资源有限的训练场景
+3. 需要在不同任务间快速切换的场景
+4. 针对隐私敏感的应用场景
+
+而当模型变小时，prompt tuning 就没有那么有竞争力了。比如在 SmolLM2 这样的模型尺寸下，prompt tuning 的意义就不大，可能还不如全量微调。
+
+## 接下来
+
+⏭️ 学习 [LoRA Adapters 的教程](./notebooks/finetune_sft_peft.ipynb)，了解如何实操 LoRA 微调模型的过程。
+
+## 学习资源
+- [PEFT 官方文档](https://huggingface.co/docs/peft)
+- [Prompt Tuning 论文](https://arxiv.org/abs/2104.08691)
+- [Hugging Face Cookbook 中关于 prompt tuning 的部分](https://huggingface.co/learn/cookbook/prompt_tuning_peft)
diff --git a/zh-cn/4_evaluation/README.md b/zh-cn/4_evaluation/README.md
new file mode 100644
index 00000000..aaa4700a
--- /dev/null
+++ b/zh-cn/4_evaluation/README.md
@@ -0,0 +1,40 @@
+# 评测
+
+在开发和部署语言模型的过程中，模型评测是一个非常关键的步骤。它有助于我们理解模型在不同方面的能力究竟如何，并找到进一步提示改进的空间。本章将涵盖标准基准测试和特定领域的评估方法，以全面评估你的 smol 模型。
+
+我们将使用 [`lighteval`](https://github.com/huggingface/lighteval) 这个强大的评测代码库。它由 HuggingFace 开发，并完美地集成入了 HuggingFace 的生态系统。我们还提供了[指南书籍](https://github.com/huggingface/evaluation-guidebook)以便读者想要深入学习评测的相关概念和最佳实践。
+
+## 章节总览
+
+一个全面的评估策略会检查模型多个方面的性能。我们将评估模型特定领域的能力，比如回答问题、概括总结，来理解模型处理不同问题的能力。我们通过生成的连贯性和事实准确性等因素来衡量输出质量。同时，我们也需要安全评测，防治模型输出有害的信息或带有偏见的观点。最后，我们还可以进行特定领域的专业性测试，来确认模型是否在特定领域掌握了专业知识。
+
+## 目录
+
+### 1️⃣ [自动化基准测试](./automatic_benchmarks_cn.md)
+
+学习如何使用标准的测试基准和指标来评估模型。我们将会学习常见的测试基准，如 MMLU 和 TruthfulQA，理解重要指标和相关配置，同时为可复现的评估结果提供最佳实践。
+
+### 2️⃣ [自定义领域的评测](./custom_evaluation_cn.md)
+
+学习怎样为你的特定任务领域量身定做评估流程。我们将学习设计自定义评估任务、代码实现特定的指标，以及构建符合你要求的评估数据集。
+
+### 3️⃣ [领域评估的项目示例](./project/README_CN.md)
+
+通过一个完整的例子，学习构建特定领域的评测流程。这包含：生成评测数据集、使用 Argilla 平台进行数据标注、构建标准化的数据集、用 LightEval 评测模型。
+
+### Notebook 练习
+
+| 标题 | 简述 | 习题 | 链接 | Colab |
+|-------|-------------|----------|------|-------|
+| 评测并分析你的大语言模型 | 学习使用 LightEval 在特定领域评测、比较模型 | 🐢 使用医学相关领域的任务评估模型 <br> 🐕 Create a new domain evaluation with different MMLU tasks <br> 🦁 为你的特定任务领域创建一个自定义的评测任务 | [Notebook](./notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+
+
+## Resources
+
+- [评测的指南书籍](https://github.com/huggingface/evaluation-guidebook) - 大语言模型评测领域的全面指南
+- [LightEval 文档](https://github.com/huggingface/lighteval) - LightEval 官方文档
+- [Argilla 文档](https://docs.argilla.io) - 了解 Argilla 标注平台
+- [MMLU 论文](https://arxiv.org/abs/2009.03300) - 关于 MMLU 测评基准的论文
+- [LightEval 如何添加自定义任务](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
+- [LightEval 如何添加自定义测评指标](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
+- [LightEval 如何使用现有的测评指标](https://github.com/huggingface/lighteval/wiki/Metric-List)
\ No newline at end of file
diff --git a/zh-cn/4_evaluation/automatic_benchmarks.md b/zh-cn/4_evaluation/automatic_benchmarks.md
new file mode 100644
index 00000000..9d63a003
--- /dev/null
+++ b/zh-cn/4_evaluation/automatic_benchmarks.md
@@ -0,0 +1,137 @@
+# 自动化基准测试
+
+自动化基准测试可以作为一个标准化的工具，来衡量语言模型在不同任务上的能力。不过，虽然它可以有效地用来了解模型当前的性能，但我们还要认识到，这些评测结果仅仅是模型全面评估的一小部分，不能完全反映模型性能。
+
+## 理解自动化基准评测
+
+自动化基准评测一般是在定义好的领域和评测指标下，对特定的数据集进行测试。这种基准测试会评估模型多方面的能力，从最基础的语言理解到复杂的逻辑推理。其最主要的优点还是标准型方面，不同模型都使用相同标准来测试，可以用来对比不同模型的效果，同时测评出的结果也是可复现的。
+
+然而，我们也需要知道，这种基准测试也不是完全反映模型的现实能力的。比如，一个在学术评测基准上表现优异的模型，也许在其它应用领域或实践层面表现很差。
+
+## 现有评测基准及其局限性
+
+### 通识层面的评测基准
+
+MMLU（Massive Multitask Language Understanding）这个评测基准，涵盖了从科学到人文共 57 个学科的知识，是一个通识层面的评测基准。但虽然全面，它可能在某些领域的专业深度还不算够。另一方面，TruthfulQA 这个评测基准，涵盖 38 个学科问答，则会评测模型输出的真实性如何。
+
+### 推理层面的评测基准
+
+BBH（Big Bench Hard）和 GSM8K 这两个评测基准重点关注复杂的推理任务。BBH 主要测试逻辑思考和规划能力，GSM8K 则特别关注数学问题的求解。这些评测基准可以用来评测模型的分析问题能力，但人在现实世界中微妙的推理细节可能会被忽略。
+
+### 语言理解层面的评测基准
+
+在语言理解层面，HELM 提供了一个全面的评测框架，而 WinoGrande 则通过代词指代的歧义消除，测试模型在常识层面的能力。这些评测基准让我们能深入了解模型在语言处理层面的水平，但缺点是暂未模仿到人与人之间对话的复杂性，同时暂未测评到专业术语。
+
+## 其它评测方法
+
+除了上述评基准，很多机构也开发了其它评测方法，以应对标准化基准测试的缺陷：
+
+### 用大语言模型作为评审
+
+用一个大语言模型去评测另一个大语言模型的输出，这种方法最近开始常用起来。相比于传统的评测指标，这种方法可以提供更细致入微的反馈。缺点是作为评审的大语言模型自己也有偏见和局限性，可能导致评测结果不够好。
+
+### 在竞技场内相互评测
+
+像 Anthropic's Constitutional AI Arena 这样的平台，可以让模型在里面相互互动、评测。这样的评测场景也有助于模型发现各自的强项和弱点。
+
+### 自定义评测基准
+
+很多组织也会自己开发对内的评测基准，通常是针对特定的需求或应用场景开发的。这样开发出来的评测基准一般包含特定的专业领域知识、反映产品的实际应哟过场景。
+
+## Creating Your Own Evaluation Strategy
+
+虽然使用 LightEval 可以很方便地进行标准化的基准评测，但作为一个 LLM 开发者，你必须也要针对你们产品的应用场景开发自己的评测方案。标准化的基准评测仅仅是一测评开始的第一步，你绝不能只用它进行模型测试。
+
+如何自定义你的方位测评？方法如下：
+
+1. 首先从相关的标准化基准测试中开始，建立一个基准，保证能够和其它模型进行对比。
+
+2. 针对你的应用场景的独特需求，确认你的模型将会应对的挑战。例如，你的模型上线后将会主要执行什么任务？有可能出现哪些问题？哪些 bad case 是最应该避免的？
+
+3. 开发你自己的测试数据集，以便专门应对你的测试场景。这可能包括：
+   - 在你的特定领域里，真实用户的请求
+   - 常见的边缘案例
+   - 可能发生的有挑战性的情况
+
+4. 也需要考虑开发一个多层级的评测策略：
+   - 首先，为了能快速获取反馈，你可以设置一个自动化的评测指标
+   - 针对细微的语言理解能力，考虑人工测评
+   - 针对专业领域的应用，引入行业专家的评审
+   - 在控制变量的环境下，进行 A/B 测试
+
+## 使用 LightEval 进行基准评测
+
+LightEval 评测任务通过以下格式定义：
+```
+{suite}|{task}|{num_few_shot}|{auto_reduce}
+```
+
+- **suite**：哪一套基准评测方法（比如 'mmlu'、'truthfulqa'）
+- **task**：这一套基准评测方法中的哪个任务（比如 'abstract_algebra'）
+- **num_few_shot**：提示词中加入的示例的数量（如果是 0，那就是 zero-shot 测试）
+- **auto_reduce**：当提示词太长时，是否自动减少提示词中 few-shot 的样本量
+
+举例来说，`"mmlu|abstract_algebra|0|0"` 就会评测 MMLU 的 abstract algebra 任务，推理是 zero-shot 形式。
+
+### 评测代码示例
+
+以下代码就是一个在某个领域进行自动化评测的示例：
+
+```python
+from lighteval.tasks import Task, Pipeline
+from transformers import AutoModelForCausalLM
+
+# Define tasks to evaluate
+domain_tasks = [
+    "mmlu|anatomy|0|0",
+    "mmlu|high_school_biology|0|0", 
+    "mmlu|high_school_chemistry|0|0",
+    "mmlu|professional_medicine|0|0"
+]
+
+# Configure pipeline parameters
+pipeline_params = {
+    "max_samples": 40,  # Number of samples to evaluate
+    "batch_size": 1,    # Batch size for inference
+    "num_workers": 4    # Number of worker processes
+}
+
+# Create evaluation tracker
+evaluation_tracker = EvaluationTracker(
+    output_path="./results",
+    save_generations=True
+)
+
+# Load model and create pipeline
+model = AutoModelForCausalLM.from_pretrained("your-model-name")
+pipeline = Pipeline(
+    tasks=domain_tasks,
+    pipeline_parameters=pipeline_params,
+    evaluation_tracker=evaluation_tracker,
+    model=model
+)
+
+# Run evaluation
+pipeline.evaluate()
+
+# Get and display results
+results = pipeline.get_results()
+pipeline.show_results()
+```
+
+测评结果会以表格形式打印出来：
+
+```
+|                  Task                  |Version|Metric|Value |   |Stderr|
+|----------------------------------------|------:|------|-----:|---|-----:|
+|all                                     |       |acc   |0.3333|±  |0.1169|
+|leaderboard:mmlu:_average:5             |       |acc   |0.3400|±  |0.1121|
+|leaderboard:mmlu:anatomy:5              |      0|acc   |0.4500|±  |0.1141|
+|leaderboard:mmlu:high_school_biology:5  |      0|acc   |0.1500|±  |0.0819|
+```
+
+使用 pandas 的 `DataFrame` 或其它可视化方式呈现结果也是可以的。
+
+# 接下来
+
+⏩ 学习 [Custom Domain Evaluation](./custom_evaluation_cn.md)，了解如何根据你的特定需求创建自定义的评测流程。
diff --git a/zh-cn/4_evaluation/custom_evaluation.md b/zh-cn/4_evaluation/custom_evaluation.md
new file mode 100644
index 00000000..eb1451f9
--- /dev/null
+++ b/zh-cn/4_evaluation/custom_evaluation.md
@@ -0,0 +1,135 @@
+# 在特定领域进行自定义评测
+
+虽然标准化的评测基准让我们对模型的性能有了初步的认识，但针对特定的应用场景，我们还需要专门制定评测方法，考察模型在特定领域的表现。本文将带你创建自定义的评测流程，针对你的目标领域对模型进行精准评测。
+
+## 设计评测策略
+
+成功创建自定义评测策略的第一步是确定清晰的目标。你需要考虑在你的特定领域，哪些特殊能力是你的模型需要具备的？这可能涉及技术层面的知识、推理的模式、特定的格式等。你需要认真记录好这些需求，然后参考这些需求去设计测试任务、选择评测指标。
+
+测试的样例不仅需要包含标准应用场景，也要考虑边缘场景。举例来说，如果是医学领域，常见的诊断场景和罕见情况都是需要考虑的。在金融领域，除了常规交易，复杂交易（比如设计多种货币或特殊条件的情况）的处理能力也需要被测试到。
+
+## 使用 LightEval 的代码实现
+
+LightEval 是一个非常灵活的框架，可以用来实现自定义的测评任务。下面代码展示了如何创建自定义测试任务：
+
+```python
+from lighteval.tasks import Task, Doc
+from lighteval.metrics import SampleLevelMetric, MetricCategory, MetricUseCase
+
+class CustomEvalTask(Task):
+    def __init__(self):
+        super().__init__(
+            name="custom_task",
+            version="0.0.1",
+            metrics=["accuracy", "f1"],  # Your chosen metrics
+            description="Description of your custom evaluation task"
+        )
+    
+    def get_prompt(self, sample):
+        # Format your input into a prompt
+        return f"Question: {sample['question']}\nAnswer:"
+    
+    def process_response(self, response, ref):
+        # Process model output and compare to reference
+        return response.strip() == ref.strip()
+```
+
+## 自定义评价指标
+
+特定领域的测试任务通常也需要特殊的评价指标。LightEval 也可以灵活地做到这一点：
+
+```python
+from aenum import extend_enum
+from lighteval.metrics import Metrics, SampleLevelMetric, SampleLevelMetricGrouping
+import numpy as np
+
+# Define a sample-level metric function
+def custom_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> dict:
+    """Example metric that returns multiple scores per sample"""
+    response = predictions[0]
+    return {
+        "accuracy": response == formatted_doc.choices[formatted_doc.gold_index],
+        "length_match": len(response) == len(formatted_doc.reference)
+    }
+
+# Create a metric that returns multiple values per sample
+custom_metric_group = SampleLevelMetricGrouping(
+    metric_name=["accuracy", "length_match"],  # Names of sub-metrics
+    higher_is_better={  # Whether higher values are better for each metric
+        "accuracy": True,
+        "length_match": True
+    },
+    category=MetricCategory.CUSTOM,
+    use_case=MetricUseCase.SCORING,
+    sample_level_fn=custom_metric,
+    corpus_level_fn={  # How to aggregate each metric
+        "accuracy": np.mean,
+        "length_match": np.mean
+    }
+)
+
+# Register the metric with LightEval
+extend_enum(Metrics, "custom_metric_name", custom_metric_group)
+```
+
+如果每个样例只有一个指标，代码可以是这样：
+
+```python
+def simple_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> bool:
+    """Example metric that returns a single score per sample"""
+    response = predictions[0]
+    return response == formatted_doc.choices[formatted_doc.gold_index]
+
+simple_metric_obj = SampleLevelMetric(
+    metric_name="simple_accuracy",
+    higher_is_better=True,
+    category=MetricCategory.CUSTOM,
+    use_case=MetricUseCase.SCORING,
+    sample_level_fn=simple_metric,
+    corpus_level_fn=np.mean  # How to aggregate across samples
+)
+
+extend_enum(Metrics, "simple_metric", simple_metric_obj)
+```
+
+实现完代码后，你就可以在你的评测任务中引用这些指标的名称，然后在你的评测任务中使用。这些指标会在测试过程中自动在每个样本上计算，并最终统计数值。
+
+如果需要使用更复杂的评测指标，你还可以实现这些功能：
+
+- 使用元数据，对不同测试样本的分数进行加权或其它调整
+- 对所有样本的指标进行统计时，你可以实现一个自定义的函数（上述示例中 corpus-level 统计使用了取平均的方法）
+- 对输入到你的评测指标函数中的数据进行格式检查
+- 记录边缘场景及其期望的行为
+
+
+
+你可以学习本章 [domain evaluation project](./project/README_CN.md) 这个项目课程，真正地动手实践一下自定义测评。
+
+## 测试数据集的创建
+
+高质量的测评需要高质量的测试数据集。在创建数据集时，需要考虑以下方面：
+
+1. 专家级的标注：与领域专家一起创建和检验测试样本。你可以用 [Argilla](https://github.com/argilla-io/argilla) 高效地进行标注。
+
+2. 真实世界的数据：收集真实数据并进行脱敏，确保这些样本能代表真实部署模型的场景。
+
+3. 借助合成数据：使用 LLM 生成一些初始样本，然后让领域专家检查、修改。这样可以助你快速创建数据集。
+
+## 最佳实践
+
+- 全面记录你的测试方法，包括各种假设和局限
+- 保证测试样本的多样性，确保你的领域内各个方面都能被测试到
+- 如有需要，自动化的测试指标和人工评测都要用上
+- 对测评数据集和代码进行版本控制
+- 定期更新你的评测流程，不断加入新的边缘场景、完善新的需求
+
+## 参考资料
+
+- [LightEval 如何添加自定义任务](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
+- [LightEval 如何添加自定义测评指标](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
+- [Argilla 文档](https://docs.argilla.io) 可以用来进行数据标注
+- [评测的指南书籍](https://github.com/huggingface/evaluation-guidebook) 大语言模型评测领域的全面指南
+- 
+# 接下来
+
+⏩ 完整的自定义测评请见本章 [domain evaluation project](./project/README_CN.md)。
\ No newline at end of file
diff --git a/zh-cn/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb b/zh-cn/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
new file mode 100644
index 00000000..592b84ce
--- /dev/null
+++ b/zh-cn/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
@@ -0,0 +1,19422 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TZVw9f5QYWPL"
+      },
+      "source": [
+        "# 使用 lighteval 作为你的 AI 测评框架\n",
+        "\n",
+        "本文将带你实践如何使用 lighteval 测评大语言模型，并与其它模型进行对比。\n",
+        "\n",
+        "使用 `lighteval` 可以很方便地在主流评测基准上计算评测分数。最近，`lighteval` 被进行了一次重构，以便在 Python 中最为一个库进行使用。\n",
+        "\n",
+        "接下来，我们将试着计算一些分数。\n",
+        "\n",
+        "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+        "    <h2 style='margin: 0;color:blue'>练习：评测你的模型</h2>\n",
+        "    <p>基于前面的学习，你可以试着选一个你感兴趣的领域，在上面测试模型。</p>\n",
+        "    <p><b>难度等级</b></p>\n",
+        "    <p>🐢 从 Hugging Face hub 另找一个模型，在已有的医学领域测试模型</p>\n",
+        "    <p>🐕 选择一个不同的 MMLU 任务（如 comupter science、mathematics、physcics），进行模型测试</p>\n",
+        "    <p>🦁 用 LightEval 自定义评测任务，测试你选的模型</p>\n",
+        "</div>\n",
+        "\n",
+        "## 安装依赖"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "afW_CLJoPCnF",
+        "outputId": "91611cd5-51d6-4f73-9b3d-ac79c918d074"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -qqq -U \"torch<2.5\" \"torchvision<2.5\" \"torchaudio<2.5\" --index-url https://download.pytorch.org/whl/cu121\n",
+        "!pip list | grep torch"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8xMnn_cQ1EEi",
+        "outputId": "bcaf8498-19c3-46af-9ae1-0306326f006b"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -qqq git+https://github.com/huggingface/lighteval.git tiktoken"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TDKs5ShvXw8K"
+      },
+      "source": [
+        "## 配置 `lighteval` 评测环境和评测流程\n",
+        "\n",
+        "我们首先需要配置 `lighteval` 评测环境和评测流程。注意 `push_to_hub` 和 `push_to_tensorboard` 的用法。\n",
+        "\n",
+        "### `push_to_hub`\n",
+        "\n",
+        "如果我们想要把评测结果和模型的配置和权重绑定在一起，我们可以用 `push_to_hub` 将其保存到 Hugging Face hub 上。\n",
+        "\n",
+        "### `push_to_tensorboard`\n",
+        "\n",
+        "如果我们建立了评测工具或脚本用来可视化结果，我们可以使用 `push_to_tensorboard` 来进行可视化。"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 296,
+          "referenced_widgets": [
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+            "be5fb5250a2e442cb767866442fdd4df",
+            "2ddf3728840144949e3b23521833c9ae",
+            "68cb76f44f5446d28fd4895a5cfbfee2",
+            "7d0f948b4ec7480abf9900370f698581",
+            "598f3d372159464181ea84e1bb5bcf86",
+            "18a28139ade541f2938042fff9ad4ec8"
+          ]
+        },
+        "id": "3cUebd-z6IWs",
+        "outputId": "4b5ef2ce-04a0-4b2a-f3d7-5ecb7afb662e"
+      },
+      "outputs": [],
+      "source": [
+        "import lighteval\n",
+        "import os\n",
+        "from datetime import timedelta\n",
+        "from transformers import AutoModelForCausalLM\n",
+        "\n",
+        "from lighteval.logging.evaluation_tracker import EvaluationTracker\n",
+        "from lighteval.logging.hierarchical_logger import hlog_warn, htrack\n",
+        "from lighteval.models.model_config import create_model_config\n",
+        "from lighteval.pipeline import EnvConfig, ParallelismManager, Pipeline, PipelineParameters\n",
+        "\n",
+        "TOKEN = os.getenv(\"HF_TOKEN\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "muikmXNQXgFv"
+      },
+      "outputs": [],
+      "source": [
+        "env_config = EnvConfig(token=TOKEN, cache_dir=\"~/tmp\")\n",
+        "\n",
+        "evaluation_tracker = EvaluationTracker(\n",
+        "    output_dir=\"~/tmp\",\n",
+        "    save_details=False,\n",
+        "    push_to_hub=False,\n",
+        "    push_to_tensorboard=False,\n",
+        "    public=False,\n",
+        "    hub_results_org=False,\n",
+        ")\n",
+        "\n",
+        "pipeline_params = PipelineParameters(\n",
+        "    launcher_type=ParallelismManager.ACCELERATE,\n",
+        "    env_config=env_config,\n",
+        "    job_id=1,\n",
+        "    override_batch_size=1,\n",
+        "    num_fewshot_seeds=0,\n",
+        "    max_samples=10,\n",
+        "    use_chat_template=False,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nsNjwzCtltkA"
+      },
+      "source": [
+        "# 用 `lighteval` 对比不同模型\n",
+        "\n",
+        "这里我们尝试对比两个模型：`Qwen2.5-0.5B` 和 `SmolLM2-360M-Instruct`。我们选择的领域是医学领域。\n",
+        "\n",
+        "这里我们是用 MMLU 的一个字任务创建评测的。在 lighteval 中，不同测试任务可以用字符串指代，格式如下：\n",
+        "\n",
+        "`{suite}|{task}:{subtask}|{num_few_shot}|{0 or 1 to reduce num_few_shot if prompt is too long}`\n",
+        "\n",
+        "所以，针对我们选择的医学领域，我们可以传入一系列任务，用逗号分隔开：\n",
+        "\n",
+        "```\n",
+        "\"leaderboard|mmlu:anatomy|5|0,leaderboard|mmlu:professional_medicine|5|0,leaderboard|mmlu:high_school_biology|5|0,leaderboard|mmlu:high_school_chemistry|5|0\"\n",
+        "```\n",
+        "\n",
+        "上述字符串代表以下四个任务：\n",
+        "\n",
+        "| Suite | Task | Num Fewshot Example | Limit Fewshots |\n",
+        "|---|---|---|---|\n",
+        "| leaderboard | mmlu:anatomy | 5 | False |\n",
+        "| leaderboard | mmlu:professional_medicine | 5 | False |\n",
+        "| leaderboard | mmlu:high_school_biology | 5 | False |\n",
+        "| leaderboard | mmlu:high_school_chemistry | 5 | False |\n",
+        "\n",
+        "完整的任务列表，可以参考[这篇文档](https://github.com/huggingface/lighteval/wiki/Available-Tasks)。"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qTqsizv9mVbO"
+      },
+      "outputs": [],
+      "source": [
+        "domain_tasks = \"leaderboard|mmlu:anatomy|5|0,leaderboard|mmlu:professional_medicine|5|0,leaderboard|mmlu:high_school_biology|5|0,leaderboard|mmlu:high_school_chemistry|5|0\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XwcJklSFX4H6"
+      },
+      "source": [
+        "# 评测 Qwen2.5 0.5B"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
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+          ]
+        },
+        "id": "PXJuaXVxUNBO",
+        "outputId": "87aca361-5bd5-4b12-9097-3e97f1165d7b"
+      },
+      "outputs": [],
+      "source": [
+        "qwen_model = AutoModelForCausalLM.from_pretrained(\"Qwen/Qwen2.5-0.5B\")\n",
+        "\n",
+        "pipeline = Pipeline(\n",
+        "    tasks=domain_tasks,\n",
+        "    pipeline_parameters=pipeline_params,\n",
+        "    evaluation_tracker=evaluation_tracker,\n",
+        "    model=qwen_model\n",
+        ")\n",
+        "\n",
+        "pipeline.evaluate()\n",
+        "\n",
+        "qwen_results = pipeline.get_results()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CIwCaCxJX_hA"
+      },
+      "source": [
+        "# 评测 SmolLM 360M"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
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+        },
+        "id": "Dxg0RtlNVT4y",
+        "outputId": "ee40393c-bf62-499d-d443-e96da72411e8"
+      },
+      "outputs": [],
+      "source": [
+        "smol_model = AutoModelForCausalLM.from_pretrained(\"HuggingFaceTB/SmolLM2-360M-Instruct\")\n",
+        "\n",
+        "pipeline = Pipeline(\n",
+        "    tasks=domain_tasks,\n",
+        "    pipeline_parameters=pipeline_params,\n",
+        "    evaluation_tracker=evaluation_tracker,\n",
+        "    model=smol_model\n",
+        ")\n",
+        "\n",
+        "pipeline.evaluate()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pdjyzfKHVt52"
+      },
+      "outputs": [],
+      "source": [
+        "smol_results = pipeline.get_results()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eugvMFfgV1VD",
+        "outputId": "62956e3f-29c8-4493-f868-4ba63b7d8e96"
+      },
+      "outputs": [],
+      "source": [
+        "pipeline.show_results()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0HD8aFwSYGHu"
+      },
+      "source": [
+        "# 可视化结果\n",
+        "\n",
+        "得到结果后，我们可以可视化一下，并列在一起对比。这里我们使用了 `pandas` 简单地实现了可视化操作，不过你也可以使用不同的可视化方法："
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 447
+        },
+        "id": "1sReqrgQUO9r",
+        "outputId": "9a0779e1-c278-48ab-9ecc-5055028ea3fd"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "df = pd.DataFrame.from_records(smol_results[\"results\"]).T[\"acc\"].rename(\"SmolLM2-360M-Instruct\")\n",
+        "_df = pd.DataFrame.from_records(qwen_results[\"results\"]).T[\"acc\"].rename(\"Qwen2-0.5B-DPO\")\n",
+        "df = pd.concat([df, _df], axis=1)\n",
+        "df.plot(kind=\"barh\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qJEbQeYDplKX"
+      },
+      "source": [
+        "# 💐 就是这些！\n",
+        "\n",
+        "本文提供了标准化评测的简单代码，使用这些代码，你还可以：\n",
+        "\n",
+        "- 选择模型进行推理\n",
+        "- 在训练过程中针对不同阶段保存的模型实时测试\n",
+        "- 分享你的测试成绩到开源社区"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jWdS38syaipm"
+      },
+      "source": [
+        "🏃接下来\n",
+        "\n",
+        "- 如果你还想深入学习，可以看看这个 [notebook](https://github.com/huggingface/evaluation-guidebook/blob/main/contents/examples/comparing_task_formulations.ipynb)\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "A100",
+      "machine_shape": "hm",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
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diff --git a/zh-cn/4_evaluation/project/README.md b/zh-cn/4_evaluation/project/README.md
new file mode 100644
index 00000000..c896e4af
--- /dev/null
+++ b/zh-cn/4_evaluation/project/README.md
@@ -0,0 +1,81 @@
+# 用 Argilla、Distilabel 和 LightEval 进行特定领域评测
+
+绝大多数的常用评测基准关注的都是模型非常基本的能力，比如推理、数学、编程等，而忽略了特定的专业领域能力。如何进行专业领域（如金融、法律、医学等）的模型评测呢？
+
+本教程将会展示**自定义领域**模型测试的完整流程。我们重点关注数据部分。在相关数据收集、标注测试数据方面，我们会使用 [Argilla](https://github.com/argilla-io/argilla)、[Distilabel](https://github.com/argilla-io/distilabel) 和 [LightEval](https://github.com/huggingface/lighteval) 作为工具，生成考试问题相关的数据。
+
+
+## 项目结构
+
+本项目包含四份 Python 代码文件。我们分四个步骤完成模型在自定义领域的测评，每份代码对应一个步骤。这四个步骤分别是：数据生成、数据标注、相关测试样本的提取，以及模型评测。
+
+| 代码文件 | 概述 |
+|-------------|-------------|
+| generate_dataset.py | 使用一个专门的语言模型，从多个文本文档中生成考试问题。 |
+| annotate_dataset.py | 用 Argilla 创建一个数据集，手动为生成的考试问题数据进行标注。 |
+| create_dataset.py | 处理标注过的数据，并创建对应的 HuggingFace 数据集。 |
+| evaluation_task.py | 自定义了一个 LightEval 任务，在前面建立好的测试数据上测试。 |
+
+## 步骤
+
+### 1. 数据集的生成
+
+使用 `generate_dataset.py`，我们可以用 `distilabel` 这个库根据几个文本文档生成一些考试问题。一个特定的模型（这里默认使用 Meta-Llama-3.1-8B-Instruct）被拿来用以生成问题、正确答案和错误答案（错误答案用来作为干扰项）。你需要加入你自己的数据，也可以切换使用别的模型。
+
+通过以下命令可以开始生成：
+
+```sh
+python generate_dataset.py --input_dir path/to/your/documents --model_id your_model_id --output_path output_directory
+```
+
+代码运行中将会创建一个 [Distiset](https://distilabel.argilla.io/dev/sections/how_to_guides/advanced/distiset/)，它包含根据文档生成的考试问题，其中保存文档的目录是 `input_dir`。
+
+### 2. 标注数据集
+
+使用 `annotate_dataset.py` 可以将生成的问题创建为一个 Argilla 数据集，用以标注。该程序会构建起数据集结构，并把生成的问题和回答填充进统一结构中，还可以改变数据样本的顺序来避免偏向性。使用 Argilla，你或者一个领域专家可以用选择的方式给出每个问题的正确回答。
+
+在标注界面中，已生成的几个回答会随机排列着，供标注人员选择正确答案。这其中，LLM 会提供一个建议的正确回答，你可以选择你认为正确的回答，也可以对选中的正确回答进行编辑改进。标注过程的耗时取决于你的数据集规模、领域内数据的复杂度，以及 LLM 的能力强弱。举例来说，我们是可以借助 Llama-3.1-70B-Instruct 在 1 小时内标注好迁移学习领域 150 个样本的，大多数时候，直接选择正确答案即可。
+
+通过以下命令可以开始标注：
+
+
+```sh
+python annotate_dataset.py --dataset_path path/to/distiset --output_dataset_name argilla_dataset_name
+```
+
+这将会创建一个 Argilla 数据集，用来手工检查和标注数据。
+
+![argilla_dataset](./images/domain_eval_argilla_view.png)
+
+可以参考[这里的指引](https://docs.argilla.io/latest/getting_started/quickstart/)，在本地或 Hugging Face 的 space 里部署标注任务。
+
+### 3. 创建数据集
+
+使用 `create_dataset.py` 可以进一步处理 Argilla 标注的数据，并创建一个 Hugging Face 数据集。这个数据集里每条数据包含这些内容：问题、可能的回答、正确回答（所在的列的名字）。运行以下命令即可创建最终的数据集：
+
+```sh
+huggingface_hub login
+python create_dataset.py --dataset_path argilla_dataset_name --dataset_repo_id your_hf_repo_id
+```
+
+最终，数据集会被推送到 Hugging Face Hub 里。本示例的数据集已经上传了，可以在[这里](https://huggingface.co/datasets/burtenshaw/exam_questions/viewer/default/train)查看，大致是这样：
+
+![hf_dataset](./images/domain_eval_dataset_viewer.png)
+
+### 4. 开始测评
+
+使用 `evaluation_task.py`，你可以自定义一个 LightEval 任务，用来在前面创建的数据集上测试模型。具体执行命令如下：
+
+```sh
+lighteval accelerate \
+    --model_args "pretrained=HuggingFaceH4/zephyr-7b-beta" \
+    --tasks "community|exam_questions|0|0" \
+    --custom_tasks domain-eval/evaluation_task.py \
+    --output_dir "./evals"
+```
+
+此外，lighteval 的 wiki 也提供了更详细的讲解：
+
+- [自定义评测任务](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
+- [自定义评测指标](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
+- [使用已有的评测指标](https://github.com/huggingface/lighteval/wiki/Metric-List)
diff --git a/zh-cn/4_evaluation/project/annotate_dataset.py b/zh-cn/4_evaluation/project/annotate_dataset.py
new file mode 100644
index 00000000..dadffc10
--- /dev/null
+++ b/zh-cn/4_evaluation/project/annotate_dataset.py
@@ -0,0 +1,129 @@
+import argparse
+import json
+from random import choices, sample
+
+import argilla as rg
+from distilabel.distiset import Distiset
+
+################################################################################
+# Script Parameters
+################################################################################
+
+parser = argparse.ArgumentParser(
+    description="Annotate exam questions dataset using Argilla."
+)
+parser.add_argument(
+    "--argilla_api_key",
+    type=str,
+    default="argilla.apikey",
+    help="API key for Argilla",
+)
+parser.add_argument(
+    "--argilla_api_url",
+    type=str,
+    default="http://localhost:6900",
+    help="API URL for Argilla",
+)
+parser.add_argument(
+    "--dataset_path",
+    type=str,
+    default="exam_questions",
+    help="Path to the exam questions dataset",
+)
+parser.add_argument(
+    "--dataset_config",
+    type=str,
+    default="default",
+    help="Configuration of the dataset",
+)
+parser.add_argument(
+    "--dataset_split",
+    type=str,
+    default="train",
+    help="Split of the dataset to use",
+)
+parser.add_argument(
+    "--output_dataset_name",
+    type=str,
+    default="exam_questions",
+    help="Name of the output Argilla dataset",
+)
+
+args = parser.parse_args()
+
+################################################################################
+# Create Argilla dataset with the feedback task for validation
+################################################################################
+
+client = rg.Argilla(api_key=args.argilla_api_key, api_url=args.argilla_api_url)
+
+if client.datasets(args.output_dataset_name):
+    print(f"Deleting existing dataset '{args.output_dataset_name}'")
+    client.datasets(args.output_dataset_name).delete()
+
+settings = rg.Settings(
+    fields=[
+        rg.TextField("question"),
+        rg.TextField("answer_a"),
+        rg.TextField("answer_b"),
+        rg.TextField("answer_c"),
+        rg.TextField("answer_d"),
+    ],
+    questions=[
+        rg.LabelQuestion(
+            name="correct_answer",
+            labels=["answer_a", "answer_b", "answer_c", "answer_d"],
+        ),
+        rg.TextQuestion(
+            name="improved_question",
+            description="Could you improve the question?",
+        ),
+        rg.TextQuestion(
+            name="improved_answer",
+            description="Could you improve the best answer?",
+        ),
+    ],
+)
+
+dataset = rg.Dataset(settings=settings, name=args.output_dataset_name)
+dataset.create()
+
+################################################################################
+# Load the Distiset and process and add records to Argilla dataset
+# We will validate that questions appear in random order to avoid bias
+# but we will show correct answers in the Argilla UI as suggestions.
+################################################################################
+
+distiset = Distiset.load_from_disk(args.dataset_path)
+answer_names = ["answer_a", "answer_b", "answer_c", "answer_d"]
+dataset_records = []
+
+for exam in distiset[args.dataset_config][args.dataset_split]:
+    exam_json = json.loads(exam["generation"])["exam"]
+
+    for question in exam_json:
+        answer = question["answer"]
+        distractors = question["distractors"]
+        distractors = choices(distractors, k=3)
+        answers = distractors + [answer]
+        answers = sample(answers, len(answers))
+        suggestion_idx = answers.index(answer)
+        fields = dict(zip(answer_names, answers))
+        fields["question"] = question["question"]
+
+        record = rg.Record(
+            fields=fields,
+            suggestions=[
+                rg.Suggestion(
+                    question_name="correct_answer",
+                    value=answer_names[suggestion_idx],
+                )
+            ],
+        )
+        dataset_records.append(record)
+
+dataset.records.log(dataset_records)
+
+print(
+    f"Dataset '{args.output_dataset_name}' has been created and populated in Argilla."
+)
diff --git a/zh-cn/4_evaluation/project/create_dataset.py b/zh-cn/4_evaluation/project/create_dataset.py
new file mode 100644
index 00000000..822e268e
--- /dev/null
+++ b/zh-cn/4_evaluation/project/create_dataset.py
@@ -0,0 +1,72 @@
+import argparse
+
+import argilla as rg
+from datasets import Dataset
+
+################################################################################
+# Script Parameters
+################################################################################
+
+parser = argparse.ArgumentParser(
+    description="Create a Hugging Face dataset from annotated Argilla data."
+)
+parser.add_argument(
+    "--argilla_api_key",
+    type=str,
+    default="argilla.apikey",
+    help="API key for Argilla",
+)
+parser.add_argument(
+    "--argilla_api_url",
+    type=str,
+    default="http://localhost:6900",
+    help="API URL for Argilla",
+)
+parser.add_argument(
+    "--dataset_path",
+    type=str,
+    default="exam_questions",
+    help="Path to the Argilla dataset",
+)
+parser.add_argument(
+    "--dataset_repo_id",
+    type=str,
+    default="burtenshaw/exam_questions",
+    help="Hugging Face dataset repository ID",
+)
+
+args = parser.parse_args()
+
+################################################################################
+# Initialize Argilla client and load dataset
+################################################################################
+
+client = rg.Argilla(api_key=args.argilla_api_key, api_url=args.argilla_api_url)
+dataset = client.datasets(args.dataset_path)
+
+################################################################################
+# Process Argilla records
+################################################################################
+
+dataset_rows = []
+
+for record in dataset.records(with_suggestions=True, with_responses=True):
+    row = record.fields
+
+    if len(record.responses) == 0:
+        answer = record.suggestions["correct_answer"].value
+        row["correct_answer"] = answer
+    else:
+        for response in record.responses:
+            if response.question_name == "correct_answer":
+                row["correct_answer"] = response.value
+    dataset_rows.append(row)
+
+################################################################################
+# Create Hugging Face dataset and push to Hub
+################################################################################
+
+hf_dataset = Dataset.from_list(dataset_rows)
+hf_dataset.push_to_hub(repo_id=args.dataset_repo_id)
+
+print(f"Dataset has been successfully pushed to {args.dataset_repo_id}")
diff --git a/zh-cn/4_evaluation/project/evaluation_task.py b/zh-cn/4_evaluation/project/evaluation_task.py
new file mode 100644
index 00000000..397c75ad
--- /dev/null
+++ b/zh-cn/4_evaluation/project/evaluation_task.py
@@ -0,0 +1,86 @@
+import numpy as np
+
+from lighteval.tasks.lighteval_task import LightevalTaskConfig
+from lighteval.tasks.requests import Doc
+from lighteval.metrics.utils.metric_utils import (
+    SampleLevelMetric,
+    MetricCategory,
+    MetricUseCase,
+)
+
+################################################################################
+# Define the prompt function based on the structure of the dataset
+################################################################################
+
+
+def prompt_fn(line, task_name: str = None):
+    """Converts a dataset line to a Doc object for evaluation."""
+    instruction = "Choose the correct answer for the following exam question:"
+    return Doc(
+        task_name=task_name,
+        query=f"{instruction} {line['question']}",
+        choices=[
+            f" {line['answer_a']}",
+            f" {line['answer_b']}",
+            f" {line['answer_c']}",
+            f" {line['answer_d']}",
+        ],
+        gold_index=["answer_a", "answer_b", "answer_c", "answer_d"].index(
+            line["correct_answer"]
+        ),
+        instruction=instruction,
+    )
+
+
+################################################################################
+# Define the custom metric based on guide here https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric
+# Or use an existing metric based on the guide here: https://github.com/huggingface/lighteval/wiki/Metric-List
+# Existing metrics can be imported from lighteval.metrics.metrics
+################################################################################
+
+
+def sample_level_fn(formatted_doc: Doc, **kwargs) -> bool:
+    response = np.argmin(kwargs["choices_logprob"])
+    return response == formatted_doc.gold_index
+
+
+custom_metric = SampleLevelMetric(
+    metric_name="exam_question_accuracy",
+    higher_is_better=True,
+    category=MetricCategory.MULTICHOICE,
+    use_case=MetricUseCase.NONE,
+    sample_level_fn=sample_level_fn,
+    corpus_level_fn=np.mean,
+)
+
+################################################################################
+# Define the task based on the prompt function and the custom metric
+# Based on the guide here: https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task
+################################################################################
+
+task = LightevalTaskConfig(
+    name="example",
+    prompt_function=prompt_fn,
+    suite=["community"],
+    hf_repo="burtenshaw/exam_questions",
+    hf_subset="default",
+    hf_avail_splits=["train"],
+    evaluation_splits=["train"],
+    few_shots_split=None,
+    few_shots_select=None,
+    metric=[custom_metric],
+)
+
+# Add the task to TASKS_TABLE
+TASKS_TABLE = [task]
+
+# MODULE LOGIC
+if __name__ == "__main__":
+    print([t.name for t in TASKS_TABLE])
+    print(len(TASKS_TABLE))
+
+# lighteval accelerate \
+# "pretrained=HuggingFaceTB/SmolLM2-135M-Instruct" \
+# "community|example|0|0" \
+# --custom-tasks "submitted_tasks/example.py" \
+# --output-dir "results"
diff --git a/zh-cn/4_evaluation/project/generate_dataset.py b/zh-cn/4_evaluation/project/generate_dataset.py
new file mode 100644
index 00000000..e0dbd6ed
--- /dev/null
+++ b/zh-cn/4_evaluation/project/generate_dataset.py
@@ -0,0 +1,168 @@
+import argparse
+import os
+from pydantic import BaseModel, Field
+from datasets import Dataset
+from typing import List
+
+from distilabel.llms import InferenceEndpointsLLM
+from distilabel.pipeline import Pipeline
+from distilabel.steps.tasks import TextGeneration
+
+
+################################################################################
+# Script Parameters
+################################################################################
+
+parser = argparse.ArgumentParser(
+    description="Generate exam questions from text files in a directory."
+)
+parser.add_argument(
+    "--model_id",
+    type=str,
+    default="Qwen/Qwen2.5-7B-Instruct",
+    help="Model ID for text generation",
+)
+parser.add_argument(
+    "--tokenizer_id",
+    type=str,
+    default="Qwen/Qwen2.5-7B-Instruct",
+    help="Tokenizer ID for text generation",
+)
+parser.add_argument(
+    "--input_dir",
+    type=str,
+    help="Directory containing input text files",
+    default="data",
+)
+parser.add_argument(
+    "--max_new_tokens",
+    type=int,
+    default=2048,
+    help="Maximum number of new tokens to generate",
+)
+parser.add_argument(
+    "--output_path",
+    type=str,
+    default="exam_questions_output",
+    help="Directory to save the generated datasets",
+)
+
+args = parser.parse_args()
+
+################################################################################
+# Load the documents
+# We assume that the documents are in the input directory, and that each file
+# is a separate document about the same topic.
+################################################################################
+
+# Process all text files in the input directory
+documents = []
+for filename in os.listdir(args.input_dir):
+    if filename.endswith(".txt"):
+        file_path = os.path.join(args.input_dir, filename)
+        with open(file=file_path, mode="r", encoding="utf-8") as file:
+            document_content = file.read()
+            documents.append(document_content)
+
+# Create a single dataset from all document contents
+dataset = Dataset.from_dict({"document": documents})
+
+################################################################################
+# Define the prompts
+# We use a system prompt to guide the model to generate the correct output format.
+# A template is used to insert the document into the prompt.
+################################################################################
+
+SYSTEM_PROMPT = """\
+You are an exam writer specialized in writing exams for students.
+Your goal is to create questions and answers based on the document provided, 
+and a list of distractors, that are incorrect but viable answers to the question.
+Your answer must adhere to the following format:
+```
+[
+    {
+        "question": "Your question",
+        "answer": "The correct answer to the question",
+        "distractors": ["wrong answer 1", "wrong answer 2", "wrong answer 3"]
+    },
+    ... (more questions and answers as required)
+]
+```
+""".strip()
+
+INSTRUCTION_TEMPLATE = """\
+    Generate a list of answers and questions about the document. 
+    Document:\n\n{{ instruction }}"""
+
+################################################################################
+# Define the output structure
+# We define a data model for the output of the pipeline, this is used to ensure
+# that the output is in the correct format for the evaluation task.
+################################################################################
+
+
+class ExamQuestion(BaseModel):
+    question: str = Field(..., description="The question to be answered")
+    answer: str = Field(..., description="The correct answer to the question")
+    distractors: List[str] = Field(
+        ..., description="A list of incorrect but viable answers to the question"
+    )
+
+
+class ExamQuestions(BaseModel):
+    exam: List[ExamQuestion]
+
+
+################################################################################
+# Create the pipeline
+# We create a pipeline with a single task that generates the exam questions
+# based on the document and in the correct format. We will Hugging Face
+# InferenceEndpoints and the model specified in the arguments.
+################################################################################
+
+with Pipeline(
+    name="Domain-Eval-Questions",
+    description="Generate exam questions based on given documents.",
+) as pipeline:
+    # Set up the text generation task
+    text_generation = TextGeneration(
+        name="exam_generation",
+        llm=InferenceEndpointsLLM(
+            model_id=args.model_id,
+            tokenizer_id=args.model_id,
+            api_key=os.environ["HF_TOKEN"],
+            structured_output={
+                "schema": ExamQuestions.model_json_schema(),
+                "format": "json",
+            },
+        ),
+        input_batch_size=8,
+        output_mappings={"model_name": "generation_model"},
+        input_mappings={"instruction": "document"},
+        system_prompt=SYSTEM_PROMPT,
+        template=INSTRUCTION_TEMPLATE,
+    )
+
+
+################################################################################
+# Run the pipeline
+# We run the pipeline for all documents and save the results to the output path.
+################################################################################
+
+if __name__ == "__main__":
+    # Run the pipeline for all documents
+    distiset = pipeline.run(
+        parameters={
+            "exam_generation": {
+                "llm": {
+                    "generation_kwargs": {
+                        "max_new_tokens": args.max_new_tokens,
+                    }
+                }
+            }
+        },
+        use_cache=False,
+        dataset=dataset,
+    )
+
+    distiset.save_to_disk(args.output_path)
diff --git a/zh-cn/4_evaluation/project/images/domain_eval_argilla_view.png b/zh-cn/4_evaluation/project/images/domain_eval_argilla_view.png
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diff --git a/zh-cn/5_vision_language_models/README.md b/zh-cn/5_vision_language_models/README.md
new file mode 100644
index 00000000..cc087c22
--- /dev/null
+++ b/zh-cn/5_vision_language_models/README.md
@@ -0,0 +1,40 @@
+# 视觉语言模型
+
+## 1. 视觉语言模型的用处
+
+视觉语言模型（Vision Language Models 或简称 VLM）是一种不仅仅接收语言输入、而且可以处理图片输入的模型，它可以支持诸如图像文本描述生成（image captioning）、视觉问答（visual question answering）、多模态推理（multimodal reasoning）等任务。
+
+一个典型的 VLM 架构包含一个图像编码器（用来提取视觉特征）、一个映射层（用来对齐视觉特征和文本表征）以及一个语言模型（用以处理视觉语言特征并输出文本）。这使得模型得以在视觉元素和语言概念之间建立起连接。
+
+VLM 可以有很多用处。基本的用途包括通用的视觉语言任务，而那些针对聊天对话场景优化过的 VLM 则可以支持对话式的人机互动。还有些模型可以根据图像中的证据预测事实，或者进行特定的任务，如物体检测。
+
+关于 VLM 的更多技术和使用，建议读者在 [VLM 的使用](./vlm_usage_cn.md)这一节中学习。
+
+## 2. 视觉语言模型的微调
+
+微调一个 VLM 的过程，通常是指选择一个预训练过的模型，在一个新的数据集上学习处理特定领域的任务。这个过程可以参考本教程第 1 和 2 章的相关方法，比如有监督微调、偏好优化等。
+
+虽然核心工具与技术和大语言模型（LLMs）所用的大致相同，但微调视觉语言模型（VLMs）需要格外关注图像的数据表示与准备。如此才能确保模型有效整合并处理视觉与文本数据，以实现最佳性能。鉴于演示模型 SmolVLM 比前一模块使用的语言模型大得多，探索高效的微调方法至关重要。量化和参数高效微调（PEFT）等技术，能让这一过程更易于操作且成本更低，使更多用户能够对该模型进行试验。
+
+如果你想了解详细的微调 VLM 的技术，你可以学习 [VLM 微调](./vlm_finetuning_cn.md) 这一节。  
+
+
+## Exercise Notebooks  
+
+
+| 标题 | 概述 | 练习 | 链接 | Colab |
+|-------|-------------|----------|------|-------|
+| VLM 的使用 | 学习如何载入并使用一个预训练过的 VLM 来处理各种任务 | 🐢 尝试处理一张图片 <br>🐕 尝试用 batch 的方式处理多个图片 <br>🦁 尝试处理一整个视频 | [Notebook](./notebooks/vlm_usage_sample_cn.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/5_vision_language_models/notebooks/vlm_usage_sample.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+| VLM 微调 | 学习针对特定任务数据集来微调一个预训练过的 VLM | 🐢 使用一个基本数据集进行微调<br>🐕 尝试使用新数据集 <br>🦁 试验一种不同的微调方法 | [Notebook](./notebooks/vlm_sft_sample_cn.ipynb)| <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/5_vision_language_models/notebooks/vlm_sft_sample.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | 
+
+
+## 参考资料  
+- [Hugging Face Learn: Supervised Fine-Tuning VLMs](https://huggingface.co/learn/cookbook/fine_tuning_vlm_trl)
+- [Hugging Face Learn: Supervised Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_smol_vlm_sft_trl)  
+- [Hugging Face Learn: Preference Optimization Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_vlm_dpo_smolvlm_instruct)  
+- [Hugging Face Blog: Preference Optimization for VLMs](https://huggingface.co/blog/dpo_vlm)
+- [Hugging Face Blog: Vision Language Models](https://huggingface.co/blog/vlms)
+- [Hugging Face Blog: SmolVLM](https://huggingface.co/blog/smolvlm)  
+- [Hugging Face Model: SmolVLM-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct)
+- [CLIP: Learning Transferable Visual Models from Natural Language Supervision](https://arxiv.org/abs/2103.00020)  
+- [Align Before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://arxiv.org/abs/2107.07651)  
diff --git a/zh-cn/5_vision_language_models/images/VLM_Architecture.png b/zh-cn/5_vision_language_models/images/VLM_Architecture.png
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diff --git a/zh-cn/5_vision_language_models/notebooks/vlm_sft_sample.ipynb b/zh-cn/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
new file mode 100644
index 00000000..9ddf65a6
--- /dev/null
+++ b/zh-cn/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
@@ -0,0 +1,461 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 微调一个 VLM \n",
+    "\n",
+    "本文将展示如何用 `trl` 库的 `SFTTrainer` 微调 `HuggingFaceTB/SmolVLM-Instruct` 模型。文中的代码均可运行，你可选择不同的微调数据集尝试训练。\n",
+    "\n",
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>练习：用 SFTTrainer 微调 SmolVLM </h2>\n",
+    "    <p>从 Hugging Face hub 找一个数据，在该数据集上微调模型 </p> \n",
+    "    <p><b>难度等级</b></p>\n",
+    "    <p>🐢 用 `HuggingFaceM4/ChartQA` 数据集进行 SFT </p>\n",
+    "    <p>🐕 用微调过的模型生成回答，和未微调的模型进行效果对比</p>\n",
+    "    <p>🦁 尝试使用不同的数据集进行微调训练，并对比效果</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install the requirements in Google Colab\n",
+    "# !pip install transformers datasets trl huggingface_hub\n",
+    "\n",
+    "# Authenticate to Hugging Face\n",
+    "from huggingface_hub import login\n",
+    "login()\n",
+    "\n",
+    "# for convenience you can create an environment variable containing your hub token as HF_TOKEN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import dependencies\n",
+    "\n",
+    "import torch\n",
+    "import os\n",
+    "from transformers import AutoProcessor, AutoModelForVision2Seq, BitsAndBytesConfig\n",
+    "from transformers.image_utils import load_image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`low_cpu_mem_usage` was None, now default to True since model is quantized.\n",
+      "You shouldn't move a model that is dispatched using accelerate hooks.\n",
+      "Some kwargs in processor config are unused and will not have any effect: image_seq_len. \n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "device = (\n",
+    "    \"cuda\"\n",
+    "    if torch.cuda.is_available()\n",
+    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
+    ")\n",
+    "\n",
+    "quantization_config = BitsAndBytesConfig(load_in_4bit=True, \n",
+    "                                         bnb_4bit_use_double_quant=True, \n",
+    "                                         bnb_4bit_quant_type=\"nf4\", \n",
+    "                                         bnb_4bit_compute_dtype=torch.bfloat16)\n",
+    "model_name = \"HuggingFaceTB/SmolVLM-Instruct\"\n",
+    "model = AutoModelForVision2Seq.from_pretrained(\n",
+    "    model_name,\n",
+    "    quantization_config=quantization_config,\n",
+    "    torch_dtype=torch.bfloat16,\n",
+    ").to(device)\n",
+    "processor = AutoProcessor.from_pretrained(\"HuggingFaceTB/SmolVLM-Instruct\")\n",
+    "\n",
+    "# Set our name for the finetune to be saved &/ uploaded to\n",
+    "finetune_name = \"SmolVLM-FT-MyDataset\"\n",
+    "finetune_tags = [\"smol-course\", \"module_5\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 数据集准备\n",
+    "\n",
+    "我们将加载一个示例数据集，并对其进行格式化以用于训练。该数据集应构建为输入-输出对的形式，其中每个输入是一个提示语（包含文本和图像），输出是模型预期的响应。\n",
+    "\n",
+    "**TRL 将根据模型的聊天模板对输入消息进行格式化。** 它们会被整理成一个字典列表，字典包含的 key 有 `role` 和 `content`。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['image', 'query', 'label', 'human_or_machine'],\n",
+       "        num_rows: 28299\n",
+       "    })\n",
+       "    val: Dataset({\n",
+       "        features: ['image', 'query', 'label', 'human_or_machine'],\n",
+       "        num_rows: 1920\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['image', 'query', 'label', 'human_or_machine'],\n",
+       "        num_rows: 2500\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load a sample dataset\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "# TODO: define your dataset and config using the path and name parameters\n",
+    "dataset_name = \"HuggingFaceM4/ChartQA\"\n",
+    "ds = load_dataset(path=dataset_name)\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=314x281 at 0x7D354DDE73D0>, 'query': 'What is the sum favourable  value in the year 2014 and 2015?', 'label': ['95'], 'human_or_machine': 0, 'image_size': ((314, 281),)}\n",
+      "Query: What is the sum favourable  value in the year 2014 and 2015?\n",
+      "Expected Answer: 95\n",
+      "Model Prediction: User:<image>What is the sum favourable  value in the year 2014 and 2015?\n",
+      "Answer: 91.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "sample = ds['train'][3]\n",
+    "\n",
+    "# Display the structure of a single sample\n",
+    "sample['image_size'] = sample['image'].size,\n",
+    "\n",
+    "# Visualize the image and related metadata\n",
+    "plt.imshow(sample['image'])\n",
+    "plt.axis(\"off\")\n",
+    "plt.title(\"Sample Chart Image\")\n",
+    "plt.show()\n",
+    "\n",
+    "print(sample)\n",
+    "\n",
+    "# Preprocess the sample\n",
+    "prompt = [{\"role\": \"user\", \"content\": [{\"type\": \"image\"}, {\"type\": \"text\", \"text\": sample[\"query\"]}]}]\n",
+    "formatted_query = processor.apply_chat_template(prompt, tokenize=False)\n",
+    "\n",
+    "inputs = processor(\n",
+    "    images=sample[\"image\"], \n",
+    "    text=formatted_query, \n",
+    "    return_tensors=\"pt\"\n",
+    ").to(device)\n",
+    "inputs = {key: val.to(device, dtype=torch.bfloat16) if val.dtype == torch.float else val.to(device) for key, val in inputs.items()}\n",
+    "\n",
+    "# Generate predictions\n",
+    "with torch.no_grad():\n",
+    "    outputs = model.generate(**inputs,\n",
+    "                             max_length=1600)\n",
+    "\n",
+    "# Decode the prediction\n",
+    "prediction = processor.batch_decode(outputs, skip_special_tokens=True)\n",
+    "\n",
+    "# Display the result\n",
+    "print(f\"Query: {sample['query']}\")\n",
+    "print(f\"Expected Answer: {sample['label'][0]}\")\n",
+    "print(f\"Model Prediction: {prediction[0]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 配置 LoRA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trainable params: 2,568,192 || all params: 2,248,841,072 || trainable%: 0.1142\n"
+     ]
+    }
+   ],
+   "source": [
+    "from peft import LoraConfig, get_peft_model\n",
+    "\n",
+    "# Configure LoRA\n",
+    "peft_config = LoraConfig(\n",
+    "    lora_alpha=16,\n",
+    "    lora_dropout=0.05,\n",
+    "    r=8,\n",
+    "    bias=\"none\",\n",
+    "    target_modules=[\"q_proj\", \"v_proj\"],\n",
+    "    task_type=\"CAUSAL_LM\",\n",
+    ")\n",
+    "\n",
+    "# Apply PEFT model adaptation\n",
+    "peft_model = get_peft_model(model, peft_config)\n",
+    "\n",
+    "# Print trainable parameters\n",
+    "peft_model.print_trainable_parameters()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 配置 Trainer\n",
+    "\n",
+    "通过调整 `Trainer` 的各种参数，我们可以控制训练过程。这些参数包括训练步数、batch size、学习率以及评估策略。你可根据具体需求和计算资源来调整这些参数。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def collate_fn(examples):\n",
+    "    # System message template for the VLM\n",
+    "    system_message = \"\"\"You are a Vision Language Model specialized in interpreting visual data from chart images.\n",
+    "    Your task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\n",
+    "    The charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\n",
+    "    Focus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.\"\"\"\n",
+    "\n",
+    "    # Initialize lists for text and image inputs\n",
+    "    text_inputs = []\n",
+    "    image_inputs = []\n",
+    "\n",
+    "    # Process all examples in one loop\n",
+    "    for example in examples:\n",
+    "        # Format the chat structure for the processor\n",
+    "        formatted_example = {\n",
+    "            \"messages\": [\n",
+    "                {\n",
+    "                    \"role\": \"system\",\n",
+    "                    \"content\": [{\"type\": \"text\", \"text\": system_message}],\n",
+    "                },\n",
+    "                {\n",
+    "                    \"role\": \"user\",\n",
+    "                    \"content\": [\n",
+    "                        {\n",
+    "                            \"type\": \"image\",\n",
+    "                        },\n",
+    "                        {\n",
+    "                            \"type\": \"text\",\n",
+    "                            \"text\": example[\"query\"],\n",
+    "                        },\n",
+    "                    ],\n",
+    "                },\n",
+    "            ]\n",
+    "        }\n",
+    "        # Apply chat template and strip extra spaces\n",
+    "        text_inputs.append(processor.apply_chat_template(formatted_example[\"messages\"], tokenize=False).strip())\n",
+    "        \n",
+    "        # Ensure images are in RGB mode\n",
+    "        image = example[\"image\"]\n",
+    "        if image.mode != 'RGB':\n",
+    "            image = image.convert('RGB')\n",
+    "        image_inputs.append( [image] )\n",
+    "\n",
+    "    # Tokenize the texts and process the images\n",
+    "    batch = processor(\n",
+    "        text=text_inputs,\n",
+    "        images=image_inputs,\n",
+    "        return_tensors=\"pt\",\n",
+    "        padding=True\n",
+    "    )\n",
+    "\n",
+    "    # Clone input IDs for labels\n",
+    "    labels = batch[\"input_ids\"].clone()\n",
+    "    labels[labels == processor.tokenizer.pad_token_id] = -100  # Mask padding tokens in labels\n",
+    "\n",
+    "    # Ensure image_token is converted to string if it is an AddedToken\n",
+    "    # In some processor, processor.image_token return a list for each image.\n",
+    "    # TODO: AutoProcessor.from_pretrained(\"HuggingFaceTB/SmolVLM-Instruct\") only have one ?\n",
+    "    image_token_id = processor.tokenizer.convert_tokens_to_ids(str(processor.image_token))\n",
+    "\n",
+    "    # Mask image token IDs in the labels\n",
+    "    labels[labels == image_token_id] = -100\n",
+    "\n",
+    "    # Add labels back to the batch\n",
+    "    batch[\"labels\"] = labels\n",
+    "\n",
+    "\n",
+    "    return batch\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from trl import SFTConfig, SFTTrainer\n",
+    "\n",
+    "# Configure the Trainer\n",
+    "training_args = SFTConfig(\n",
+    "    output_dir=\"sft_output\",  # Directory to save the model\n",
+    "    num_train_epochs=3,                     # number of training epochs\n",
+    "    per_device_train_batch_size=1,          # batch size per device during training\n",
+    "    gradient_accumulation_steps=16,         # number of steps before performing a backward/update pass\n",
+    "    gradient_checkpointing=True,            # use gradient checkpointing to save memory\n",
+    "    optim=\"adamw_torch_fused\",              # use fused adamw optimizer\n",
+    "    logging_steps=5,                        # log every 10 steps\n",
+    "    save_strategy=\"epoch\",                  # save checkpoint every epoch\n",
+    "    learning_rate=2e-4,                     # learning rate, based on QLoRA paper\n",
+    "    bf16=True,                              # use bfloat16 precision\n",
+    "    tf32=True,                              # use tf32 precision\n",
+    "    max_grad_norm=0.3,                      # max gradient norm based on QLoRA paper\n",
+    "    warmup_ratio=0.03,                      # warmup ratio based on QLoRA paper\n",
+    "    lr_scheduler_type=\"constant\",           # use constant learning rate scheduler\n",
+    "    push_to_hub=True,                       # push model to hub\n",
+    "    gradient_checkpointing_kwargs = {\"use_reentrant\": False}, # use reentrant checkpointing\n",
+    "    # dataloader_num_workers=16, \n",
+    "    dataset_text_field=\"\", # need a dummy field for collator\n",
+    "    dataset_kwargs = {\"skip_prepare_dataset\": True}, # important for collator\n",
+    "    remove_unused_columns = False                    # necessary else features except label will be removed\n",
+    ")\n",
+    "# Initialize the Trainer\n",
+    "trainer = SFTTrainer(\n",
+    "    model=model,\n",
+    "    args=training_args,\n",
+    "    train_dataset=ds[\"train\"],\n",
+    "    eval_dataset=ds[\"test\"],\n",
+    "    data_collator=collate_fn,\n",
+    "    peft_config=peft_config,\n",
+    "    tokenizer=processor.tokenizer,\n",
+    ")\n",
+    "\n",
+    "# TODO: 🦁 🐕 align the SFTTrainer params with your chosen dataset. For example, if you are using the `bigcode/the-stack-smol` dataset, you will need to choose the `content` column`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 训练模型\n",
+    "\n",
+    "训练器配置完成后，我们现在就可以着手训练模型了。训练过程将包括对数据集进行迭代，计算损失值，并更新模型参数以最小化该损失。 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b4e2897ddbc47fa90254bcf12ce92f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5304 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "trainer.train()\n",
+    "\n",
+    "# Save the model\n",
+    "trainer.save_model(f\"./{finetune_name}\")\n",
+    "\n",
+    "# Save to the huggingface hub if login (HF_TOKEN is set)\n",
+    "if os.getenv(\"HF_TOKEN\"):\n",
+    "    trainer.push_to_hub(tags=finetune_tags)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💐 完成了！\n",
+    "\n",
+    "本文提供了一份使用 `SFTTrainer` 对 `HuggingFaceTB/SmolVLM` 模型进行微调的分步指南。按照这些步骤操作，你可以让模型更有效地执行特定任务。如果你想继续学习本课程，不妨尝试以下步骤：\n",
+    "\n",
+    "- 尝试更高难度等级的练习题。\n",
+    "- 对 GitHub 上的 PRs 进行review。\n",
+    "- 通过 GitHub 提出 issue 或 PR，为完善本课程材料贡献力量。\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py310",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/zh-cn/5_vision_language_models/notebooks/vlm_usage_sample.ipynb b/zh-cn/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
new file mode 100644
index 00000000..7554d217
--- /dev/null
+++ b/zh-cn/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
@@ -0,0 +1,473 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 用 VLM 处理图片或视频相关的多模态任务\n",
+    "\n",
+    "本文以 `HuggingFaceTB/SmolVLM-Instruct` 的 4bit 量化模型为例，展示如何处理以下多模态任务：\n",
+    "- 视觉问题回答（VQA）：基于图片内容回答问题\n",
+    "- 文本识别（OCR）：提取并识别图片中的文本文字\n",
+    "- 视频理解：通过分析一系列的视频帧，来生成对视频的描述\n",
+    "\n",
+    "通过有效地组织提示语（文本 + 视觉），你可以将该模型应用于诸多领域，如场景理解、文档分析以及动态视觉推理。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install the requirements in Google Colab\n",
+    "# !pip install transformers datasets trl huggingface_hub bitsandbytes\n",
+    "\n",
+    "# Authenticate to Hugging Face\n",
+    "from huggingface_hub import notebook_login\n",
+    "notebook_login()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`low_cpu_mem_usage` was None, now default to True since model is quantized.\n",
+      "You shouldn't move a model that is dispatched using accelerate hooks.\n",
+      "Some kwargs in processor config are unused and will not have any effect: image_seq_len. \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'longest_edge': 1536}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch, PIL\n",
+    "from transformers import AutoProcessor, AutoModelForVision2Seq, BitsAndBytesConfig\n",
+    "from transformers.image_utils import load_image\n",
+    "\n",
+    "device = (\n",
+    "    \"cuda\"\n",
+    "    if torch.cuda.is_available()\n",
+    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
+    ")\n",
+    "\n",
+    "quantization_config = BitsAndBytesConfig(load_in_4bit=True)\n",
+    "model_name = \"HuggingFaceTB/SmolVLM-Instruct\"\n",
+    "model = AutoModelForVision2Seq.from_pretrained(\n",
+    "    model_name,\n",
+    "    quantization_config=quantization_config,\n",
+    ").to(device)\n",
+    "processor = AutoProcessor.from_pretrained(\"HuggingFaceTB/SmolVLM-Instruct\")\n",
+    "\n",
+    "print(processor.image_processor.size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 图像级输入的情况"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "我们先探索生成图片的文本描述，以及针对图片提问的情况。后面我们也会探索多图输入的情况。\n",
+    "\n",
+    "### 1. 单副图片的文本描述生成"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"https://cdn.pixabay.com/photo/2024/11/20/09/14/christmas-9210799_1280.jpg\"/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"https://cdn.pixabay.com/photo/2024/11/23/08/18/christmas-9218404_1280.jpg\"/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image, display\n",
+    "\n",
+    "image_url1 = \"https://cdn.pixabay.com/photo/2024/11/20/09/14/christmas-9210799_1280.jpg\"\n",
+    "display(Image(url=image_url1))\n",
+    "\n",
+    "image_url2 = \"https://cdn.pixabay.com/photo/2024/11/23/08/18/christmas-9218404_1280.jpg\"\n",
+    "display(Image(url=image_url2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/duydl/Miniconda3/envs/py310/lib/python3.10/site-packages/bitsandbytes/nn/modules.py:451: UserWarning: Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference or training speed.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['User:<image>Can you describe the image?\\nAssistant: The image is a scene of a person walking in a forest. The person is wearing a coat and a cap. The person is holding the hand of another person. The person is walking on a path. The path is covered with dry leaves. The background of the image is a forest with trees.']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load  one image\n",
+    "image1 = load_image(image_url1)\n",
+    "\n",
+    "# Create input messages\n",
+    "messages = [\n",
+    "    {\n",
+    "        \"role\": \"user\",\n",
+    "        \"content\": [\n",
+    "            {\"type\": \"image\"},\n",
+    "            {\"type\": \"text\", \"text\": \"Can you describe the image?\"}\n",
+    "        ]\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "# Prepare inputs\n",
+    "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
+    "inputs = processor(text=prompt, images=[image1], return_tensors=\"pt\")\n",
+    "inputs = inputs.to(device)\n",
+    "\n",
+    "# Generate outputs\n",
+    "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
+    "generated_texts = processor.batch_decode(\n",
+    "    generated_ids,\n",
+    "    skip_special_tokens=True,\n",
+    ")\n",
+    "\n",
+    "print(generated_texts)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. 对比多幅图片\n",
+    "模型也可以处理并对比多幅图片。我们接下来让模型总结一下两张图片的共同主题。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['User:<image>What event do they both represent?\\nAssistant: Christmas.']\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Load images\n",
+    "image2 = load_image(image_url2)\n",
+    "\n",
+    "# Create input messages\n",
+    "messages = [\n",
+    "    # {\n",
+    "    #     \"role\": \"user\",\n",
+    "    #     \"content\": [\n",
+    "    #         {\"type\": \"image\"},\n",
+    "    #         {\"type\": \"image\"},\n",
+    "    #         {\"type\": \"text\", \"text\": \"Can you describe the two images?\"}\n",
+    "    #     ]\n",
+    "    # },\n",
+    "    {\n",
+    "        \"role\": \"user\",\n",
+    "        \"content\": [\n",
+    "            {\"type\": \"image\"},\n",
+    "            {\"type\": \"image\"},\n",
+    "            {\"type\": \"text\", \"text\": \"What event do they both represent?\"}\n",
+    "        ]\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "# Prepare inputs\n",
+    "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
+    "inputs = processor(text=prompt, images=[image1, image2], return_tensors=\"pt\")\n",
+    "inputs = inputs.to(device)\n",
+    "\n",
+    "# Generate outputs\n",
+    "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
+    "generated_texts = processor.batch_decode(\n",
+    "    generated_ids,\n",
+    "    skip_special_tokens=True,\n",
+    ")\n",
+    "\n",
+    "print(generated_texts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 🔠 文字识别（OCR）\n",
+    "\n",
+    "VLM 也可以识别并提取文字，这使得它也可以用来处理诸如文档分析之类的任务。\n",
+    "你也可以试试使用文字更密集的图片。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"https://cdn.pixabay.com/photo/2020/11/30/19/23/christmas-5792015_960_720.png\"/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['User:<image>What is written?\\nAssistant: MERRY CHRISTMAS AND A HAPPY NEW YEAR']\n"
+     ]
+    }
+   ],
+   "source": [
+    "document_image_url = \"https://cdn.pixabay.com/photo/2020/11/30/19/23/christmas-5792015_960_720.png\"\n",
+    "display(Image(url=document_image_url))\n",
+    "\n",
+    "# Load the document image\n",
+    "document_image = load_image(document_image_url)\n",
+    "\n",
+    "# Create input message for analysis\n",
+    "messages = [\n",
+    "    {\n",
+    "        \"role\": \"user\",\n",
+    "        \"content\": [\n",
+    "            {\"type\": \"image\"},\n",
+    "            {\"type\": \"text\", \"text\": \"What is written?\"}\n",
+    "        ]\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "# Prepare inputs\n",
+    "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
+    "inputs = processor(text=prompt, images=[document_image], return_tensors=\"pt\")\n",
+    "inputs = inputs.to(device)\n",
+    "\n",
+    "# Generate outputs\n",
+    "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
+    "generated_texts = processor.batch_decode(\n",
+    "    generated_ids,\n",
+    "    skip_special_tokens=True,\n",
+    ")\n",
+    "\n",
+    "print(generated_texts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 视频级输入的情况\n",
+    "\n",
+    "视觉语言模型（VLMs）可以通过提取关键帧并按时间顺序对其进行推理，从而间接处理视频。虽然视觉语言模型缺乏专用视频模型的时间建模能力，但它们仍然能够：\n",
+    "\n",
+    "- 通过按顺序分析采样帧来描述动作或事件。\n",
+    "- 根据具有代表性的关键帧回答有关视频的问题。\n",
+    "- 通过组合多个帧的文本描述来总结视频内容。\n",
+    "\n",
+    "我们用这个视频作为例子：\n",
+    "\n",
+    "<video width=\"640\" height=\"360\" controls>\n",
+    "  <source src=\"https://cdn.pixabay.com/video/2023/10/28/186794-879050032_large.mp4\" type=\"video/mp4\">\n",
+    "  Your browser does not support the video tag.\n",
+    "</video>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install opencv-python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import Video\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "\n",
+    "def extract_frames(video_path, max_frames=50, target_size=None):\n",
+    "    cap = cv2.VideoCapture(video_path)\n",
+    "    if not cap.isOpened():\n",
+    "        raise ValueError(f\"Could not open video: {video_path}\")\n",
+    "    \n",
+    "    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
+    "    frame_indices = np.linspace(0, total_frames - 1, max_frames, dtype=int)\n",
+    "\n",
+    "    frames = []\n",
+    "    for idx in frame_indices:\n",
+    "        cap.set(cv2.CAP_PROP_POS_FRAMES, idx)\n",
+    "        ret, frame = cap.read()\n",
+    "        if ret:\n",
+    "            frame = PIL.Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n",
+    "            if target_size:\n",
+    "                frames.append(resize_and_crop(frame, target_size))\n",
+    "            else:\n",
+    "                frames.append(frame)\n",
+    "    cap.release()\n",
+    "    return frames\n",
+    "\n",
+    "def resize_and_crop(image, target_size):\n",
+    "    width, height = image.size\n",
+    "    scale = target_size / min(width, height)\n",
+    "    image = image.resize((int(width * scale), int(height * scale)), PIL.Image.Resampling.LANCZOS)\n",
+    "    left = (image.width - target_size) // 2\n",
+    "    top = (image.height - target_size) // 2\n",
+    "    return image.crop((left, top, left + target_size, top + target_size))\n",
+    "\n",
+    "# Video link\n",
+    "video_link = \"https://cdn.pixabay.com/video/2023/10/28/186794-879050032_large.mp4\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Response: User: Following are the frames of a video in temporal order.<image>Describe what the woman is doing.\n",
+      "Assistant: The woman is hanging an ornament on a Christmas tree.\n"
+     ]
+    }
+   ],
+   "source": [
+    "question = \"Describe what the woman is doing.\"\n",
+    "\n",
+    "def generate_response(model, processor, frames, question):\n",
+    "\n",
+    "    image_tokens = [{\"type\": \"image\"} for _ in frames]\n",
+    "    messages = [\n",
+    "        {\n",
+    "            \"role\": \"user\",\n",
+    "            \"content\": [{\"type\": \"text\", \"text\": \"Following are the frames of a video in temporal order.\"}, *image_tokens, {\"type\": \"text\", \"text\": question}]\n",
+    "        }\n",
+    "    ]\n",
+    "    inputs = processor(\n",
+    "        text=processor.apply_chat_template(messages, add_generation_prompt=True),\n",
+    "        images=frames,\n",
+    "        return_tensors=\"pt\"\n",
+    "    ).to(model.device)\n",
+    "\n",
+    "    outputs = model.generate(\n",
+    "        **inputs, max_new_tokens=100, num_beams=5, temperature=0.7, do_sample=True, use_cache=True\n",
+    "    )\n",
+    "    return processor.decode(outputs[0], skip_special_tokens=True)\n",
+    "\n",
+    "\n",
+    "# Extract frames from the video\n",
+    "frames = extract_frames(video_link, max_frames=15, target_size=384)\n",
+    "\n",
+    "processor.image_processor.size = (384, 384)\n",
+    "processor.image_processor.do_resize = False\n",
+    "# Generate response\n",
+    "response = generate_response(model, processor, frames, question)\n",
+    "\n",
+    "# Display the result\n",
+    "# print(\"Question:\", question)\n",
+    "print(\"Response:\", response)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💐 完成了！\n",
+    "\n",
+    "本文展示了使用视觉语言模型的多个案例，按照文中的步骤操作，你可以对视觉语言模型展开很多试验，探索其应用场景。\n",
+    "\n",
+    "后续你还可以：\n",
+    "\n",
+    "- 尝试视觉语言模型（VLMs）更多的用例。\n",
+    "- 对 GitHub 上的 PRs 进行review。\n",
+    "- 通过 GitHub 提出 issue 或 PR，为完善本课程材料贡献力量。\n",
+    "\n",
+    "祝你学习愉快！ 🌟"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py310",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/zh-cn/5_vision_language_models/vlm_finetuning.md b/zh-cn/5_vision_language_models/vlm_finetuning.md
new file mode 100644
index 00000000..45ea6cd6
--- /dev/null
+++ b/zh-cn/5_vision_language_models/vlm_finetuning.md
@@ -0,0 +1,102 @@
+# VLM 微调
+
+## 高效微调
+
+### 量化
+
+量化可降低模型权重与激活值的精度，大幅减少内存使用并加快计算速度。例如，从 `float32` 转换为 `bfloat16`，每个参数的内存需求减半，同时还能维持性能。若要进行更激进的压缩，可采用 8 位和 4 位量化，进一步降低内存使用，不过这会牺牲一定的准确性。这些技术既能应用于模型，也能用于优化器设置，从而使在资源有限的硬件上高效训练成为可能。
+
+### PEFT & LoRA
+
+专注于学习紧凑的秩分解矩阵，同时保持原始模型的权重不变。这极大地减少了可训练参数的数量，显著降低了资源需求。当 LoRA 与 PEFT 相结合时，仅通过调整一小部分可训练参数就能对大型模型进行微调。这种方法对于特定任务的微调特别有效，它能将数十亿个可训练参数减少到仅数百万个，同时维持性能。
+
+### Batch Size 的优化
+
+为优化微调时的 batch size 大小，可先从较大的值开始，如果出现内存不足（OOM）错误，则将其减小。可通过增加 `gradient_accumulation_steps` 来补偿，从而在多次更新过程中有效地维持总批量大小。此外，启用 `gradient_checkpointing` 可通过在反向传播期间重新计算中间状态来降低内存使用，以计算时间为代价来减少激活内存需求。这些策略可使硬件利用率最大化，并有助于克服内存限制。
+
+```python
+from transformers import TrainingArguments
+
+training_args = TrainingArguments(
+    output_dir="./fine_tuned_model",  # Directory for model checkpoints
+    per_device_train_batch_size=4,   # Batch size per device (GPU/TPU)
+    num_train_epochs=3,              # Total training epochs
+    learning_rate=5e-5,              # Learning rate
+    save_steps=1000,                 # Save checkpoint every 1000 steps
+    bf16=True,                       # Use mixed precision for training
+    gradient_checkpointing=True,     # Enable to reduce activation memory usage
+    gradient_accumulation_steps=16,  # Accumulate gradients over 16 steps
+    logging_steps=50                 # Log metrics every 50 steps
+)
+```
+
+## **有监督微调（SFT）**
+
+有监督微调（Supervised Fine-tuning 或 SFT）通过利用包含配对输入（例如图像和相应文本）的标注数据集，使预训练的视觉语言模型（VLM）适应特定任务。这种方法可增强模型执行特定领域或特定任务功能的能力，如视觉问答（VQA）、图像文本描述生成、图表解析。
+
+### **简介**
+
+当你需要视觉语言模型（VLM）专门从事某个特定领域，或解决基础模型无法解决的问题时，有监督微调（SFT）就显得至关重要。例如，如果该模型在处理特殊的视觉特征或特定领域的术语方面表现不佳，有监督微调可让它从标注数据中学习，获得处理这些领域的任务的能力。
+
+While SFT is highly effective, it has notable limitations:
+虽然 SFT 非常高效，但它也有很多明显的局限性：
+- **数据依赖**：针对特定任务的高质量标注数据是必须要有的
+- **计算资源**：对大型的 VLM 进行微调需耗费大量计算资源
+- **过拟合的风险**：如果微调范围过窄，模型可能会失去其泛化能力
+
+尽管存在这些挑战，有监督微调（SFT）仍是一种强大的技术，可在特定场景中提升模型性能。
+
+### **实践方法**
+
+1. **数据准备**：从一个将图像与文本（如问题和答案）进行配对的标注数据集入手。例如，在图表分析这类任务中，“HuggingFaceM4/ChartQA” 数据集包含图表图像、查询内容以及简洁的回答。
+2. **模型设置**：加载适合该任务的预训练视觉语言模型（VLM），比如 `HuggingFaceTB/SmolVLM - Instruct`，以及用于处理文本和图像输入的模型。针对监督学习并结合硬件条件对模型进行配置。
+3. **微调过程**：
+  - **数据格式化**：将数据集构建成类似聊天机器人的格式，把系统消息、用户查询以及相应答案进行配对。
+  - **训练配置**：使用诸如 Hugging Face 的 `TrainingArguments` 或 TRL 的 `SFTConfig` 等工具来设置训练参数。这些参数包括 batch size、学习率和梯度累积步数，以优化资源使用。
+  - **优化技术**：在训练过程中使用 **gradient checkpointing** 来节省内存。采用量化模型降低内存需求并加快计算速度。
+  - 利用 TRL 库中的 `SFTTrainer` 训练器来简化训练流程。
+
+## 偏好优化
+
+偏好优化，尤其是直接偏好优化（DPO），旨在训练视觉语言模型（VLM）使其与人类偏好保持一致。该模型并非严格遵循预先设定的指令，而是学习对人类主观上更青睐的输出结果作为优先选择。这种方法在涉及创造性判断、精细化推理或存在多种可接受答案的任务中尤为实用。
+
+### **简介**
+
+偏好优化适用于那些人类主观偏好对任务成功起着关键作用的场景。通过在反映人类偏好的数据集上进行微调，直接偏好优化（DPO）提升了模型生成在上下文和风格上都符合用户期望的回复的能力。这种方法在创意写作、客户互动或多项选择等场景的任务中特别有效。
+
+尽管偏好优化有诸多益处，但它也面临一些挑战：
+
+- **数据质量**：需要高质量且带有偏好注释的数据集，这往往使数据收集成为瓶颈。
+- **复杂性**：训练可能涉及复杂的过程，比如对偏好进行成对采样以及平衡计算资源。
+
+偏好数据集必须清晰体现候选输出之间的偏好差异。例如，一个数据集可能会将一个问题与两个回复配对，一个是受偏好的，另一个则不太能被接受。模型会学习预测出那个受偏好的回复，即便它并非完全正确，只要它更符合人类的判断即可。
+
+### **实践方法**
+1. **数据准备**  
+   准备好一个带有偏好注释的数据集对训练至关重要。每个数据样本一般包含一个问题和一副图片，以及两个候选回答：一个是受偏好的，另一个则不太能被接受。比如：
+   - **问题**: How many families?  
+     - **不倾向的回答**: The image does not provide any information about families.  
+     - **倾向的回答**: The image shows a Union Organization table setup with 18,000 families.  
+
+   该数据集引导模型优先选择偏好的回答，即便这些回答并非尽善尽美。
+
+2. **模型配置**  
+   加载一个预训练的视觉语言模型，准备好处理文本和图像输入的模型，结合 Hugging Face 的 `TRL` 库（支持 DPO 算法）写训练程序。针对监督学习进行模型配置，并使其适配你的硬件。
+
+3. **训练流程** 
+   训练需要配置 DPO 相关参数，主要有这些方向需要注意：
+
+   - **数据集格式**：对每个样本进行结构化，整理好问题、图片、候选回答。
+   - **损失函数**：需要使用和偏向性相关的函数，以此训练模型去选择倾向的回答。
+   - **高效训练**：结合量化、梯度累积、LoRA 等技术，减少显存使用和计算量。
+
+## 学习资源
+
+- [Hugging Face Learn: Supervised Fine-Tuning VLMs](https://huggingface.co/learn/cookbook/fine_tuning_vlm_trl) 
+- [Hugging Face Learn: Supervised Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_smol_vlm_sft_trl)  
+- [Hugging Face Learn: Preference Optimization Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_vlm_dpo_smolvlm_instruct)  
+- [Hugging Face Blog: Preference Optimization for VLMs](https://huggingface.co/blog/dpo_vlm)
+
+## 接下来
+
+⏩ 学习 [vlm_finetune_sample_cn.ipynb](./notebooks/vlm_finetune_sample.ipynb)，动手实现 VLM 的偏好对齐训练。
\ No newline at end of file
diff --git a/zh-cn/5_vision_language_models/vlm_usage.md b/zh-cn/5_vision_language_models/vlm_usage.md
new file mode 100644
index 00000000..e35e1091
--- /dev/null
+++ b/zh-cn/5_vision_language_models/vlm_usage.md
@@ -0,0 +1,80 @@
+# 视觉语言模型
+
+视觉语言模型（VLMs）弥合了图像与文本之间的鸿沟，使得诸如生成图像的文字描述、基于视觉内容回答问题，以及理解文本与视觉数据之间的关系等高级任务得以实现。其架构旨在无缝处理这两种模态的数据。
+
+### 模型架构
+
+视觉语言模型将图像处理组件与文本生成模型相结合，以实现统一的理解。其架构的主要组成部分包括：
+
+![VLM Architecture](./images/VLM_Architecture.png)
+
+- **图像编码器（Image Encoder）**：用来将图片转化为用数值表示的特征，通常使用经训练过的 CLIP 或 ViT。
+- **特征映射模块（Embedding Projector）**：用来将图片特征映射到文本特征空间，使其与文本特征兼容，通常使用全联接层或线性变换实现。
+- **文本解码器（Text Decoder）**：作为文本生成模块，将融合的多模态信息转换成连贯的文字。举例来说，这部分可以用 Llama、Vicuna 等生成模型实现。
+- **多模态映射模块（Multimodal Projector）**：: 提供了一个额外的层，来融合图像与文本的表征。对于像 LLaVA 这样的模型而言，这一层对于在两种模态之间建立更紧密的联系至关重要。
+
+大多数视觉语言模型（VLMs）会利用预训练的图像编码器和文本解码器，并通过在图像-文本这样的成对数据上进行微调，使二者的特征相互对齐。这种方法既能提高训练效率，又能让模型有效地实现泛化。
+
+### 使用方法
+
+![VLM Process](./images/VLM_Process.png)
+
+视觉语言模型（VLMs）被应用于一系列多模态任务。凭借其适应性，只需进行不同程度的微调，就能在多个领域发挥作用：
+
+- **图像字幕生成**：为图像生成描述性文字。
+- **视觉问答（VQA）**：针对图像内容回答相关问题。
+- **跨模态检索**：为给定图像找到对应的文本，或反之，为给定文本找到对应的图像。
+- **创意应用**：辅助设计、艺术创作，或生成引人入胜的多媒体内容。
+
+
+
+![VLM Usage](./images/VLM_Usage.png)
+
+训练和微调视觉语言模型（VLMs）依赖于高质量的数据集，这些数据集将图像与文本注释进行配对。像 Hugging Face 的 `transformers` 库这样的工具，为获取预训练的视觉语言模型提供了便捷途径，同时也简化了自定义微调的工作流程。
+
+### Chat Format
+
+很多视觉语言模型也是通过像聊天机器人一样的方式与用户互动的。这种格式包含以下信息：
+
+
+
+- **系统信息（system message）**：为模型设定角色、上下文，例如“你是一个小助理，将会帮我分析视觉数据”这样。
+- **用户查询（User queries）**：用户询问模型时输入给模型的信息，这包括视觉信息和文本信息
+- **模型回答（Assistant responses）**：模型给出的文本形式的回复
+
+这种对话结构直观易懂，符合用户预期，尤其适用于诸如客服或教育工具这类交互式应用程序。
+
+下面就是一个格式化的输入示例：
+
+```json
+[
+    {
+        "role": "system",
+        "content": [{"type": "text", "text": "You are a Vision Language Model specialized in interpreting visual data from chart images..."}]
+    },
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": "<image_data>"},
+            {"type": "text", "text": "What is the highest value in the bar chart?"}
+        ]
+    },
+    {
+        "role": "assistant",
+        "content": [{"type": "text", "text": "42"}]
+    }
+]
+```
+
+**多图输入或视频输入**
+
+视觉语言模型（VLMs）还能够处理多张图像，甚至是视频。方法是调整输入结构，以适应连续或并行的视觉输入。对于视频，可以提取多帧并将其作为图像进行处理，同时保持时间顺序。
+
+## 学习资源
+
+- [Hugging Face Blog: Vision Language Models](https://huggingface.co/blog/vlms)
+- [Hugging Face Blog: SmolVLM](https://huggingface.co/blog/smolvlm) 
+
+## 接下来
+
+⏩ 你可以尝试 [vlm_usage_sample_cn.ipynb](./notebooks/vlm_usage_sample.ipynb)，了解如何使用 SmolVLM 来处理各种任务。
\ No newline at end of file
diff --git a/zh-cn/6_synthetic_datasets/README.md b/zh-cn/6_synthetic_datasets/README.md
new file mode 100644
index 00000000..4a760d18
--- /dev/null
+++ b/zh-cn/6_synthetic_datasets/README.md
@@ -0,0 +1,39 @@
+# 合成数据集
+
+合成数据是人工生成的数据，用于模拟现实世界中的真实情况。它能够通过扩充或强化数据集，克服数据方面的局限。尽管合成数据此前已在某些应用场景中有所运用，但大语言模型使得合成数据集在语言模型的预训练、后训练以及评估过程中变得更为流行。
+
+我们将使用 [`distilabel`](https://distilabel.argilla.io/latest/)，这是一个为工程师提供合成数据和人工智能反馈的框架。如果需要基于经验证的研究论文来构建快速、可靠且可扩展的流程，该框架十分适用。如果你想深入了解该框架并学习最佳实践，你可以阅读[官方文档](https://distilabel.argilla.io/latest/)。
+
+## 章节概览
+
+语言模型领域的合成数据可以分为三类：指令（instructions）、偏好（preferences）和评论（critiques）。我们将重点关注前两类，因为它们涉及为指令微调和偏好对齐生成人工数据。在学习这两类的同时，我们也会涉及第三类某些方面的内容，因为这关乎借助模型评论和重写，来提升现有数据的质量。
+
+![Synthetic Data Taxonomies](./images/taxonomy-synthetic-data.png)
+
+## 内容目录
+
+### 1. [指令数据集](./instruction_datasets_cn.md)
+
+你将学习如何为指令微调（instruction tuning）生成指令数据集，这包括基本的提示语方式以及最近论文提出的更精细的方式。这里面，通过上下文学习并借助种子数据来生成指令微调数据集的方法包括 SelfInstruct 和 Magpie；进化改进指令微调数据的方法包括 EvolInstruct。你可以点击[这里](./instruction_datasets_cn.md)开始学习。
+
+### 2. [偏好数据集](./preference_datasets_cn.md)
+
+你讲学习如何为偏好对齐（preference alignment）去生成偏好数据集。我们将在上一节的基础上，额外生成别的回答。接下来，我们还会学习如何通过 EvolQuality 来优化这些回答。最终，我们会学习如何用 UltraFeedback 生成分数和评论，来评估这些回答，以此来获取偏好数据对。你可以点击[这里](./preference_datasets_cn.md)开始学习。
+
+### Exercise Notebooks
+
+| 标题 | 概述 | 练习 | 链接 | Colab |
+|-------|-------------|----------|------|-------|
+| 指令微调数据集 | 为指令微调生成数据集 | 🐢 为指令微调生成数据集 <br> 🐕 借助种子数据为指令微调生成数据集 <br> 🦁 借助种子数据和 EvolInstruct 为指令微调生成数据集 | [Link](./notebooks/instruction_sft_dataset_cn.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb) |
+| 偏好数据集 | 为偏好对齐生成数据集 | 🐢 为偏好对齐生成数据集 <br> 🐕 为偏好对齐生成数据集，对回答进行迭代优化 <br> 🦁 为偏好对齐生成数据集，对回答进行迭代优化，并借助对回答的评价 | [Link](./notebooks/preference_alignment_dataset_cn.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/6_synthetic_datasets/notebooks/preference_alignment_dataset.ipynb) |
+
+## Resources
+
+- [Distilabel 官方文档](https://distilabel.argilla.io/latest/)
+- [Synthetic Data Generator is UI app](https://huggingface.co/blog/synthetic-data-generator)
+- [SmolTalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk)
+- [Self-instruct 论文](https://arxiv.org/abs/2212.10560)
+- [Evol-Instruct 论文](https://arxiv.org/abs/2304.12244)
+- [Magpie 论文](https://arxiv.org/abs/2406.08464)
+- [UltraFeedback 论文](https://arxiv.org/abs/2310.01377)
+- [Deita 论文](https://arxiv.org/abs/2312.15685)
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literal 0
HcmV?d00001

diff --git a/zh-cn/6_synthetic_datasets/instruction_datasets.md b/zh-cn/6_synthetic_datasets/instruction_datasets.md
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+# 合成指令微调数据集
+
+在[指令微调](../1_instruction_tuning/README_CN.md)这一章节，我们学习了通过 SFT 的方法来微调模型。在本文中，我们将学习如何为 SFT 合成指令数据集，这涵盖：使用基本的提示语还生成指令微调数据集，以及使用一些论文中提出的更精细的方法。我们还可以使用种子数据进行上下文学习（in-context learning）来合成数据，相关的方法包括 SelfInstruct 和 Magpie。此外我们还会学习使用 EvolInstruct 来改进现有数据。最后，我们将学习如何使用 `distilabel` 的 `pipeline` 来规范化地进行指令微调数据集的生成。
+
+## 从提示语到数据
+
+合成数据听起来很高级，但它可以简单理解为通过有效的提示语从模型中提取知识来创建数据。反过来，你可以将此视为为特定任务生成数据的一种方式。挑战在于要进行有效提示，同时确保数据具有多样性和代表性。幸运的是，已有许多论文探讨了这个问题，在本文中，我们将研究其中一些有价值的论文。首先，我们将探索如何通过手动地提供提示语来生成合成数据。
+
+### 基本的提示语方式
+
+我们用一个基础的例子开始合成数据的学习。这里我们使用 [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) 模型，并使用 `distilabel` 中已经集成的 `transformers` 库来载入这个模型。在示例中，我们用 `TextGeneration` 类来生成一个 `prompt` 并基于此再生成 `completion`，这样一个问答就生成完毕了。
+
+首先，通过 `distilabel` 库来加载模型：
+
+```python
+from distilabel.llms import TransformersLLM
+from distilabel.steps.tasks import TextGeneration
+
+llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
+gen = TextGeneration(llm=llm)
+gen.load()
+```
+
+!!! 注意
+    `distilabel` 会把 `llm` 加载进内存。当我们在一个 notebook 中跑该代码时，我们还需要在结束时执行 `gen.unload()` 来防止内存持续占用。
+
+接下来，我们开始用 `llm` 来生成一个 `prompt` 作为问题。
+
+```python
+next(gen.process([{"instruction": "Generate a questions about the Hugging Face Smol-Course on small AI models."}]))
+# What is the purpose of Smol-Course?
+```
+
+最后，用 `prompt` 作为问题输入，来生成一个 `completion` 作为回答。
+
+```python
+next(gen.process([{"instruction": "What is the purpose of Smol-Course?"}]))
+# The Smol-Course is a platform designed to learning computer science concepts.
+```
+
+这样我们就得到了一个 `prompt` 和一个对应的 `completion`。重复这一简单步骤，就可以得到更多的类似数据。然而，这些数据的质量并不高，且没有考虑到不同任务领域的微妙差别。此外，重复这个过程使得我们的数据集缺乏多样性。不过，我们有办法解决这个问题。
+
+### SelfInstruct
+
+SelfInstruct 基于一个种子数据集生成新的指令数据。一条种子数据可以是一个单一的指令或者一段上下文。该过程通常从一系列初始的种子数据开始。然后语言模型被要求基于种子数据进行上下文学习，针对上下文提出新的问题。在 [distilabel 中的模板](https://github.com/argilla-io/distilabel/blob/main/src/distilabel/steps/tasks/templates/self-instruct.jinja2) 中包含了要求模型执行这一任务的提示语，大致是这样：
+
+```
+# Task Description
+Develop {{ num_instructions }} user queries that can be received by the given AI application and applicable to the provided context. Emphasize diversity in verbs and linguistic structures within the model's textual capabilities.
+
+# Context
+{{ input }}
+
+# Output
+```
+
+具体到代码实现上，我们首先要将 `llm` 传入 [SelfInstruct](https://distilabel.argilla.io/dev/components-gallery/tasks/selfinstruct/) 这个类的构造函数。这里我们假设用一段描述如何合成人工数据集的英文文字（就是代码中的`text`）作为上下文，来生成新的指令：
+
+```python
+from distilabel.steps.tasks import SelfInstruct
+
+self_instruct = SelfInstruct(llm=llm)
+self_instruct.load()
+
+text = "..."
+
+next(self_instruct.process([{"input": text}]))["instructions"][0]
+# What is the process of generating synthetic data through manual prompting?
+```
+
+这样生成的指令数据会好很多，且符合我们的实际任务领域。不过，我们还可以做得更好，方法是通过不断进化改进提示语。
+
+### EvolInstruct
+
+EvolInstruct 是一种提示语技术，它以已有的指令数据作为输入，不断改进，使得原数据进化成为更好的版本。这里，“更好”是因为我们对数据生成过程添加了新的标准和限制条件，且进行了深化、具体化、推理或复杂化处理。该过程可以重复多次，针对相同的指令数据生成不同的进化版本，获取比原始数据更好的数据。相关的系统提示语[在 distilabel 中也有实现](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/evol_instruct)，大致是这样：
+
+```
+I want you act as a Prompt Rewriter.
+Given prompt a prompt, rewrite it into a more complex version.
+Complicate the prompt based on the following criteria:
+{{ criteria }}
+
+# Prompt
+{{ input }}
+
+# Output
+```
+
+具体到代码实现上，我们首先要将 `llm` 传入 [SelfInstruct](https://distilabel.argilla.io/dev/components-gallery/tasks/evolinstruct/) 这个类的构造函数。这里我们使用在 [SelfInstruct](#selfinstruct) 这一小节生成的 `prompt` 来作为输入（就是代码中的 `text`），利用它得到一个进化过的版本。本示例中我们只进化一次：
+
+```python
+from distilabel.steps.tasks import EvolInstruct
+
+evol_instruct = EvolInstruct(llm=llm, num_evolutions=1)
+evol_instruct.load()
+
+text = "What is the process of generating synthetic data through manual prompting"
+
+next(evol_instruct.process([{"instruction": text}]))
+# What is the process of generating synthetic data through manual prompting?
+# And, how does the artificial intelligence system, GPT4, use machine learning algorithms to manipulate the input data into synthetic data?
+```
+
+经过进化，指令数据变得更复杂了，不过也丧失了一些原有数据的信息。所以我们也要知道，该方法也是一个双刃剑，我们需要对进化过的数据的质量格外关注。
+
+### Magpie
+
+Magpie 技术利用了语言模型自回归的特性以及前面学习到过的[聊天模板](../1_instruction_tuning/chat_templates_cn.md)。由于聊天模板在指令微调过程中为对话设定了固定的格式，清晰指出了话语是哪个角色说出来的（系统、用户、助手），在预测过程中，模型的输出也会遵循这个格式去走。这正是 Magpie 所利用的点。一开始，Magpie 直接输入给大语言模型一个聊天模板，但这个聊天模板只包含系统信息，结束于用户开始提问的地方（例如在 `<|im_start|>user\n` 这里结束）。然后利用大语言模型的自回归特性，模型会自己不停输出文本，直到输出了助手回答结束符那里（比如输出到 `<|im_end|>` 终止）。这样一段完整的聊天模板就生成了。这种方法可以让我们高效生成数据，并可以扩展到多轮对话情况。很多人也认为这个方法是可以让大语言模型吐出自己指令微调时的训练数据的。
+
+聊天模板因不同的大语言模型而异，这里我们可以通过下面一个简单的模板，来了解这一技术的具体做法。
+
+```bash
+# Step 1: provide the pre-query-prompt
+<|im_start|>user\n
+
+# Step 2: the language model generates the user prompt
+<|im_start|>user\n
+What is the purpose of Smol-Course?
+
+# Step 3: stop the generation
+<|im_end|>
+```
+
+具体到代码实现上，我们首先要将 `llm` 传入 [Magpie class](https://distilabel.argilla.io/dev/components-gallery/tasks/magpie/) 这个类的构造函数。
+
+```python
+from distilabel.steps.tasks import Magpie
+
+magpie = Magpie(llm=llm)
+magpie.load()
+
+next(magpie.process([{"system_prompt": "You are a helpful assistant."}]))
+# [{
+#   "role": "user",
+#   "content": "Can you provide me with a list of the top 3 universities?"
+# },
+# {
+#   "role": "assistant",
+#   "content": "The top 3 universities are: MIT, Yale, Stanford."
+# }]
+```
+
+通过上述代码，我们可以马上生成一个包含 `prompt` 和 `completion` 的数据集。但如果我们想在特定领域提升数据质量，我们还可以在 `system_prompt` 加入额外的上下文信息。这样可以使得大语言模型在生成数据时，知道用户会接下来问什么问题。具体来说，特定领域信息加在系统提示语中，多多少少可以让模型产出的数据更偏向这一领域。
+
+```
+You're an AI assistant that will help users solving math problems.
+```
+
+例如我们应该按上述方法添加系统信息的内容，而不是下面这种写法。
+
+```
+You're an AI assistant that generates math problems
+```
+
+但总的来说，在训练时，语言模型一般也较少使用包含上下文信息的 `system_prompt`，所以这个方法在自定义情形下也并不是都适用。
+
+
+### 从 Prompts 到 Pipelines
+
+上面的技术都是通过 `distilabel` 里特定的类去实现的。但为了更规范，我们还可以用  `distilabel` 里的 `Pipeline` 生成数据集。在下面的例子中，我们将使用 `Pipeline` 来为指令为题生成数据集。这个 `Pipeline` 包含 `LoadDataFromDicts` 用以载入种子数据，以及 `TextGeneration` 用以生成 `prompt` 和 `completion`。从种子数据到问题（`prompt`）再到回答（`completion`）的流程，可以用 `>>` 这个运算符串联起来。
+
+```python
+from distilabel.llms import TransformersLLM
+from distilabel.pipeline import Pipeline
+from distilabel.steps import LoadDataFromDicts
+from distilabel.steps.tasks import TextGeneration
+
+with Pipeline() as pipeline:
+    data = LoadDataFromDicts(data=[{"instruction": "Generate a short question about the Hugging Face Smol-Course."}])
+    llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
+    gen_a = TextGeneration(llm=llm, output_mappings={"generation": "instruction"})
+    gen_b = TextGeneration(llm=llm, output_mappings={"generation": "response"})
+    data >> gen_a >> gen_b
+
+if __name__ == "__main__":
+    distiset = pipeline.run(use_cache=False)
+    print(distiset["default"]["train"][0])
+# [{
+#   "instruction": "What is the purpose of Smol-Course?",
+#   "response": "The Smol-Course is a platform designed to learning computer science concepts."
+# }]
+```
+
+在 pipeline 的内部，其实也有很多好用的功能。它可以自动缓存生成结果，所以有些时候我们可以不去重新跑生成步骤。它也有容错机制，某一步生成失败了，也会继续跑。此外，pipeline 的数据生成是并行运行，所以速度也快。我们还可以用 `draw` 方法对 pipeline 进行可视化。下图就展示了数据流的走向，也展示了 `output_mappings` 是如何将当前环节的输出作为输入送给下一环节的。
+
+![Pipeline](./images/pipeline.png)
+
+## 最佳实践
+
+- 需要确保你的种子数据足够多样，来覆盖足够多的对话场景
+- 定时检查数据集，确保生成的数据足够多样、质量足够好
+- 对系统提示词进行优化，可以改善生成数据的质量
+
+## 接下来
+
+👨🏽‍💻 代码 - 通过[练习](./notebooks/instruction_sft_dataset_cn.ipynb)生成一个指令微调的数据集
+🧑‍🏫 阅读 - 学习[合成偏好数据集](./preference_datasets.md)部分
+
+## References
+
+- [Distilabel 官方文档](https://distilabel.argilla.io/latest/)
+- [Self-instruct 论文](https://arxiv.org/abs/2212.10560)
+- [Evol-Instruct 论文](https://arxiv.org/abs/2304.12244)
+- [Magpie 论文](https://arxiv.org/abs/2406.08464)
\ No newline at end of file
diff --git a/zh-cn/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb b/zh-cn/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb
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index 00000000..7a88d00a
--- /dev/null
+++ b/zh-cn/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb
@@ -0,0 +1,123 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 合成指令微调数据集\n",
+    "\n",
+    "\n",
+    "本文将介绍如何为指令微调构建人造的数据集。我们将使用 `distilabel` 这个库。\n",
+    "\n",
+    "接下来我们开始实践学习。\n",
+    "\n",
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>练习：为指令微调生成数据集\t</h2>\n",
+    "    <p>通过学习，你可以尝试为指令微调生成数据集</p>\n",
+    "    <p><b>难度等级</b></p>\n",
+    "    <p>🐢 为指令微调生成数据集</p>\n",
+    "    <p>🐕 借助种子数据为指令微调生成数据集</p>\n",
+    "    <p>🦁 借助种子数据和 EvolInstruct 为指令微调生成数据集</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "source": [
+    "## 安装依赖\n",
+    "\n",
+    "在 `transformers` 之外，你也可以安装 `vllm` 或 `hf-inference-endpoints`。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install \"distilabel[hf-transformers,outlines,instructor]\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 开始合成数据\n",
+    "\n",
+    "前面已经学过了，我们可以用 `distilabel` 的 pipeline 去执行指令微调数据集的生成。一个最小化的 pipeline 已经提供过了。你可以扩展这个 pipeline 生成更大的数据集。你可以试验一下改变模型、修改生成参数等操作如何影响合成数据集的质量。先在小规模上试验，得出结论后再扩展到大规模生成上。\n",
+    "\n",
+    "\n",
+    "\n",
+    "你可以阅读 [distilabel](https://distilabel.argilla.io/latest/components-gallery/) 相关文档学习更多处理方法。\n",
+    "\n",
+    "你可以从 Hugging Face Hub 载入种子数据集，而不是像之前提供一个字典类数据。\n",
+    "\n",
+    "```python\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "with Pipeline(...) as pipeline:\n",
+    "    ...\n",
+    "\n",
+    "if __name__ == \"__main__:\n",
+    "    dataset = load_dataset(\"my-dataset\", split=\"train\")\n",
+    "    distiset = pipeline.run(dataset=dataset)\n",
+    "```\n",
+    "\n",
+    "跑完后，你也可以将你生成的数据上传到 Hugging Face Hub。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from distilabel.llms import TransformersLLM\n",
+    "from distilabel.pipeline import Pipeline\n",
+    "from distilabel.steps import LoadDataFromDicts\n",
+    "from distilabel.steps.tasks import TextGeneration\n",
+    "\n",
+    "with Pipeline() as pipeline:\n",
+    "    data = LoadDataFromDicts(data=[{\"instruction\": \"Generate a short question about the Hugging Face Smol-Course.\"}])\n",
+    "    llm = TransformersLLM(model=\"HuggingFaceTB/SmolLM2-1.7B-Instruct\")\n",
+    "    gen_a = TextGeneration(llm=llm, output_mappings={\"generation\": \"instruction\"})\n",
+    "    gen_b = TextGeneration(llm=llm, output_mappings={\"generation\": \"response\"})\n",
+    "    data >> gen_a >> gen_b\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    distiset = pipeline.run(use_cache=False)\n",
+    "    distiset.push_to_hub(\"huggingface-smol-course-instruction-tuning-dataset\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🌯 完成\n",
+    "\n",
+    "现在你已经学会如何为指令微调生成数据集了，你可以用你学到的东西来：\n",
+    "\n",
+    "- 为指令微调生成训练数据集\n",
+    "- 为指令微调生成评测数据集\n",
+    "\n",
+    "接下来\n",
+    "\n",
+    "🧑‍🏫 阅读 - 学习[合成偏好数据集](./preference_datasets_cn.md)这一节\n",
+    "\n",
+    "🏋️‍♂️ 参考[第一章的内容](../../1_instruction_tuning_cn/README.md)，用合成数据集对一个模型进行指令微调\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/zh-cn/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb b/zh-cn/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb
new file mode 100644
index 00000000..2e84619f
--- /dev/null
+++ b/zh-cn/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb
@@ -0,0 +1,118 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 为偏好对齐生成数据集\n",
+    "\n",
+    "本文将介绍如何为偏好对齐构建人造的数据集。我们将使用 `distilabel` 这个库。\n",
+    "\n",
+    "接下来我们开始实践学习。\n",
+    "\n",
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>练习：为偏好对齐生成数据集</h2>\n",
+    "    <p>通过学习，你可以尝试为偏好对齐生成数据集</p>\n",
+    "    <p><b>难度等级</b></p>\n",
+    "    <p>🐢 为偏好对齐生成数据集</p>\n",
+    "    <p>🐕 为偏好对齐生成数据集，对回答进行迭代优化</p>\n",
+    "    <p>🦁 为偏好对齐生成数据集，对回答进行迭代优化，并使用 model pooling 技术</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "source": [
+    "## 安装依赖\n",
+    "\n",
+    "在 `transformers` 之外，你也可以安装 `vllm` 或 `hf-inference-endpoints`。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install \"distilabel[hf-transformers,outlines,instructor]\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 开始合成数据\n",
+    "\n",
+    "前面已经学过了，我们可以用 `distilabel` 的 pipeline 去执行偏好数据集的生成。一个最小化的 pipeline 已经提供过了。你可以扩展这个 pipeline 生成更大的数据集。你可以试验一下改变模型、修改生成参数等操作如何影响合成数据集的质量。先在小规模上试验，得出结论后再扩展到大规模生成上。\n",
+    "\n",
+    "你可以阅读 [distilabel](https://distilabel.argilla.io/latest/components-gallery/) 相关文档学习更多处理方法。\n",
+    "\n",
+    "你可以从 Hugging Face Hub 载入种子数据集，而不是像之前提供一个字典类数据。\n",
+    "\n",
+    "```python\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "with Pipeline(...) as pipeline:\n",
+    "    ...\n",
+    "\n",
+    "if __name__ == \"__main__:\n",
+    "    dataset = load_dataset(\"my-dataset\", split=\"train\")\n",
+    "    distiset = pipeline.run(dataset=dataset)\n",
+    "```\n",
+    "\n",
+    "跑完后，你也可以将你生成的数据上传到 Hugging Face Hub。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from distilabel.llms import TransformersLLM\n",
+    "from distilabel.pipeline import Pipeline\n",
+    "from distilabel.steps import GroupColumns, LoadDataFromDicts\n",
+    "from distilabel.steps.tasks import TextGeneration\n",
+    "\n",
+    "with Pipeline() as pipeline:\n",
+    "    data = LoadDataFromDicts(data=[{\"instruction\": \"What is synthetic data?\"}])\n",
+    "    llm_a = TransformersLLM(model=\"HuggingFaceTB/SmolLM2-1.7B-Instruct\")\n",
+    "    gen_a = TextGeneration(llm=llm_a)\n",
+    "    llm_b = TransformersLLM(model=\"Qwen/Qwen2.5-1.5B-Instruct\")\n",
+    "    gen_b = TextGeneration(llm=llm_b)\n",
+    "    group = GroupColumns(columns=[\"generation\"])\n",
+    "    data >> [gen_a, gen_b] >> group\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    distiset = pipeline.run()\n",
+    "    distiset.push_to_hub(\"huggingface-smol-course-preference-tuning-dataset\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🌯 完成\n",
+    "\n",
+    "现在你已经学会如何为指令微调生成数据集了，你可以用你学到的东西来：\n",
+    "\n",
+    "- 为偏好对齐生成训练数据集\n",
+    "- 为偏好对齐生成评测数据集\n",
+    "\n",
+    "🏋️‍♂️ 参考[第二章的内容](../../2_preference_alignment/README.md)，用合成数据集对一个模型进行偏好对齐\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/zh-cn/6_synthetic_datasets/preference_datasets.md b/zh-cn/6_synthetic_datasets/preference_datasets.md
new file mode 100644
index 00000000..a1f3a4f8
--- /dev/null
+++ b/zh-cn/6_synthetic_datasets/preference_datasets.md
@@ -0,0 +1,155 @@
+# 合成偏好数据集
+
+在[前面章节](../2_preference_alignment/README_CN.md)，我们学习了直接偏好优化（DPO）。这里我们将学习如何为 DPO 之类的偏好优化算法生成训练数据集。我们的方法将基于前面[合成指令微调数据集](./instruction_datasets_cn.md)部分。此外，我们将展示如何通过基本提示或使用 EvolQuality 来添加额外的回答，以提高回答质量。最后，我们将展示如何使用 UltraFeedback 生成评分和评论。
+
+## 生成多个回答
+
+偏好数据集需要针对一个问题（`instruction`）的多个回答（`completions`）。通过提示语让模型生成两个回答当然是可行的，但我们要确保第二个回答不要和第一个过于相似（从质量和词汇上说）。这对训练非常重要，因为模型需要通过明显的差异来区分人类偏好。这里我们还需要判断哪个回答是我们倾向的（`chosen`）、哪个是我们不倾向的（`rejected`）。在[生成分数](#生成分数)部分我们会讲解。 
+
+### Model pooling
+
+Model pooling 是一个很简单的方法：你可以使用不同的模型针对同意问题生成不同的回答。如果想进一步改进第二个回答的质量，你还可以调节不同的生成参数，如 `temperature`。通过不同的提示语模板或系统提示语，你也可以生成有多样性的回答。理论上讲，用两个不同质量的模型生成两个回答，选择质量好的那个回答就可以完成这件事情。
+
+这里我们通过 [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) 和 [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) 这两个模型进行 model pooling。针对每一个问题，我们都可以生成两个回答。同样地，我们首先用 `LoadDataFromDicts` 载入种子数据，然后用 `>>` 运算符串联 pipeline 的数据流，用 `[]` 取结果。
+
+```python
+from distilabel.llms import TransformersLLM
+from distilabel.pipeline import Pipeline
+from distilabel.steps import GroupColumns, LoadDataFromDicts
+from distilabel.steps.tasks import TextGeneration
+
+with Pipeline() as pipeline:
+    data = LoadDataFromDicts(data=[{"instruction": "What is synthetic data?"}])
+    llm_a = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
+    gen_a = TextGeneration(llm=llm_a)
+    llm_b = TransformersLLM(model="Qwen/Qwen2.5-1.5B-Instruct")
+    gen_b = TextGeneration(llm=llm_b)
+    group = GroupColumns(columns=["generation"])
+    data >> [gen_a, gen_b] >> group
+
+if __name__ == "__main__":
+    distiset = pipeline.run()
+    print(distiset["default"]["train"]["grouped_generation"][0])
+# {[
+#   'Synthetic data is artificially generated data that mimics real-world usage.',
+#   'Synthetic data refers to data that has been generated artificially.'
+# ]}
+```
+
+可以看到，我们这里获取到了两个回答。如果想提升回答的多样性，还可以在 `TextGeneration` 这一步提供 `system_prompt` 或给 `TransformersLLM` 传递生成参数。接下来我们看看怎样用 EvolQuality 进一步提升回答的质量。
+
+### EvolQuality
+
+EvolQuality 和 [EvolInstruct](./instruction_datasets_cn.md#evolinstruct) 类似，都是提示语技术。但 EvolQuality 改进的是回答的质量，而不是输入的问题。EvolQuality 会把问题和回答都作为输入，然后根据提供的标准不断修改进化回答。根据有帮助度、相关性、深度、创造性和其它细节，我们定义一个更好的回答。这个更好的回答就可以加入数据集。理论上，我们可以认为这个“更好的回答”就是我们偏好优化时倾向的回答。
+
+具体的提示语[在 distilabel 已有实现](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/evol_quality)，这里我们展示一个简化版本：
+
+```bash
+I want you act as a Response Rewriter.
+Given prompt a and a response, rewrite the response into a better version.
+Complicate the prompt based on the following criteria:
+{{ criteria }}
+
+# Prompt
+{{ input }}
+
+# Response
+{{ output }}
+
+# Improved Response
+```
+
+代码实现上，我们需要用 [EvolQuality](https://distilabel.argilla.io/dev/components-gallery/tasks/evolquality/) 这个类。这里代码只进化迭代一次。
+
+```python
+from distilabel.llms import TransformersLLM
+from distilabel.steps.tasks import EvolQuality
+
+llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
+evol_quality = EvolQuality(llm=llm, num_evolutions=1)
+evol_quality.load()
+
+instruction = "What is synthetic data?"
+completion = "Synthetic data is artificially generated data that mimics real-world usage."
+
+next(evol_quality.process([{
+    "instruction": instruction,
+    "response": completion
+}]))
+# The process of generating synthetic data through manual prompting involves creating artificial data sets that mimic real-world usage patterns.
+```
+
+可以看到，回答与问题对应，且已经更复杂了。这是个好的开端，但如果你做过 `EvolInstruct` 的实验，就会知道这种算法得到的“更好”回答有时并没有变好。所以我们还需要加入额外的测评技术，确保数据集不出现错误标注。接下来部分就会讨论这一技术。
+
+## 生成分数
+
+分数是衡量一个回答相较于另一个回答受偏好程度的指标。一般来说，这些分数可以是绝对的、主观的或相对的。在本课程中，我们将重点关注前两种，因为它们对于创建偏好数据集最为有用。这种评分方式是一种利用语言模型进行判断和评估的方法，因此与我们在[评测](../4_evaluation/README_CN.md)这一章中看到的评估技术存在一些重叠。与其他评估技术一样，分数和评估通常需要更大的模型，以便更好地契合人类偏好。
+
+### UltraFeedback
+
+UltraFeedback 可以对给出的问题和回答生成分数和评价。
+
+回答的分数一般基于一定的标准给出。有四类细化的标准：有用性、相关性、深度、创造性。借助这些标准，可以简化评分过程。得到分数后，我们就可以决定哪个回答是倾向的（`chosen`），哪个是不倾向的（`rejected`）。因为分数是绝对的，它也可以被用来过滤质量极低或区分度不高的数据。
+
+此外，评价信息可以为得分提供一些逻辑道理，可以用来理解分数造成差异的原因。
+
+UltraFeedback 对应的提示语在 [distilabel 中也有实现](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/templates/ultrafeedback)，这里提供简化版本：
+
+```bash
+Evaluate the model's outputs based on various criteria: Helpfulness, Relevance, Deepening, Creativity
+Your role is to provide a holistic assessment based on the above factors.
+Score the output from 1 to 5 on overall quality.
+
+Answer with the following format: score - rationale
+
+# Input
+{{ input }}
+
+# Response
+{{ output }}
+
+# Score - Rationale
+```
+
+在代码层面，我们使用 [UltraFeedback](https://distilabel.argilla.io/dev/components-gallery/tasks/ultrafeedback/) 类，评估取自 [Model Pooling](#model-pooling) 这一部分的合成的 `prompt` 和 `completion`。
+
+```python
+from distilabel.llms import TransformersLLM
+from distilabel.steps.tasks import UltraFeedback
+
+llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
+ultrafeedback = UltraFeedback(llm=llm)
+ultrafeedback.load()
+
+instruction = "What is synthetic data?"
+completion_a = "Synthetic data is artificially generated data that mimics real-world usage."
+completion_b = "Synthetic data refers to data that has been generated artificially."
+
+next(ultrafeedback.process([{
+    "instruction": instruction,
+    "generations": [completion_a, completion_b]
+}]))
+# [
+#     {
+#         'ratings': [4, 5],
+#         'rationales': ['could have been more specific', 'good definition'],
+#     }
+# ]
+```
+
+## 最佳实践
+
+- 相比于生成分数和评价，只生成分数一般会更简单、更少耗费资源
+- 用更大的模型去生成分数和评价
+- 建议使用一系列有多样性的模型，去生成分数和评价
+- 不断优化 `system_prompt` 和模型
+
+## 接下来
+
+👨🏽‍💻 代码 - 通过[练习](./notebooks/preference_dpo_dataset.ipynb) 去生成一个偏好对齐的数据集
+
+## 参考资料
+
+- [Distilabel 官方文档](https://distilabel.argilla.io/latest/)
+- [Deita 论文](https://arxiv.org/abs/2312.15685)
+- [UltraFeedback 论文](https://arxiv.org/abs/2310.01377)
diff --git a/zh-cn/7_inference/README.md b/zh-cn/7_inference/README.md
new file mode 100644
index 00000000..d05ff2fc
--- /dev/null
+++ b/zh-cn/7_inference/README.md
@@ -0,0 +1,36 @@
+# 大模型推理
+
+推理是指用经过训练的语言模型来进行预测或生成回答的过程。尽管推理看似简单直接，但要大规模高效部署模型，就需要仔细考量诸如性能、成本和可靠性等多种因素。由于大语言模型（LLMs）的规模和计算需求，模型推理存在着一些独特的挑战。
+
+这里，我们借助 [`transformers`](https://huggingface.co/docs/transformers/index) 和 [`text-generation-inference`](https://github.com/huggingface/text-generation-inference) 来探索简单的推理方法以及适用于生产环境的推理方法。针对生产环境的模型部署，我们将重点关注 Text Generation Inference 这个工具包（简称 TGI），它提供了优化过的服务能力。
+
+## 章节概览
+
+大语言模型的推理可以分为两类：简单的使用 pipeline 进行推理，这适用于开发和测试阶段；优化过的服务级推理方案，者适用于生产环境的部署。我们将会讲解这两类方法，从简单的 pipeline 方法开始，再逐步深入到生产环境部署方案。
+
+## 内容目录
+
+### 1. [基本的 Pipeline 推理](./inference_pipeline_cn.md)
+
+这一节你将了解使用 Hugging Face Transformers 的 pipeline 进行基本的推理方法。我们将讲解 pipeline 的设置、生成参数的配置，以及本地部署的最佳实践。该方法适用于原型设计和小规模应用。
+
+### 2. [使用 TGI 进行生产环境部署](./text_generation_inference_cn.md)
+
+这一节你讲学习如何使用 Text Generation Inference 这个工具包进行生产环境的模型部署。我们将探索服务端模型部署的优化技术、组 batch 推理的策略，以及如何监控。TGI 提供了很多适用于生产环境的功能，如健康监测、评估指标、Docker 部署等。
+
+### 练习
+
+| 标题 | 概述 | 练习 | 链接 | Colab |
+|-------|-------------|----------|------|-------|
+| 使用 Pipeline 推理 | 使用 transformers 的 pipeline 进行推理 | 🐢 创建一个 pipeline <br> 🐕 配置生成参数 <br> 🦁 创建一个简单的网页端服务 | [Link](./notebooks/basic_pipeline_inference_cn.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/7_inference/notebooks/basic_pipeline_inference.ipynb) |
+| 使用 TGI 部署 | 在生产环境进行 TGI 部署 | 🐢 用 TGI 部署一个模型 <br> 🐕 调节参数，优化性能 <br> 🦁 监控和扩展 | [Link](./notebooks/tgi_deployment_cn.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/7_inference/notebooks/tgi_deployment.ipynb) |
+
+## 参考资料
+
+- [Hugging Face 的 Pipeline 教程](https://huggingface.co/docs/transformers/en/pipeline_tutorial)
+- [Text Generation Inference 官方文档](https://huggingface.co/docs/text-generation-inference/en/index)
+- [基于 Pipeline 创建 WebServer 指南](https://huggingface.co/docs/transformers/en/pipeline_tutorial#using-pipelines-for-a-webserver)
+- [TGI GitHub 代码仓库](https://github.com/huggingface/text-generation-inference)
+- [Hugging Face 模型部署文档](https://huggingface.co/docs/inference-endpoints/index)
+- [vLLM: High-throughput LLM Serving](https://github.com/vllm-project/vllm)
+- [优化 Transformer 模型推理](https://huggingface.co/blog/optimize-transformer-inference)
diff --git a/zh-cn/7_inference/inference_pipeline.md b/zh-cn/7_inference/inference_pipeline.md
new file mode 100644
index 00000000..f9d72fd3
--- /dev/null
+++ b/zh-cn/7_inference/inference_pipeline.md
@@ -0,0 +1,170 @@
+# 使用 transformers 的 pipeline 进行基本的模型推理
+
+🤗 Transformers 中的 `pipeline` 抽象，为使用 Hugging Face 模型库中的任何模型进行推理提供了一种简单的方法。它处理了所有的预处理和后处理步骤，使得在无需深入了解模型架构或要求的情况下就能轻松使用这些模型。
+
+## pipeline 的工作原理
+
+Hugging Face 的 pipeline 通过将原始输入和人类可读输出之间的三个关键阶段自动化，简化了机器学习工作流程：
+
+**预处理阶段**
+pipeline 首先将你的原始输入为模型做好准备。这会因输入类型而异：
+- 文本输入会经过分词处理，将单词转换为对模型友好的 token ID
+- 图像会被调整大小并进行归一化处理，以符合模型要求
+- 音频会通过特征提取进行处理，以创建频谱图或其他表示形式
+
+**模型推理**
+在正向传播过程中，pipeline 实现了这些事情：
+- 自动进行输入的 batch 处理，以实现高效处理
+- 选择最优计算设备（CPU/GPU）进行计算
+- 如果硬件可以支持，会使用诸如半精度（FP16）推理等技术进行性能优化
+
+**后处理阶段**
+最后，pipeline 将原始的模型输出转换为有用的结果：
+- 将 token ID 解码回可读文本
+- 将 logits 值转换为概率值
+- 根据具体任务（例如分类标签、生成文本），对输出进行格式化
+
+这种抽象让你可以专注于应用程序逻辑，而管道会处理模型推理的技术复杂性。
+
+## 基本用法
+
+下面示例展示如何使用 pipeline 进行文本生成：
+
+```python
+from transformers import pipeline
+
+# Create a pipeline with a specific model
+generator = pipeline(
+    "text-generation",
+    model="HuggingFaceTB/SmolLM2-1.7B-Instruct",
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+# Generate text
+response = generator(
+    "Write a short poem about coding:",
+    max_new_tokens=100,
+    do_sample=True,
+    temperature=0.7
+)
+print(response[0]['generated_text'])
+```
+
+## 关键配置
+
+### 载入模型
+```python
+# CPU inference
+generator = pipeline("text-generation", model="HuggingFaceTB/SmolLM2-1.7B-Instruct", device="cpu")
+
+# GPU inference (device 0)
+generator = pipeline("text-generation", model="HuggingFaceTB/SmolLM2-1.7B-Instruct", device=0)
+
+# Automatic device placement
+generator = pipeline(
+    "text-generation",
+    model="HuggingFaceTB/SmolLM2-1.7B-Instruct",
+    device_map="auto",
+    torch_dtype="auto"
+)
+```
+
+### 生成相关的参数
+
+```python
+response = generator(
+    "Translate this to French:",
+    max_new_tokens=100,     # 生成文本的最大长度
+    do_sample=True,         # 解码时用采样的策略，而不是贪心策略
+    temperature=0.7,        # 这个参数可以控制随机性，值越大越随机
+    top_k=50,               # 采样时，只考虑最靠前的前 k 个 token
+    top_p=0.95,             # 采样时，概率值的阈值
+    num_return_sequences=1  # 针对一个输入输出几个输出
+)
+```
+
+## 同时处理多个输入
+
+Pipeline 可以借助 batch 的技术，高效地同时处理多个输入：
+
+```python
+# Prepare multiple prompts
+prompts = [
+    "Write a haiku about programming:",
+    "Explain what an API is:",
+    "Write a short story about a robot:"
+]
+
+# Process all prompts efficiently
+responses = generator(
+    prompts,
+    batch_size=4,              # Number of prompts to process together
+    max_new_tokens=100,
+    do_sample=True,
+    temperature=0.7
+)
+
+# Print results
+for prompt, response in zip(prompts, responses):
+    print(f"Prompt: {prompt}")
+    print(f"Response: {response[0]['generated_text']}\n")
+```
+
+## 集成入网页端服务
+
+下面示例展示了如何将 pipeline 集成入 FastAPI 应用：
+
+```python
+from fastapi import FastAPI, HTTPException
+from transformers import pipeline
+import uvicorn
+
+app = FastAPI()
+
+# Initialize pipeline globally
+generator = pipeline(
+    "text-generation",
+    model="HuggingFaceTB/SmolLM2-1.7B-Instruct",
+    device_map="auto"
+)
+
+@app.post("/generate")
+async def generate_text(prompt: str):
+    try:
+        if not prompt:
+            raise HTTPException(status_code=400, detail="No prompt provided")
+            
+        response = generator(
+            prompt,
+            max_new_tokens=100,
+            do_sample=True,
+            temperature=0.7
+        )
+        
+        return {"generated_text": response[0]['generated_text']}
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=5000)
+```
+
+## 局限性
+
+虽然 Pipeline 对于原型设计和小规模部署很有用，但它们存在一些局限性：
+
+- 与专用服务解决方案相比，优化选项有限。
+- 不支持动态 batch 处理等高级特性。
+- 可能不适合高吞吐量的生产工作负载。
+
+对于有高吞吐量要求的生产部署，可考虑使用 TGI 或其他专门的服务解决方案。
+
+
+## 参考资料
+
+- [Hugging Face 的 Pipeline 教程](https://huggingface.co/docs/transformers/en/pipeline_tutorial)
+- [Pipeline API 参考资料](https://huggingface.co/docs/transformers/en/main_classes/pipelines)
+- [Text Generation 参数文档](https://huggingface.co/docs/transformers/en/main_classes/text_generation)
+- [模型量化指南](https://huggingface.co/docs/transformers/en/perf_infer_gpu_one)
\ No newline at end of file
diff --git a/zh-cn/7_inference/text_generation_inference.md b/zh-cn/7_inference/text_generation_inference.md
new file mode 100644
index 00000000..01f6cb41
--- /dev/null
+++ b/zh-cn/7_inference/text_generation_inference.md
@@ -0,0 +1,138 @@
+# Text Generation Inference (TGI)
+
+Text Generation Inference（简称 TGI）是一个由 Hugging Face 开发的工具包，主要用于对大语言模型进行部署和服务搭建。它旨在为常用的开源大语言模型（LLMs）实现高性能文本生成。TGI 被 Hugging Chat 用于实际生产，Hugging Chat 是一个面向开源模型的开源交互界面。
+
+
+## 为什么使用 TGI？
+
+TGI 解决了在生产环境中部署大语言模型的关键难题。虽然许多框架在模型开发方面表现出色，但 TGI 专门针对生产部署和扩展进行了优化。它的一些关键特性包括：
+
+- **张量并行**：TGI 能够通过张量并行技术将模型分割到多个 GPU 上，这对于高效部署更大的模型至关重要。
+- **连续 batch 处理**：连续 batch 处理系统通过动态处理请求，最大限度地提高 GPU 利用率，同时诸如 Flash Attention 和 Paged Attention 等优化方法显著降低了内存使用并提高了速度。
+- **token streaming**：实时应用程序受益于通过服务器发送事件（Server-Sent Events）实现的 token streaming 技术，以最低延迟提供响应。
+
+## 怎样使用 TGI
+
+### 基本 Python 用法
+
+TGI 采用了一种简洁而强大的 REST API 集成方式，这使得它能轻松与你的应用程序整合。
+
+### 使用 REST API
+
+TGI 提供了一个接受 JSON 数据的 RESTful API 。这使得任何能够发出 HTTP 请求的编程语言或工具都可以访问它。以下是一个使用 curl 的基本示例：
+
+```bash
+# Basic generation request
+curl localhost:8080/v1/chat/completions \
+    -X POST \
+    -d '{
+  "model": "tgi",
+  "messages": [
+    {
+      "role": "system",
+      "content": "You are a helpful assistant."
+    },
+    {
+      "role": "user",
+      "content": "What is deep learning?"
+    }
+  ],
+  "stream": true,
+  "max_tokens": 20
+}' \
+    -H 'Content-Type: application/json'
+```
+
+### 使用 `huggingface_hub` 的 Python 客户端
+
+`huggingface_hub` 的 Python 客户端可以处理连接管理、请求格式化以及响应解析。示例如下：
+
+
+```python
+from huggingface_hub import InferenceClient
+
+client = InferenceClient(
+    base_url="http://localhost:8080/v1/",
+)
+
+output = client.chat.completions.create(
+    model="tgi",
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Count to 10"},
+    ],
+    stream=True,
+    max_tokens=1024,
+)
+
+for chunk in output:
+    print(chunk.choices[0].delta.content)
+```
+
+
+### 使用 OpenAI 格式的 API
+
+很多代码库支持 OpenAI API，你也可以用对应的客户端请求去和 TGI 进行互动。
+
+```python
+from openai import OpenAI
+
+# init the client but point it to TGI
+client = OpenAI(
+    base_url="http://localhost:8080/v1/",
+    api_key="-"
+)
+
+chat_completion = client.chat.completions.create(
+    model="tgi",
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant." },
+        {"role": "user", "content": "What is deep learning?"}
+    ],
+    stream=True
+)
+
+# iterate and print stream
+for message in chat_completion:
+    print(message)
+```
+
+## 为 TGI 部署准备模型
+
+如果想借助 TGI 给模型构建服务端，你还需要执行以下步骤：
+
+1. **支持的模型架构**：检查你的模型结构，看看是否受 TGI 支持（目前支持 Llama、BLOOM、T5 等)
+
+2. **模型格式**：将模型权重转换为 safetensors 格式，加速模型参数加载
+
+```python
+from safetensors.torch import save_file
+from transformers import AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained("your-model")
+state_dict = model.state_dict()
+save_file(state_dict, "model.safetensors")
+```
+
+1. **量化**（可选步骤）：对模型进行量化，以减少内存使用：
+
+```python
+from transformers import BitsAndBytesConfig
+
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype="float16"
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    "your-model",
+    quantization_config=quantization_config
+)
+```
+
+## 参考资料
+
+- [Text Generation Inference 官方文档](https://huggingface.co/docs/text-generation-inference)
+- [TGI 在 GitHub 上的代码仓库](https://github.com/huggingface/text-generation-inference)
+- [Hugging Face Model Hub](https://huggingface.co/models)
+- [TGI 的 API 参考文档](https://huggingface.co/docs/text-generation-inference/api_reference)
\ No newline at end of file
diff --git a/zh-cn/8_agents/README.md b/zh-cn/8_agents/README.md
new file mode 100644
index 00000000..3c5b3ed8
--- /dev/null
+++ b/zh-cn/8_agents/README.md
@@ -0,0 +1,36 @@
+# 智能体
+
+AI 智能体（AI Agents）是一种自主系统，能够理解用户请求、将请求拆解为多个步骤，然后执行相应行动以完成任务。它们将语言模型与工具及外部功能相结合，从而与所处环境进行交互。本模章将讲解如何使用 [`smolagents`](https://github.com/huggingface/smolagents) 库构建高效的智能体，该库为创建强大的 AI 智能体提供了一个轻量级框架。
+
+## 章节概览
+
+高效的智能体通常具备三种关键能力。一是检索能力，智能体需要能够从各种信息来源获取和使用相关信息。二是函数调用，这使得智能体能够在所处环境中采取具体行动。最后是特定领域的知识和工具，这能让智能体可以执行诸如代码操作这类专业任务。
+
+## 内容目录
+
+### 1️⃣ [检索智能体](./retrieval_agents_cn.md)
+
+检索型智能体将模型与知识库相结合。这些智能体能够从多个来源搜索并整合信息，借助向量数据库实现高效检索，并采用检索增强生成（Retrieval Augmented Generation 或 RAG）模式。它们擅长将网页搜索与自定义知识库相融合，同时通过记忆系统维持对话上下文。这部分教程主要讲解其实现策略，包括用于稳健信息检索的回退机制。
+
+### 2️⃣ [代码智能体](./code_agents_cn.md)
+
+代码智能体是为软件开发任务而设计的专门化自主系统。这些智能体擅长分析和生成代码、执行自动重构，并与开发工具相集成。这部分教程主要讲解如何构建以代码为核心的智能体，以及最佳实践。这些智能体能够理解编程语言、与构建系统（build systems）协作，并与版本控制系统交互，同时保持较高的代码质量标准。
+
+### 3️⃣ [自定义函数](./custom_functions_cn.md)
+
+自定义函数智能体通过专门的函数调用扩展了基本的人工智能能力。这部分教程将探讨如何设计模块化和可扩展的函数接口，使其直接与应用程序的逻辑相集成。你将学习在创建可靠的函数驱动的工作流时，如何实施适当的验证和错误处理。重点在于构建简单的系统，在该系统中智能体可以以可预测的方式与外部工具和服务进行交互。
+
+### 练习
+
+| 标题 | 概述 | 练习 | 链接 | Colab |
+|-------|-------------|----------|------|-------|
+| 构建科研智能体 | 构建一个借助检索和自定义函数可以执行科研任务的智能体 | 🐢 构建简单的 RAG 智能体 <br> 🐕 加入自定义的搜索函数 <br> 🦁 创建完整的科研助手 | [Notebook](./notebooks/agents_cn.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/8_agents/notebooks/agents.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+
+## Resources
+
+- [smolagents Documentation](https://huggingface.co/docs/smolagents) - smolagents 代码库的官方文档
+- [Building Effective Agents](https://www.anthropic.com/research/building-effective-agents) - 智能体架构方面的研究性论文
+- [Agent Guidelines](https://huggingface.co/docs/smolagents/tutorials/building_good_agents) - 构建可靠智能体的最佳实践
+- [LangChain Agents](https://python.langchain.com/docs/how_to/#agents) - 智能体实现的一些示例
+- [Function Calling Guide](https://platform.openai.com/docs/guides/function-calling) - 理解 LLM 中的函数调用
+- [RAG Best Practices](https://www.pinecone.io/learn/retrieval-augmented-generation/) - 高效 RAG 实现指南
diff --git a/zh-cn/8_agents/code_agents.md b/zh-cn/8_agents/code_agents.md
new file mode 100644
index 00000000..ed79de98
--- /dev/null
+++ b/zh-cn/8_agents/code_agents.md
@@ -0,0 +1,68 @@
+# 代码智能体
+
+代码智能体是专门的自主系统，可处理诸如分析、生成、重构和测试之类的代码任务。这些智能体利用关于编程语言、构建系统（build systems）和版本控制的领域知识来增强软件开发工作流程。
+
+## 为什么我们需要代码智能体？
+
+代码智能体通过将重复性任务自动化，同时保持代码质量，从而加速开发过程。它们擅长生成样板代码、进行系统性重构，并通过静态分析识别潜在问题。这些智能体将检索能力（访问外部文档和代码库）与函数调用相结合，以执行创建文件或运行测试等具体操作。
+
+## 代码智能体的组成
+
+代码智能体建立在为代码理解而微调过的专门语言模型之上。这些模型辅以诸如代码检查工具、格式化工具和编译器等开发工具，以便与现实世界环境进行交互。通过检索技术，智能体通过访问文档和代码历史记录来保持上下文感知，以符合组织的模式和标准。以行动为导向的函数使智能体能够执行具体任务，例如提交变更或发起合并请求。
+
+在以下示例中，我们创建一个代码智能体，它可以像我们之前构建的检索智能体那样使用 DuckDuckGo 进行网络搜索。
+
+```python
+from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
+
+agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=HfApiModel())
+
+agent.run("How many seconds would it take for a leopard at full speed to run through Pont des Arts?")
+```
+
+在以下示例中，我们创建了一个代码智能体，它能够获取两个位置之间的运动时间。这里，我们使用 `@tool` 装饰器来定义一个可用作工具的自定义函数。
+
+```python
+from smolagents import CodeAgent, HfApiModel, tool
+
+@tool
+def get_travel_duration(start_location: str, destination_location: str, departure_time: Optional[int] = None) -> str:
+    """Gets the travel time in car between two places.
+    
+    Args:
+        start_location: the place from which you start your ride
+        destination_location: the place of arrival
+        departure_time: the departure time, provide only a `datetime.datetime` if you want to specify this
+    """
+    import googlemaps # All imports are placed within the function, to allow for sharing to Hub.
+    import os
+
+    gmaps = googlemaps.Client(os.getenv("GMAPS_API_KEY"))
+
+    if departure_time is None:
+        from datetime import datetime
+        departure_time = datetime(2025, 1, 6, 11, 0)
+
+    directions_result = gmaps.directions(
+        start_location,
+        destination_location,
+        mode="transit",
+        departure_time=departure_time
+    )
+    return directions_result[0]["legs"][0]["duration"]["text"]
+
+agent = CodeAgent(tools=[get_travel_duration], model=HfApiModel(), additional_authorized_imports=["datetime"])
+
+agent.run("Can you give me a nice one-day trip around Paris with a few locations and the times? Could be in the city or outside, but should fit in one day. I'm travelling only via public transportation.")
+
+```
+
+当然，这两个例子仅仅是你开发代码智能体的开始。你需要阅读 [smolagents 文档](https://huggingface.co/docs/smolagents)来了解更多构建代码智能体的方法。
+
+`smolagents` 为构建代码智能体提供了一个轻量级的框架，其核心代码实现仅有 1000 行左右。该框架专注于构建能够编写和执行 Python 代码片段的智能体，并提供沙盒式执行环境以确保安全性。它同时支持开源和专有语言模型，使其能够适配各种开发环境。
+
+## 延伸阅读
+
+- [smolagents Blog](https://huggingface.co/blog/smolagents) - 介绍 smolagents 的博客
+- [smolagents: Building Good Agents](https://huggingface.co/docs/smolagents/tutorials/building_good_agents) - 构建可靠智能体的最佳实践
+- [Building Effective Agents - Anthropic](https://www.anthropic.com/research/building-effective-agents) - 智能体设计原则
diff --git a/zh-cn/8_agents/custom_functions.md b/zh-cn/8_agents/custom_functions.md
new file mode 100644
index 00000000..57b80298
--- /dev/null
+++ b/zh-cn/8_agents/custom_functions.md
@@ -0,0 +1,71 @@
+# 自定义函数智能体
+
+自定义函数智能体是一类借助专门的函数调用（或称为 tools）来执行任务的人工智能智能体。与通用智能体不同，自定义函数智能体专注于通过直接与应用程序的逻辑相集成，为高级工作流程提供支持。例如，你可以将数据库查询、系统命令或任何自定义实用程序作为独立函数开放，以供智能体调用。
+
+## 为什么需要自定义函数智能体？
+
+- **模块化与可扩展性**：无需构建一个庞大的单体智能体，你可以设计代表不同独立功能的单个函数，使你的架构更具可扩展性。
+
+- **细粒度控制**：开发人员可以通过明确指定哪些函数可用以及它们接受哪些参数，来精细地控制智能体的行为。
+
+- **增强可靠性**：通过为每个函数构建清晰的模式和验证机制，可减少错误和意外行为。
+
+## 基本工作流程
+
+1. **确定函数**
+   确定哪些任务可以转化为自定义函数（例如，文件输入输出、数据库查询、流数据处理）。
+
+2. **定义接口**
+   使用函数签名或模式精确概述每个函数的输入、输出以及预期行为。这在智能体与其运行环境之间建立了严格的契约。
+
+3. **向智能体注册**
+   智能体需要 “了解” 哪些函数可用。通常，你将描述每个函数接口的元数据传递给语言模型或智能体框架。
+
+4. **调用与验证**
+   一旦智能体选择要调用的函数，使用提供的参数运行该函数并验证结果。如果结果有效，将结果反馈给智能体作为上下文，以推动后续决策。
+
+## 示例
+
+以下是一个简化示例，以伪代码展示自定义函数调用可能的形式。目标是执行用户定义的搜索并检索相关内容：
+
+```python
+# Define a custom function with clear input/output types
+def search_database(query: str) -> list:
+    """
+    Search the database for articles matching the query.
+    
+    Args:
+        query (str): The search query string
+        
+    Returns:
+        list: List of matching article results
+    """
+    try:
+        results = database.search(query)
+        return results
+    except DatabaseError as e:
+        logging.error(f"Database search failed: {e}")
+        return []
+
+# Register the function with the agent
+agent.register_function(
+    name="search_database",
+    function=search_database,
+    description="Searches database for articles matching a query"
+)
+
+# Example usage
+def process_search():
+    query = "Find recent articles on AI"
+    results = agent.invoke("search_database", query)
+    
+    if results:
+        agent.process_results(results)
+    else:
+        logging.info("No results found for query")
+```
+
+## 延伸阅读
+
+- [smolagents Blog](https://huggingface.co/blog/smolagents) - 介绍 smolagents 的博客
+- [Building Good Agents](https://huggingface.co/docs/smolagents/tutorials/building_good_agents) - 构建可靠智能体的最佳实践
\ No newline at end of file
diff --git a/zh-cn/8_agents/notebooks/agents.ipynb b/zh-cn/8_agents/notebooks/agents.ipynb
new file mode 100644
index 00000000..54de1d6e
--- /dev/null
+++ b/zh-cn/8_agents/notebooks/agents.ipynb
@@ -0,0 +1,197 @@
+{
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "# 构建 AI 智能体\n",
+                "\n",
+                "\n",
+                "本文包含一些练习，旨在帮助你学习如何使用 `smolagents` 库构建不同类型的智能体。我们将从基础实现逐步过渡到更复杂的实现。\n",
+                "\n",
+                "## 准备\n",
+                "\n",
+                "首先我们要安装需要用到的包："
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "!pip install smolagents\n",
+                "\n",
+                "# Install the requirements in Google Colab\n",
+                "# !pip install transformers datasets trl huggingface_hub\n",
+                "\n",
+                "# Authenticate to Hugging Face\n",
+                "from huggingface_hub import login\n",
+                "\n",
+                "login()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## 🐢 练习 1：基本的代码智能体\n",
+                "\n",
+                "我们从创建一个简单的代码智能体开始，它可以通过网页搜索回答一些编程相关的问题。"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel\n",
+                "\n",
+                "# Initialize the agent\n",
+                "agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=HfApiModel())\n",
+                "\n",
+                "# Test the agent\n",
+                "response = agent.run(\"What's the difference between a list and a tuple in Python?\")\n",
+                "print(response)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "### 🤔 练习 1 挑战项\n",
+                "试着让智能体解释不同的编程概念，并评估它的回复。它在以下方面的表现如何：\n",
+                "1. 基本的语法问题\n",
+                "2. 语言特定的特性\n",
+                "3. 代码示例"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## 🐕 练习 2：带有自定义函数的智能体\n",
+                "\n",
+                "现在我们创建一个可以通过自定义的函数去执行特定任务的智能体。这里我们创建一个简单的计算器，让它作为自定义的工具。"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "from smolagents import CodeAgent, tool\n",
+                "from typing import Union\n",
+                "\n",
+                "\n",
+                "@tool\n",
+                "def calculate(operation: str, numbers: object) -> float:\n",
+                "    \"\"\"Performs basic mathematical operations on a list of numbers.\n",
+                "\n",
+                "    Args:\n",
+                "        operation: One of 'sum', 'average', 'multiply', 'min', 'max'\n",
+                "        numbers: List of numbers to operate on\n",
+                "\n",
+                "    Returns:\n",
+                "        float: Result of the operation\n",
+                "    \"\"\"\n",
+                "    if operation == \"sum\":\n",
+                "        return sum(numbers)\n",
+                "    elif operation == \"average\":\n",
+                "        return sum(numbers) / len(numbers)\n",
+                "    elif operation == \"multiply\":\n",
+                "        result = 1\n",
+                "        for n in numbers:\n",
+                "            result *= n\n",
+                "        return result\n",
+                "    elif operation == \"min\":\n",
+                "        return min(numbers)\n",
+                "    elif operation == \"max\":\n",
+                "        return max(numbers)\n",
+                "    else:\n",
+                "        raise ValueError(f\"Unknown operation: {operation}\")\n",
+                "\n",
+                "\n",
+                "# Create agent with custom tool\n",
+                "math_agent = CodeAgent(tools=[calculate], model=HfApiModel())\n",
+                "\n",
+                "# Test the agent\n",
+                "response = math_agent.run(\"What is the average of 10, 15, 20, 25, and 30?\")\n",
+                "print(response)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "### 🤔 练习 2 挑战项\n",
+                "1. 往计算器这个工具多加入点数学运算功能\n",
+                "2. 创建一个新的自定义工具（例如，字符串处理或日期计算）\n",
+                "3. 把多个工具结合入一个智能体中"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## 🦁 练习 3：改进检索智能体\n",
+                "\n",
+                "最后，让我们构建一个更复杂的智能体，它将网络搜索和记忆相结合，以便在对话中保持上下文连贯。"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "from smolagents import CodeAgent, DuckDuckGoSearchTool\n",
+                "\n",
+                "# Initialize the agent with memory\n",
+                "research_agent = CodeAgent(...)  # TODO: Define the agent\n",
+                "\n",
+                "# Test with a multi-turn conversation\n",
+                "questions = [\n",
+                "    \"What are the main types of machine learning?\",\n",
+                "    \"Can you explain supervised learning in more detail?\",\n",
+                "    \"What are some popular algorithms for this type?\",\n",
+                "]\n",
+                "\n",
+                "# TODO: Test the agent"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "### 🤔 Exercise 3 Challenge\n",
+                "1. 测试智能体在不同主题下保持上下文的能力如何。\n",
+                "2. 实现一个自定义知识库工具（如 [这里](retrieval_agents_cn.md) 所示）。\n",
+                "3. 创建一个混合智能体，将代码理解能力与研究能力相结合。"
+            ]
+        }
+    ],
+    "metadata": {
+        "kernelspec": {
+            "display_name": "Python 3",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.12.8"
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 2
+}
diff --git a/zh-cn/8_agents/retrieval_agents.md b/zh-cn/8_agents/retrieval_agents.md
new file mode 100644
index 00000000..523eb30d
--- /dev/null
+++ b/zh-cn/8_agents/retrieval_agents.md
@@ -0,0 +1,122 @@
+# 构建自主性检索增强生成（RAG）系统
+
+自主性检索增强生成（RAG 或 Retrieval Augmented Generation）将有自主性的智能体的能力与知识检索能力相结合。传统的 RAG 系统只是利用大语言模型（LLM），基于检索到的信息来回答询问。而自主性 RAG 更进一步，允许系统智能地控制自身的检索和回答过程。
+
+传统 RAG 存在关键局限 —— 它仅执行单一的检索步骤，且依赖与用户询问的直接语义相似性，这可能会遗漏相关信息。自主性 RAG 通过让智能体能够自行制定搜索查询、评估结果，并根据需要执行多个检索步骤，来应对这些挑战。
+
+## 使用 DuckDuckGo 实现基本检索
+
+我们首先构建一个简单的智能体，它可以用 DuckDuckGo 去搜索网页。这个智能体可以通过检索相关信息并整合，来回答问题。
+
+```python
+from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
+
+# Initialize the search tool
+search_tool = DuckDuckGoSearchTool()
+
+# Initialize the model
+model = HfApiModel()
+
+agent = CodeAgent(
+    model = model,
+    tools=[search_tool]
+)
+
+# Example usage
+response = agent.run(
+    "What are the latest developments in fusion energy?"
+)
+print(response)
+```
+
+这个智能体背后所做的事情是：
+1. 分析这个问题，确定需要哪些参考信息
+2. 使用 DuckDuckGo 这个搜索引擎寻找相关内容
+3. 整合检索到的信息，形成条理清晰的回答
+4. 在内存中保存本次交互信息，以供将来参考
+
+## 自定义知识库
+
+针对特定领域的应用，我们通常希望在网络搜索之外再加入特有的知识库作为参考。接下来我们就创建一个自定义的工具，用以从一个技术文档的向量数据库中进行查询。
+
+```python
+from smolagents import Tool
+
+class RetrieverTool(Tool):
+    name = "retriever"
+    description = "Uses semantic search to retrieve the parts of transformers documentation that could be most relevant to answer your query."
+    inputs = {
+        "query": {
+            "type": "string",
+            "description": "The query to perform. This should be semantically close to your target documents. Use the affirmative form rather than a question.",
+        }
+    }
+    output_type = "string"
+
+    def __init__(self, docs, **kwargs):
+        super().__init__(**kwargs)
+        self.retriever = BM25Retriever.from_documents(
+            docs, k=10
+        )
+
+    def forward(self, query: str) -> str:
+        assert isinstance(query, str), "Your search query must be a string"
+
+        docs = self.retriever.invoke(
+            query,
+        )
+        return "\nRetrieved documents:\n" + "".join(
+            [
+                f"\n\n===== Document {str(i)} =====\n" + doc.page_content
+                for i, doc in enumerate(docs)
+            ]
+        )
+
+retriever_tool = RetrieverTool(docs_processed)
+```
+
+这个增强版的智能体背后所做的事情是：
+1. 首先检查文档，看有没有相关信息First check the documentation for relevant information
+2. 如有需要，也进行网页搜索
+3. 集成两个信息源的信息
+4. 在内存中保存对话的上下文
+
+## 增强检索能力
+
+当构建自主性检索增强生成（RAG）系统时，智能体还可以借鉴以下复杂策略：
+
+1. 查询重构：智能体并非直接使用用户的原始查询，而是精心设计优化后的搜索词，使其与目标文档更匹配。
+2. 多步检索：智能体可以执行多次搜索，依据初始检索结果来调整后续的查询。
+3. 来源整合：整合来自多个渠道的信息，如网页搜索和本地文档。
+4. 结果验证：在将检索到的内容纳入回复之前，会对其相关性和准确性进行分析。
+
+要构建有效的自主性检索增强生成（RAG）系统，需要仔细考量几个关键方面。智能体应依据查询类型和上下文，在可用工具中做出选择。记忆系统有助于保存对话历史，避免重复检索。制定回退策略可确保即便首选的检索方法失效，系统仍能发挥作用。此外，实施验证步骤有助于保证检索信息的准确性和相关性。
+
+
+```python
+import datasets
+from langchain.docstore.document import Document
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.retrievers import BM25Retriever
+
+knowledge_base = datasets.load_dataset("m-ric/huggingface_doc", split="train")
+knowledge_base = knowledge_base.filter(lambda row: row["source"].startswith("huggingface/transformers"))
+
+source_docs = [
+    Document(page_content=doc["text"], metadata={"source": doc["source"].split("/")[1]})
+    for doc in knowledge_base
+]
+
+text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size=500,
+    chunk_overlap=50,
+    add_start_index=True,
+    strip_whitespace=True,
+    separators=["\n\n", "\n", ".", " ", ""],
+)
+docs_processed = text_splitter.split_documents(source_docs)
+```
+
+## 接下来
+
+⏩ 学习[代码智能体](./code_agents_cn.md)部分，了解如何构建一个可以处理代码任务的智能体。

From 1d8cbea18d4c87fe71139f8dcda5999032569125 Mon Sep 17 00:00:00 2001
From: HoiM <hoiming0730@gmail.com>
Date: Fri, 28 Mar 2025 15:51:18 +0800
Subject: [PATCH 2/3] remove other lang

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diff --git a/1_instruction_tuning/README.md b/1_instruction_tuning/README.md
index a7fae79c..43f9639e 100644
--- a/1_instruction_tuning/README.md
+++ b/1_instruction_tuning/README.md
@@ -1,30 +1,30 @@
-# Instruction Tuning
+# 指令微调
 
-This module will guide you through instruction tuning language models. Instruction tuning involves adapting pre-trained models to specific tasks by further training them on task-specific datasets. This process helps models improve their performance on targeted tasks. 
+本章内容主要聚焦大语言模型指令微调部分。指令微调会把预训练模型在特定领域的数据集上进一步训练，来适配特定的任务。这一过程能有效地提升模型在目标任务上的性能。
 
-In this module, we will explore two topics: 1) Chat Templates and 2) Supervised Fine-Tuning.
+具体而言，本章将会重点探索两个主题：聊天模板和有监督微调。
 
-## 1️⃣ Chat Templates
+## 1️⃣ 聊天模板
 
-Chat templates structure interactions between users and AI models, ensuring consistent and contextually appropriate responses. They include components like system prompts and role-based messages. For more detailed information, refer to the [Chat Templates](./chat_templates.md) section.
+聊天模板的主要作用是把用户和 AI 模型之间的交互信息结构化，确保模型能够稳定输出且根据上下文作出回答。一个聊天模板包含系统提示词和人机两个角色发送的消息。本章的[聊天模板教程](./chat_templates.md)将会详细讲述这一内容。
 
-## 2️⃣ Supervised Fine-Tuning
+## 2️⃣ 有监督微调
 
-Supervised Fine-Tuning (SFT) is a critical process for adapting pre-trained language models to specific tasks. It involves training the model on a task-specific dataset with labeled examples. For a detailed guide on SFT, including key steps and best practices, see the [Supervised Fine-Tuning](./supervised_fine_tuning.md) page.
+有监督微调（SFT）是你将预训练模型往特定任务迁移时的一个重要过程。SFT 通过在特定领域的有标注数据集上进一步训练，来提升模型在这里应用领域的性能。本章[有监督微调教程](./supervised_fine_tuning.md)将会详细讲解相关内容，包括其中的重要步骤和最佳实践。
 
-## Exercise Notebooks
+## 实践练习
 
-| Title | Description | Exercise | Link | Colab |
+| 标题 | 简介 | 习题 | 链接 | Colab |
 |-------|-------------|----------|------|-------|
-| Chat Templates | Learn how to use chat templates with SmolLM2 and process datasets into chatml format | 🐢 Convert the `HuggingFaceTB/smoltalk` dataset into chatml format <br> 🐕 Convert the `openai/gsm8k` dataset into chatml format | [Notebook](./notebooks/chat_templates_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/1_instruction_tuning/notebooks/chat_templates_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-| Supervised Fine-Tuning | Learn how to fine-tune SmolLM2 using the SFTTrainer | 🐢 Use the `HuggingFaceTB/smoltalk` dataset<br>🐕 Try out the `bigcode/the-stack-smol` dataset<br>🦁 Select a dataset for a real world use case | [Notebook](./notebooks/sft_finetuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-
-## References
-
-- [Transformers documentation on chat templates](https://huggingface.co/docs/transformers/main/en/chat_templating)
-- [Script for Supervised Fine-Tuning in TRL](https://github.com/huggingface/trl/blob/main/examples/scripts/sft.py)
-- [`SFTTrainer` in TRL](https://huggingface.co/docs/trl/main/en/sft_trainer)
-- [Direct Preference Optimization Paper](https://arxiv.org/abs/2305.18290)
-- [Supervised Fine-Tuning with TRL](https://huggingface.co/docs/trl/main/en/tutorials/supervised_finetuning)
-- [How to fine-tune Google Gemma with ChatML and Hugging Face TRL](https://www.philschmid.de/fine-tune-google-gemma)
-- [Fine-tuning LLM to Generate Persian Product Catalogs in JSON Format](https://huggingface.co/learn/cookbook/en/fine_tuning_llm_to_generate_persian_product_catalogs_in_json_format)
+| 聊天模板 | 学习使用 SmolLM2 的聊天模板，并将数据集转换为 ChatML 聊天模板的格式 | 🐢 将 `HuggingFaceTB/smoltalk` 数据集转换为 ChatnML 格式 <br> 🐕 将 `openai/gsm8k` 转换为 ChatML 格式 | [Notebook](./notebooks/chat_templates_example_cn.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/1_instruction_tuning/notebooks/chat_templates_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+| 有监督微调 | 学习用 SFTTrainer 去微调 SmolLM2 模型 | 🐢 使用 `HuggingFaceTB/smoltalk` 数据集训练模型<br>🐕 使用 `bigcode/the-stack-smol` 数据集训练模型<br>🦁 针对一个实际场景选取数据集去训练 | [Notebook](./notebooks/sft_finetuning_example_cn.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+
+## 参考资料
+
+- [transformers 文档中关于聊天模板的介绍](https://huggingface.co/docs/transformers/main/en/chat_templating)
+- [使用 TRL 进行有监督微调的示例脚本](https://github.com/huggingface/trl/blob/main/examples/scripts/sft.py)
+- [TRL 官方文档关于 `SFTTrainer` 的介绍](https://huggingface.co/docs/trl/main/en/sft_trainer)
+- [DPO 算法论文](https://arxiv.org/abs/2305.18290)
+- [TRL 官方文档中关于有监督微调的教程](https://huggingface.co/docs/trl/main/en/tutorials/supervised_finetuning)
+- [博客：用 ChatML 和 TRL 微调 Google Gemma 模型](https://www.philschmid.de/fine-tune-google-gemma)
+- [教程：微调大语言模型使其输出 JSON 格式的内容](https://huggingface.co/learn/cookbook/en/fine_tuning_llm_to_generate_persian_product_catalogs_in_json_format)
diff --git a/1_instruction_tuning/chat_templates.md b/1_instruction_tuning/chat_templates.md
index 61ff65e6..ed6fe292 100644
--- a/1_instruction_tuning/chat_templates.md
+++ b/1_instruction_tuning/chat_templates.md
@@ -1,18 +1,18 @@
-# Chat Templates
+# 聊天模板
 
-Chat templates are essential for structuring interactions between language models and users. They provide a consistent format for conversations, ensuring that models understand the context and role of each message while maintaining appropriate response patterns.
+如果想将模型与用户的交互信息结构化，那么一个聊天模板（chat template）就是必需的。它为对话提供了一个固定格式，让模型能够知道上下文信息以及每条消息是由谁发出的，只有这样模型才能生成恰当的回答。
 
-## Base Models vs Instruct Models
+## 基础模型 vs 指令模型
 
-A base model is trained on raw text data to predict the next token, while an instruct model is fine-tuned specifically to follow instructions and engage in conversations. For example, `SmolLM2-135M` is a base model, while `SmolLM2-135M-Instruct` is its instruction-tuned variant.
+基础模型（base model）指的是在未经整理的文本数据上训练、用于预测下一个 token 的模型，而指令模型（instruct model）则是通过微调来跟随指令、参与对话的模型。举例来说，`SmolLM2-135M` 就是基础模型，而 `SmolLM2-135M-Instruct` 则是前者经指令调优得到的指令模型。
 
-To make a base model behave like an instruct model, we need to format our prompts in a consistent way that the model can understand. This is where chat templates come in. ChatML is one such template format that structures conversations with clear role indicators (system, user, assistant).
+为了让基础模型成为指令模型，我们需要对我们的输入提示词进行规范化，用一种固定的格式输入给模型，以便于模型理解。这就用到**聊天模板**了。举例来说，ChatML 就是一个这样的模板，它将对话过程完全结构化，清晰地指明了每段信息是由哪个角色（系统、用户、助手）说出的。
 
-It's important to note that a base model could be fine-tuned on different chat templates, so when we're using an instruct model we need to make sure we're using the correct chat template.
+需要注意，一个基础模型可以往不同的聊天模板上微调。所以当我们使用训练好的指令模型时，我们也需要注意不要用错聊天模板。
 
-## Understanding Chat Templates
+## 聊天模板简介
 
-At their core, chat templates define how conversations should be formatted when communicating with a language model. They include system-level instructions, user messages, and assistant responses in a structured format that the model can understand. This structure helps maintain consistency across interactions and ensures the model responds appropriately to different types of inputs. Below is an example of a chat template:
+聊天模板定义了当用户和语言模型对话时，对话信息应该遵循什么样的格式。这其中包含来自三个角色的信息：系统级的指令、用户发出的信息、AI 助手的回答。这使得每次人机交互的信息格式都是一致的，确保模型针对不同问题都能恰当回答。下面就是一个聊天模板示例：
 
 ```sh
 <|im_start|>user
@@ -24,7 +24,7 @@ Can I ask a question?<|im_end|>
 <|im_start|>assistant
 ```
 
-The `transformers` library will take care of chat templates for you in relation to the model's tokenizer. Read more about how transformers builds chat templates [here](https://huggingface.co/docs/transformers/en/chat_templating#how-do-i-use-chat-templates). All we have to do is structure our messages in the correct way and the tokenizer will take care of the rest. Here's a basic example of a conversation:
+实际上，如果你使用 `transformers` 库的 tokenizer，它将会为我们将对话信息转化为聊天模板形式。你可以在[这里](https://huggingface.co/docs/transformers/en/chat_templating#how-do-i-use-chat-templates)查看相关文档。我们仅需将对话信息结构化，后面的事情交给 tokenizer 即可。比如，你可以把聊天信息写成这样：
 
 ```python
 messages = [
@@ -34,11 +34,11 @@ messages = [
 ]
 ```
 
-Let's break down the above example, and see how it maps to the chat template format.
+接下来，我们将分解聊天信息的组成：系统信息和对话部分。
 
-## System Messages
+### 系统消息
 
-System messages set the foundation for how the model should behave. They act as persistent instructions that influence all subsequent interactions. For example:
+系统消息从基本层面定义了模型应有的行为。它会影响接下来所有交互。看下列示例就能明白：
 
 ```python
 system_message = {
@@ -47,9 +47,9 @@ system_message = {
 }
 ```
 
-## Conversations
+### 对话部分
 
-Chat templates maintain context through conversation history, storing previous exchanges between users and the assistant. This allows for more coherent multi-turn conversations:
+聊天模板也需要保留对话历史记录，将之前发生的人机对话保存下来，作为后续对话的参考。只有这样，我们才能实现多轮交互式对话。
 
 ```python
 conversation = [
@@ -59,9 +59,9 @@ conversation = [
 ]
 ```
 
-## Implementation with Transformers
+## 使用 Transformers 构建聊天模板 
 
-The transformers library provides built-in support for chat templates. Here's how to use them:
+使用 `transformers` 构建聊天模板的示例如下： 
 
 ```python
 from transformers import AutoTokenizer
@@ -81,8 +81,14 @@ formatted_chat = tokenizer.apply_chat_template(
 )
 ```
 
-## Custom Formatting
-You can customize how different message types are formatted. For example, adding special tokens or formatting for different roles:
+上述代码运行完后，`formatted_chat` 应该是这样：
+```
+'<|im_start|>system\nYou are a helpful coding assistant.<|im_end|>\n<|im_start|>user\nWrite a Python function to sort a list<|im_end|>\n<|im_start|>assistant\n'
+```
+
+### 自定义聊天模板格式
+
+你也可以自定义聊天模板格式，比如为不同角色的信息添加特殊的 token 来作为标识：
 
 ```python
 template = """
@@ -92,9 +98,9 @@ template = """
 """.lstrip()
 ```
 
-## Multi-Turn Support
+### 对多轮对话的支持
 
-Templates can handle complex multi-turn conversations while maintaining context:
+聊天模板可以处理复杂多轮对话，同时保留上下文信息：
 
 ```python
 messages = [
@@ -105,10 +111,10 @@ messages = [
 ]
 ```
 
-⏭️ [Next: Supervised Fine-Tuning](./supervised_fine_tuning.md)
+⏭️ [下一节课程：有监督微调](./supervised_fine_tuning.md)
 
-## Resources
+## 其它学习资源
 
-- [Hugging Face Chat Templating Guide](https://huggingface.co/docs/transformers/main/en/chat_templating)
-- [Transformers Documentation](https://huggingface.co/docs/transformers)
-- [Chat Templates Examples Repository](https://github.com/chujiezheng/chat_templates) 
+- [Hugging Face 聊天模板使用指南](https://huggingface.co/docs/transformers/main/en/chat_templating)
+- [Transformers 官方文档](https://huggingface.co/docs/transformers)
+- [包含各种聊天模板的代码仓库](https://github.com/chujiezheng/chat_templates) 
diff --git a/1_instruction_tuning/notebooks/chat_templates_example.ipynb b/1_instruction_tuning/notebooks/chat_templates_example.ipynb
index 93772206..a6d98a62 100644
--- a/1_instruction_tuning/notebooks/chat_templates_example.ipynb
+++ b/1_instruction_tuning/notebooks/chat_templates_example.ipynb
@@ -6,9 +6,9 @@
     "id": "vZAvFVIAtFlq"
    },
    "source": [
-    "# Exploring Chat Templates with SmolLM2\n",
+    "# 借助 SmolLM2 探索聊天模板\n",
     "\n",
-    "This notebook demonstrates how to use chat templates with the `SmolLM2` model. Chat templates help structure interactions between users and AI models, ensuring consistent and contextually appropriate responses."
+    "这个教程演示如何在 `SmolLM2` 下使用聊天模板。借助聊天模板，人机之间的交互信息可以被结构化，这样我们才能确保模型的回答和上下文信息保持一致性。"
    ]
   },
   {
@@ -54,9 +54,9 @@
     "id": "XTVOqbuetFlr"
    },
    "source": [
-    "## SmolLM2 Chat Template\n",
+    "## SmolLM2 的聊天模板\n",
     "\n",
-    "Let's explore how to use a chat template with the `SmolLM2` model. We'll define a simple conversation and apply the chat template."
+    "我们先看看怎样使用 `SmolLM2` 的聊天模板。这里我们先定义一个简单的对话，然后用聊天模板转换它。"
    ]
   },
   {
@@ -201,9 +201,9 @@
     "id": "Ve4dgtjstFls"
    },
    "source": [
-    "# Apply chat template without tokenization\n",
+    "# 先不进行 tokenize，应用一下聊天模板\n",
     "\n",
-    "The tokenizer represents the conversation as a string with special tokens to describe the role of the user and the assistant.\n"
+    "先把 `tokenize` 这个入参设为 `False`。可以看到，下面例子增加了额外的角色信息。"
    ]
   },
   {
@@ -241,9 +241,9 @@
     "id": "sfvdglOqtFls"
    },
    "source": [
-    "# Decode the conversation\n",
+    "# 对已经 tokenize 过的对话进行解码\n",
     "\n",
-    "Note that the conversation is represented as above but with a further assistant message.\n"
+    "如果已经把 `tokenize` 设为 `True`，可以通过解码的方式查看对话内容。可以看到，这里多了一个 assistant 信息。"
    ]
   },
   {
@@ -284,10 +284,9 @@
     "id": "UcZQpspEtFlt"
    },
    "source": [
-    "# Tokenize the conversation\n",
+    "# tokenize 过后的对话是什么形式\n",
     "\n",
-    "Of course, the tokenizer also tokenizes the conversation and special token as ids that relate to the model's vocabulary.\n",
-    "\n"
+    "对对话应用聊天模板并进行 tokenize 之后，我们发现，整个对话，包括特殊 token，都被转化为了一系列整数。\n"
    ]
   },
   {
@@ -322,14 +321,21 @@
    },
    "source": [
     "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Exercise: Process a dataset for SFT</h2>\n",
-    "    <p>Take a dataset from the Hugging Face hub and process it for SFT. </p>\n",
-    "    <p><b>Difficulty Levels</b></p>\n",
-    "    <p>🐢 Convert the `HuggingFaceTB/smoltalk` dataset into chatml format.</p>\n",
-    "    <p>🐕 Convert the `openai/gsm8k` dataset into chatml format.</p>\n",
+    "    <h2 style='margin: 0;color:blue'>练习：为 SFT 准备数据集</h2>\n",
+    "    <p>从 HuggingFace 上找一个数据集，然后为后续 SFT 转化它</p>\n",
+    "    <p><b>难度等级</b></p>\n",
+    "    <p>🐢 将 `HuggingFaceTB/smoltalk` 数据集转换为 ChatML 形式</p>\n",
+    "    <p>🐕 将 `openai/gsm8k` 数据集转换为 ChatML 形式</p>\n",
     "</div>"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -531,7 +537,10 @@
    "execution_count": null,
    "metadata": {
     "collapsed": true,
-    "id": "bWUSv7NMtFlu"
+    "id": "bWUSv7NMtFlu",
+    "jupyter": {
+     "outputs_hidden": true
+    }
    },
    "outputs": [],
    "source": [
@@ -557,11 +566,11 @@
     "id": "qlXCuRKotFlu"
    },
    "source": [
-    "## Conclusion\n",
+    "## 总结\n",
     "\n",
-    "This notebook demonstrated how to apply chat templates to different models, `SmolLM2`. By structuring interactions with chat templates, we can ensure that AI models provide consistent and contextually relevant responses.\n",
+    "本文讲解了在 `SmolLM2` 模型中如何使用聊天模板。通过聊天模板来结构化人机交互信息，我们可以确保 AI 模型可以给出和上下文保持一致性的回答。\n",
     "\n",
-    "In the exercise you tried out converting a dataset into chatml format. Luckily, TRL will do this for you, but it's useful to understand what's going on under the hood."
+    "在练习中，你将尝试将数据集转为 ChatML 格式。但实际情况中，TRL 会为你做这些事情。不过，充分理解背后的处理逻辑还是很有必要的。"
    ]
   }
  ],
@@ -570,7 +579,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "py310",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -584,7 +593,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.15"
+   "version": "3.9.6"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
@@ -5722,5 +5731,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }
diff --git a/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb b/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb
index d18479a9..7d259da3 100644
--- a/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb
+++ b/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb
@@ -4,17 +4,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Supervised Fine-Tuning with SFTTrainer\n",
+    "# 用 SFTTrainer 实现有监督微调\n",
     "\n",
-    "This notebook demonstrates how to fine-tune the `HuggingFaceTB/SmolLM2-135M` model using the `SFTTrainer` from the `trl` library. The notebook cells run and will finetune the model. You can select your difficulty by trying out different datasets.\n",
+    "本教程将展示如何使用 `trl` 代码库中的 `SFTTrainer` 来微调 `HuggingFaceTB/SmolLM2-135M`。文中代码模块将会展示具体步骤。你可以根据难度选取在不同数据集上微调。\n",
     "\n",
     "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Exercise: Fine-Tuning SmolLM2 with SFTTrainer</h2>\n",
-    "    <p>Take a dataset from the Hugging Face hub and finetune a model on it. </p> \n",
-    "    <p><b>Difficulty Levels</b></p>\n",
-    "    <p>🐢 Use the `HuggingFaceTB/smoltalk` dataset</p>\n",
-    "    <p>🐕 Try out the `bigcode/the-stack-smol` dataset and finetune a code generation model on a specific subset `data/python`.</p>\n",
-    "    <p>🦁 Select a dataset that relates to a real world use case your interested in</p>\n",
+    "    <h2 style='margin: 0;color:blue'>练习：用 SFTTrainer 微调 SmolLM2</h2>\n",
+    "    <p>从 HuggingFace 上选取一个数据集然后微调模型 </p> \n",
+    "    <p><b>难度等级</b></p>\n",
+    "    <p>🐢 使用 `HuggingFaceTB/smoltalk` 数据集</p>\n",
+    "    <p>🐕 尝试 `bigcode/the-stack-smol` 数据集，在 `data/python` 这个子集上微调一个代码生成模型</p>\n",
+    "    <p>🦁 选取一个你感兴趣的领域，使用真实世界数据集，为你的特定应用微调模型</p>\n",
     "</div>"
    ]
   },
@@ -72,9 +72,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Generate with the base model\n",
+    "# 用基准模型生成\n",
     "\n",
-    "Here we will try out the base model which does not have a chat template. "
+    "我们首先看看不使用聊天模板的基础模型是什么效果。"
    ]
   },
   {
@@ -101,11 +101,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Dataset Preparation\n",
+    "## 准备数据集\n",
     "\n",
-    "We will load a sample dataset and format it for training. The dataset should be structured with input-output pairs, where each input is a prompt and the output is the expected response from the model.\n",
+    "接下来我们载入一个简单数据集，并转换格式。这个数据集需要结构化为“输入-输出”这样成对的形式，其中输入是一个提示语，输出就是我们期待的模型回答。\n",
     "\n",
-    "**TRL will format input messages based on the model's chat templates.** They need to be represented as a list of dictionaries with the keys: `role` and `content`,."
+    "**TRL 会自动根据聊天模板转换输入信息。**这些信息需要写成一个字典（dict）的列表，其中 key 是 `role` 和 `content`，分别代表哪个角色以及对应的话语。"
    ]
   },
   {
@@ -117,7 +117,7 @@
     "# Load a sample dataset\n",
     "from datasets import load_dataset\n",
     "\n",
-    "# TODO: define your dataset and config using the path and name parameters\n",
+    "# TODO: 稍后你可以使用你自己的数据集，替换以下的路径、名字等参数配置\n",
     "ds = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")"
    ]
   },
@@ -127,16 +127,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# TODO: 🦁 If your dataset is not in a format that TRL can convert to the chat template, you will need to process it. Refer to the [module](../chat_templates.md)"
+    "# TODO: 🦁 如果你的数据集不是 TRL 能转换成聊天模板的格式，你还需要额外处理一下。参考[聊天模板](../chat_templates_cn.md)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Configuring the SFTTrainer\n",
+    "## 配置 SFTTrainer\n",
     "\n",
-    "The `SFTTrainer` is configured with various parameters that control the training process. These include the number of training steps, batch size, learning rate, and evaluation strategy. Adjust these parameters based on your specific requirements and computational resources."
+    "我们还需要配置 `SFTTrainer` 的各项参数，来控制训练过程。这包括训练步数、batch size、学习率，以及评估策略。你需要根据你自己的情况和计算资源来调节。"
    ]
   },
   {
@@ -170,16 +170,16 @@
     "    eval_dataset=ds[\"test\"],\n",
     ")\n",
     "\n",
-    "# TODO: 🦁 🐕 align the SFTTrainer params with your chosen dataset. For example, if you are using the `bigcode/the-stack-smol` dataset, you will need to choose the `content` column`"
+    "# TODO: 🦁 🐕 根据你自己的数据集调节 SFTTrainer 的参数。假如你用的是 `bigcode/the-stack-smol` 数据集，那你就需要选择 `content` 这一列。"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Training the Model\n",
+    "## 训练模型\n",
     "\n",
-    "With the trainer configured, we can now proceed to train the model. The training process will involve iterating over the dataset, computing the loss, and updating the model's parameters to minimize this loss."
+    " 当上述配置完成后，我们就可以训练了。这个过程将遍历数据集、计算损失函数、更新模型参数等。"
    ]
   },
   {
@@ -209,8 +209,8 @@
    "metadata": {},
    "source": [
     "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Bonus Exercise: Generate with fine-tuned model</h2>\n",
-    "    <p>🐕 Use the fine-tuned to model generate a response, just like with the base example..</p>\n",
+    "    <h2 style='margin: 0;color:blue'>额外练习：用微调过的模型来生成</h2>\n",
+    "    <p>🐕 使用微调过的模型来生成回答，就像之前用基准模型一样。对比一下效果</p>\n",
     "</div>"
    ]
   },
@@ -232,26 +232,26 @@
     "# Generate response\n",
     "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
     "\n",
-    "# TODO: use the fine-tuned to model generate a response, just like with the base example."
+    "# TODO: 用微调过的模型生成相同的回答，看看有没有变化改进"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 💐 You're done!\n",
+    "## 💐 完成了！\n",
     "\n",
-    "This notebook provided a step-by-step guide to fine-tuning the `HuggingFaceTB/SmolLM2-135M` model using the `SFTTrainer`. By following these steps, you can adapt the model to perform specific tasks more effectively. If you want to carry on working on this course, here are steps you could try out:\n",
+    "本文提供了使用 `SFTTrainer` 微调 `HuggingFaceTB/SmolLM2-135M` 的指南。依照这些步骤，你可以将模型适配到各种特殊任务场景上。如果你觉得这个课程还不错，你还可以尝试这些：\n",
     "\n",
-    "- Try this notebook on a harder difficulty\n",
-    "- Review a colleagues PR\n",
-    "- Improve the course material via an Issue or PR."
+    "- 使用这个 notebook 完成难点分级更高的任务\n",
+    "- 在 GitHub 上评审别人的 pull request\n",
+    "- 通过提出 Issue 或 PR 进一步改进我们的课程资料"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "py310",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -265,9 +265,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.15"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/1_instruction_tuning/supervised_fine_tuning.md b/1_instruction_tuning/supervised_fine_tuning.md
index 8c6c3df4..e7250c38 100644
--- a/1_instruction_tuning/supervised_fine_tuning.md
+++ b/1_instruction_tuning/supervised_fine_tuning.md
@@ -1,41 +1,41 @@
-# Supervised Fine-Tuning
+# 有监督微调
 
-Supervised Fine-Tuning (SFT) is a critical process for adapting pre-trained language models to specific tasks or domains. While pre-trained models have impressive general capabilities, they often need to be customized to excel at particular use cases. SFT bridges this gap by further training the model on carefully curated datasets with human-validated examples.
+有监督微调（Supervised Fine-Tuning），简称 SFT，是将预训练模型往特定领域或任务迁移的一个重要过程。虽然预训练模型总体性能也很不错，但如果需要应对特定场景，就必须针对场景进行定制化。SFT 通过在人工筛选的高质量数据上进一步训练，将预训练模型往特定任务上迁移。
 
-## Understanding Supervised Fine-Tuning
+## SFT 原理简介
 
-At its core, supervised fine-tuning is about teaching a pre-trained model to perform specific tasks through examples of labeled tokens. The process involves showing the model many examples of the desired input-output behavior, allowing it to learn the patterns specific to your use case.
+SFT 核心思想是让预训练模型学习标注过的、特定领域的数据。这个过程会向模型提供很多我们想要的输入-输出，让模型去学习我们想要的特定回答模式。
 
-SFT is effective because it uses the foundational knowledge acquired during pre-training while adapting the model's behavior to match your specific needs.
+由于利用了预训练模型学到的基础知识，通过 SFT 将模型进一步适配到特定领域的训练还是非常高效的。
 
-## When to Use Supervised Fine-Tuning
+## 什么时候使用 SFT
 
-The decision to use SFT often comes down to the gap between your model's current capabilities and your specific requirements. SFT becomes particularly valuable when you need precise control over the model's outputs or when working in specialized domains.
+SFT 通常在你当前模型的能力和你的特殊需求还存在差距时使用，尤其是当你想要精细控制模型输出，或是想让模型在特定领域发挥作用时。
 
-For example, if you're developing a customer service application, you might want your model to consistently follow company guidelines and handle technical queries in a standardized way. Similarly, in medical or legal applications, accuracy and adherence to domain-specific terminology becomes crucial. In these cases, SFT can help align the model's responses with professional standards and domain expertise.
+举例来说，如果你在开发客户服务相关的应用，那你可能需要让模型严格遵循公司规定、用标准化的流程去处理技术性的咨询。或者，在医疗或法律领域，准确表达特定领域的专业术语也很重要。在这些情况下，SFT 就能将模型性能对齐到领域内专家的水平，使得模型的回答符合专业标准。
 
-## The Fine-Tuning Process
+## 微调的过程
 
-The supervised fine-tuning process involves adjusting a model's weights on a task-specific dataset. 
+SFT 主要就是在特定任务的数据集上训练模型。
 
-First, you'll need to prepare or select a dataset that represents your target task. This dataset should include diverse examples that cover the range of scenarios your model will encounter. The quality of this data is important - each example should demonstrate the kind of output you want your model to produce. Next comes the actual fine-tuning phase, where you'll use frameworks like Hugging Face's `transformers` and `trl` to train the model on your dataset. 
+首先，你需要准备一个能反映任务类型的数据集，这个数据集需要涵盖尽可能广泛的领域内问答场景。数据的质量非常重要，每条数据都应该向模型展示你希望得到的回答类型。接下来就是实际微调阶段了，你可以使用 HuggingFace 的 `transformers` 和 `trl` 在数据集上训练模型。
 
-Throughout the process, continuous evaluation is essential. You'll want to monitor the model's performance on a validation set to ensure it's learning the desired behaviors without losing its general capabilities. In [module 4](../4_evaluation), we'll cover how to evaluate your model.
+整个过程中，不断地对模型进行评测也是很重要的。你可以找一个验证集，然后实时监控模型性能，来确保模型学到了特定领域内的回答，同时又不丧失它原有的通用能力。在[第四章](../4_evaluation)，我们将讲解如何评测模型。
 
-## The Role of SFT in Preference Alignment
+## 通过 SFT 对齐特定偏好
 
-SFT plays a fundamental role in aligning language models with human preferences. Techniques such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) rely on SFT to form a base level of task understanding before further aligning the model’s responses with desired outcomes. Pre-trained models, despite their general language proficiency, may not always generate outputs that match human preferences. SFT bridges this gap by introducing domain-specific data and guidance, which improves the model’s ability to generate responses that align more closely with human expectations.
+此外，SFT 也广泛用于将语言模型对齐到特定的人类偏好上。如 RLHF 和 DPO 等技术依靠 SFT 来形成对任务的基本理解，然后再进一步对模型的响应进行优化，以达到预期效果。预训练模型，尽管在通用的语言能力上效果很强，但它的回答可能不符合人类的偏好。SFT 通过引入专业领域的数据进行指导，可以改善模型生成与人类期望的匹配程度。
 
-## Supervised Fine-Tuning With Transformer Reinforcement Learning
+## 使用 Transformer Reinforcement Learning 进行 SFT
 
-A key software package for Supervised Fine-Tuning is Transformer Reinforcement Learning (TRL). TRL is a toolkit used to train transformer language models using reinforcement learning (RL).
+TRL（Transformer Reinforcement Learning）是常用于 SFT 的一个重要的软件包。如果你使用强化学习训练 transformer 系列的语言模型，TRL 将为你提供有用工具。
 
-Built on top of the Hugging Face Transformers library, TRL allows users to directly load pretrained language models and supports most decoder and encoder-decoder architectures. The library facilitates major processes of RL used in language modelling, including supervised fine-tuning (SFT), reward modeling (RM), proximal policy optimization (PPO), and Direct Preference Optimization (DPO). We will use TRL in a number of modules throughout this repo.
+TRL 基于 HuggingFace 的 `transformers` 构建，允许用户直接载入预训练模型，并支持大部分 decoder 或 encoder-decoder 的架构。这个库涵盖了针对语言模型的主流强化学习算法，包括 SFT、奖励模型、PPO、DPO 等。在本课程中，我们将大量使用 TRL。
 
-# Next Steps
+# 接下来的学习
 
-Try out the following tutorials to get hands on experience with SFT using TRL:
+请通过下列 notebook 上手 SFT：
 
-⏭️ [Chat Templates Tutorial](./notebooks/chat_templates_example.ipynb)
+⏭️ [聊天模板教程](./notebooks/chat_templates_example_cn.ipynb)
 
-⏭️ [Supervised Fine-Tuning Tutorial](./notebooks/sft_finetuning_example.ipynb)
+⏭️ [有监督微调教程](./notebooks/supervised_fine_tuning_tutorial_cn.ipynb)
\ No newline at end of file
diff --git a/2_preference_alignment/README.md b/2_preference_alignment/README.md
index f85b02de..48232423 100644
--- a/2_preference_alignment/README.md
+++ b/2_preference_alignment/README.md
@@ -1,41 +1,40 @@
-# Preference Alignment
+# 偏好对齐
 
-This module covers techniques for aligning language models with human preferences. While supervised fine-tuning helps models learn tasks, preference alignment encourages outputs to match human expectations and values.
+本章将学习如何将语言模型的输出和人类偏好对齐。虽然有监督微调（SFT）已经将模型适配到特定的任务领域了，但偏好对齐（Prefenrece Alignment）将会迫使模型的输出更加匹配人类的期望、符合人类的价值观。
 
-## Overview
+## 概览
 
-Typical alignment methods involve multiple stages:
-1. Supervised Fine-Tuning (SFT) to adapt models to specific domains
-2. Preference alignment (like RLHF or DPO) to improve response quality
+典型的偏好对齐方法一般都包含这几个步骤：
+1. 使用 SFT 将模型适配到特定的领域
+2. 使用偏好对齐（如 RLHF 或 DPO 算法）进一步提升模型回答的质量
 
-Alternative approaches like ORPO combine instruction tuning and preference alignment into a single process. Here, we will focus on DPO and ORPO algorithms.
+其它偏好对齐算法还包括 ORPO，这个算法将指令微调和偏好对齐结合进了一个单一步骤中。本章我们将重点学习 DPO 和 ORPO 算法。
 
-If you would like to learn more about the different alignment techniques, you can read more about them in the [Argilla Blog](https://argilla.io/blog/mantisnlp-rlhf-part-8). 
+如果你还想进一步学习相关对齐算法，你可以阅读[这篇博客](https://argilla.io/blog/mantisnlp-rlhf-part-8)。
 
-### 1️⃣ Direct Preference Optimization (DPO)
+### 1️⃣ 直接偏好优化（DPO）
 
-Direct Preference Optimization (DPO) simplifies preference alignment by directly optimizing models using preference data. This approach eliminates the need for separate reward models and complex reinforcement learning, making it more stable and efficient than traditional Reinforcement Learning from Human Feedback (RLHF). For more details, you can refer to the [Direct Preference Optimization (DPO) documentation](./dpo.md).
+直接偏好优化（Direct Preference Optimization），简称 DPO，直接使用偏好数据对模型进行参数更新。这简化了偏好对齐的过程。这个方法无需额外设置激励模型、无需复杂强化学习步骤，比基于人类反馈的强化学习（RLHF）更高效更稳定。本章中对应的学习资料在[这里](./dpo.md)。
 
+### 2️⃣ 基于优势比的偏好优化（ORPO）
 
-### 2️⃣ Odds Ratio Preference Optimization (ORPO)
+基于优势比的偏好优化（Odds Ratio Preference Optimization），简称 ORPO，是一种将指令微调和偏好对齐结合在一起的方法。通过在 token 层面定义一个优势比（Odds），并在优势比上使用负对数似然损失函数，ORPO 改变了传统的语言建模的目标函数。ORPO 训练步骤简单、无需 DPO 中的参考模型，计算效率也更高。该方法在多项评测基准上展现了优秀的效果，尤其在 AlpacaEval 超越了传统方法。本章中对应的学习资料在[这里](./orpo.md)。
 
-ORPO introduces a combined approach to instruction tuning and preference alignment in a single process. It modifies the standard language modeling objective by combining negative log-likelihood loss with an odds ratio term on a token level. The approach features a unified single-stage training process, reference model-free architecture, and improved computational efficiency. ORPO has shown impressive results across various benchmarks, demonstrating better performance on AlpacaEval compared to traditional methods. For more details, you can refer to the [Odds Ratio Preference Optimization (ORPO) documentation](./orpo.md).
+## 实践练习
 
-## Exercise Notebooks
-
-| Title | Description | Exercise | Link | Colab |
+| 标题 | 简介 | 习题 | 链接 | Colab |
 |-------|-------------|----------|------|-------|
-| DPO Training | Learn how to train models using Direct Preference Optimization | 🐢 Train a model using the Anthropic HH-RLHF dataset<br>🐕 Use your own preference dataset<br>🦁 Experiment with different preference datasets and model sizes | [Notebook](./notebooks/dpo_finetuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-| ORPO Training | Learn how to train models using Odds Ratio Preference Optimization | 🐢 Train a model using instruction and preference data<br>🐕 Experiment with different loss weightings<br>🦁 Compare ORPO results with DPO | [Notebook](./notebooks/orpo_finetuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+| DPO 训练 | 学习用 DPO 训练模型 | 🐢 在 Anthropic HH-RLHF 数据集上训练模型<br>🐕 使用你自己的偏好数据集<br>🦁 对不同的偏好数据集和不同大小的模型进行实验 | [Notebook](./notebooks/dpo_finetuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+| ORPO 训练 | 学习用 ORPO 训练模型 | 🐢 用指令数据和偏好数据训练模型<br>🐕 对不同的损失权重进行实验<br>🦁 对比 ORPO 和 DPO 的结果 | [Notebook](./notebooks/orpo_finetuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
 
 
-## Resources
+## 参考资料
 
-- [TRL Documentation](https://huggingface.co/docs/trl/index) - Documentation for the Transformers Reinforcement Learning (TRL) library, which implements various alignment techniques including DPO.
-- [DPO Paper](https://arxiv.org/abs/2305.18290) - Original research paper introducing Direct Preference Optimization as a simpler alternative to RLHF that directly optimizes language models using preference data.
-- [ORPO Paper](https://arxiv.org/abs/2403.07691) - Introduces Odds Ratio Preference Optimization, a novel approach that combines instruction tuning and preference alignment in a single training stage.
-- [Argilla RLHF Guide](https://argilla.io/blog/mantisnlp-rlhf-part-8/) - A guide explaining different alignment techniques including RLHF, DPO, and their practical implementations.
-- [Blog post on DPO](https://huggingface.co/blog/dpo-trl) - Practical guide on implementing DPO using the TRL library with code examples and best practices.
-- [TRL example script on DPO](https://github.com/huggingface/trl/blob/main/examples/scripts/dpo.py) - Complete example script demonstrating how to implement DPO training using the TRL library.
-- [TRL example script on ORPO](https://github.com/huggingface/trl/blob/main/examples/scripts/orpo.py) - Reference implementation of ORPO training using the TRL library with detailed configuration options.
-- [Hugging Face Alignment Handbook](https://github.com/huggingface/alignment-handbook) - Resource guides and codebase for aligning language models using various techniques including SFT, DPO, and RLHF.
+- [TRL 官方文档](https://huggingface.co/docs/trl/index) - TRL 是一个基于 Transformers 的强化学习库，这里实现了包括 DPO 在内的各种对齐算法。
+- [DPO 论文](https://arxiv.org/abs/2305.18290) - 该论文针对当时已有的 RLHF 方法，提出了新的对齐方法，可以直接使用偏好数据优化模型参数。
+- [ORPO 论文](https://arxiv.org/abs/2403.07691) - ORPO 算法将指令微调和偏好优化和并进一个训练步骤中。
+- [RLHF 相关博客](https://argilla.io/blog/mantisnlp-rlhf-part-8/) - 这篇博客介绍了包括 RLHF、DPO 在内的对齐算法，同时也介绍了具体实现方法。
+- [DPO 相关博客](https://huggingface.co/blog/dpo-trl) - 介绍了使用 TRL 实现 DPO 的具体步骤，包括示例代码和其它最佳实践经验。
+- [DPO 示例训练脚本](https://github.com/huggingface/trl/blob/main/examples/scripts/dpo.py) - 完整的基于 TRL 的 DPO 训练代码。
+- [ORPO 示例训练脚本](https://github.com/huggingface/trl/blob/main/examples/scripts/orpo.py) - 完整的基于 TRL 的 ORPO 训练代码。
+- [Hugging Face 关于对齐训练的资料](https://github.com/huggingface/alignment-handbook) - 包括 SFT、DPO、RLHF 的语言模型对齐算法介绍，包括理论指导和实践代码。
\ No newline at end of file
diff --git a/2_preference_alignment/dpo.md b/2_preference_alignment/dpo.md
index a2207489..ea3b23d8 100644
--- a/2_preference_alignment/dpo.md
+++ b/2_preference_alignment/dpo.md
@@ -1,24 +1,24 @@
-# Direct Preference Optimization (DPO)
+# 直接偏好优化（DPO）
 
-Direct Preference Optimization (DPO) offers a simplified approach to aligning language models with human preferences. Unlike traditional RLHF methods that require separate reward models and complex reinforcement learning, DPO directly optimizes the model using preference data.
+直接偏好优化（Direct Preference Optimization），简称 DPO，是一种非常简洁的使用人类偏好数据对齐模型的算法。DPO 直接使用偏好数据优化模型，无需 RLHF 的激励模型和强化学习步骤。
 
-## Understanding DPO
+## 理解 DPO
 
-DPO recasts preference alignment as a classification problem on human preference data. Traditional RLHF approaches require training a separate reward model and using complex reinforcement learning algorithms like PPO to align model outputs. DPO simplifies this process by defining a loss function that directly optimizes the model's policy based on preferred vs non-preferred outputs.
+DPO 将偏好对齐任务转化为了一个在偏好数据上训练的分类任务。传统的 RLHF 需要训练一个额外的激励模型，并使用强化学习方法（如 PPO）去对齐模型输出。DPO简化了这个步骤，通过定义一个损失函数，直接在“倾向的输出”和“不倾向的输出”上进行训练。
 
-This approach has proven highly effective in practice, being used to train models like Llama. By eliminating the need for a separate reward model and reinforcement learning stage, DPO makes preference alignment more accessible and stable.
+这个方法在实践中十分高效，Llama 模型的训练就使用了 DPO。同时，没有了激励模型了强化学习，DPO 训练也更简单、更稳定。
 
-## How DPO Works
+## DPO 工作原理
 
-The DPO process requires supervised fine-tuning (SFT) to adapt the model to the target domain. This creates a foundation for preference learning by training on standard instruction-following datasets. The model learns basic task completion while maintaining its general capabilities.
+在 DPO 之前，我们需要使用 SFT 微调模型，用指令跟随的数据集先把模型适配到特定任务领域中，让模型在这个领域具备基本能力。
 
-Next comes preference learning, where the model is trained on pairs of outputs - one preferred and one non-preferred. The preference pairs help the model understand which responses better align with human values and expectations.
+接下来才是偏好学习。模型将在“倾向的输出”和“不倾向的输出”这样成对的数据上训练，学习哪种类型的回答更符合人类的喜好。
 
-The core innovation of DPO lies in its direct optimization approach. Rather than training a separate reward model, DPO uses a binary cross-entropy loss to directly update the model weights based on preference data. This streamlined process makes training more stable and efficient while achieving comparable or better results than traditional RLHF.
+DPO 的关键原理在于它直接使用偏好数据进行优化。不同于 RLHF，DPO 使用了二分类的交叉墒损失函数，这里的损失直接在“倾向的输出”和“不倾向的输出”这样的成对数据上计算。这使得模型训练更稳定、更高效，同时效果甚至还比 RLHF 好。
 
-## DPO datasets
+## DPO 数据集
 
-Datasets for DPO are typically created by annotating pairs of responses as preferred or non-preferred. This can be done manually or using automated filtering techniques. Below is an example structure of single turn preference dataset for DPO:
+构造 DPO 专用数据集，一般需要对回答进行“倾向”和“不倾向”的标注。使用人工标注或自动化方法都可以实现这一步骤。下表就是一个示例数据集：
 
 | Prompt | Chosen | Rejected |
 |--------|--------|----------|
@@ -26,14 +26,14 @@ Datasets for DPO are typically created by annotating pairs of responses as prefe
 | ...    | ...    | ...      |
 | ...    | ...    | ...      |
 
-The `Prompt` column contains the prompt used to generate the `Chosen` and `Rejected` responses. The `Chosen` and `Rejected` columns contain the responses that are preferred and non-preferred respectively. There are variations on this structure, for example, including a system prompt column or `Input` column containing reference material. The values of `chosen` and `rejected` can be be represented as strings for single turn conversations or as conversation lists. 
+`Prompt` 这一栏提供问题， `Chosen` 和 `Rejected` 分别代表针对这个问题我们倾向的回答和不倾向的回答。`chosen` 和 `rejected` 也可以是一个列表形式，包含多个不同的回答。
 
-You can find a collection of DPO datasets on Hugging Face [here](https://huggingface.co/collections/argilla/preference-datasets-for-dpo-656f0ce6a00ad2dc33069478).
+你可以在 Hugging Face 的[这个地方](https://huggingface.co/collections/argilla/preference-datasets-for-dpo-656f0ce6a00ad2dc33069478)找到很多 DPO 数据集。
 
-## Implementation with TRL
+## 用 TRL 实现 DPO
 
-The Transformers Reinforcement Learning (TRL) library makes implementing DPO straightforward. The `DPOConfig` and `DPOTrainer` classes follow the same `transformers` style API.
-Here's a basic example of setting up DPO training:
+使用 TRL 实现 DPO 非常简单直接，仅需配置 `DPOConfig` 和 `DPOTrainer` 即可。这两个类遵循 `transformers` 的 API 风格。
+下面就是一个简单的例子：
 
 ```python
 from trl import DPOConfig, DPOTrainer
@@ -55,18 +55,18 @@ trainer = DPOTrainer(
 trainer.train()
 ```
 
-We will cover more details on how to use the `DPOConfig` and `DPOTrainer` classes in the [DPO Tutorial](./notebooks/dpo_finetuning_example.ipynb).
+我们还将在 [DPO 教程](./notebooks/dpo_finetuning_example.ipynb) 中详细讲解 `DPOConfig` 和 `DPOTrainer` 的配置。
 
-## Best Practices
+## 最佳实践
 
-Data quality is crucial for successful DPO implementation. The preference dataset should include diverse examples covering different aspects of desired behavior. Clear annotation guidelines ensure consistent labeling of preferred and non-preferred responses. You can improve model performance by improving the quality of your preference dataset. For example, by filtering down larger datasets to include only high quality examples, or examples that relate to your use case.
+数据质量对 DPO 的成败至关重要。偏好数据集必须足够多样，涵盖不同的想要的回答。在数据标注过程中，需要制定清晰明确的标注指导。通过提高数据集质量一般都可以提升模型性能，可能的做法包括对大规模数据集进行过滤，仅保留高质量数据，或仅保留和应用领域相关的数据。
 
-During training, carefully monitor the loss convergence and validate performance on held-out data. The beta parameter may need adjustment to balance preference learning with maintaining the model's general capabilities. Regular evaluation on diverse prompts helps ensure the model is learning the intended preferences without overfitting.
+训练过程中，仔细监视损失的收敛情况、及时验证性能也很重要。及时调节 $\beta$ 参数，在偏好学习和通用能力间找到平衡。有规律地在多样的问题上做验证测试，确保模型不过你和。这些也都很重要。
 
-Compare the model's outputs with the reference model to verify improvement in preference alignment. Testing on a variety of prompts, including edge cases, helps ensure robust preference learning across different scenarios.
+同时，也要对比一下原模型和优化后模型针对同一问题的回答，看看 模型是否学到了偏好。在包括极端情况下的问题集上测试，确保模型健壮性。
 
-## Next Steps
+## 接下来的学习
 
-⏩ To get hands-on experience with DPO, try the [DPO Tutorial](./notebooks/dpo_finetuning_example.ipynb). This practical guide will walk you through implementing preference alignment with your own model, from data preparation to training and evaluation. 
+⏩ 在 [DPO 教程](./notebooks/dpo_finetuning_example.ipynb)中，你可以直接上手实践。该教程将会带你实践 DPO 的整个过程，从数据准备指导模型训练和验证。
 
-⏭️ After completing the tutorial, you can explore the [ORPO](./orpo.md) page to learn about another preference alignment technique.
\ No newline at end of file
+⏭️ 之后，你还可以学习 [ORPO](./orpo.md)，了解更多偏好优化算法。
\ No newline at end of file
diff --git a/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb b/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
index 3ddef6a6..e3d9869b 100644
--- a/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
+++ b/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
@@ -4,18 +4,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Preference Alignment with Direct Preference Optimization (DPO)\n",
+    "# 使用直接偏好优化（DPO）进行偏好对齐\n",
     "\n",
-    "This notebook will guide you through the process of fine-tuning a language model using Direct Preference Optimization (DPO). We will use the SmolLM2-135M-Instruct model which has already been through a SFT training, so it it compatible with DPO. You can also use the model you trained in [1_instruction_tuning](../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb).\n",
+    "本教程将会带你使用直接偏好优化（DPO）的方法去微调一个语言模型。这里我们使用的是 SmolLM2-135M-Instruct 模型，它已经经过了 SFT 这一训练步骤，所以可以接着进行 DPO。当然你也可以使用你在[第一章](../../1_instruction_tuning/notebooks/sft_finetuning_example_cn.ipynb)训练好的模型。\n",
     "\n",
     "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "     <h2 style='margin: 0;color:blue'>Exercise: Aligning SmolLM2 with DPOTrainer</h2>\n",
-    "     <p>Take a dataset from the Hugging Face hub and align a model on it. </p> \n",
-    "     <p><b>Difficulty Levels</b></p>\n",
-    "     <p>🐢 Use the `trl-lib/ultrafeedback_binarized` dataset</p>\n",
-    "     <p>🐕 Try out the `argilla/ultrafeedback-binarized-preferences` dataset</p>\n",
-    "     <p>🦁 Select a dataset that relates to a real-world use case you’re interested in, or use the model you trained in \n",
-    "        <a href=\"../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb\">1_instruction_tuning</a></p>\n",
+    "     <h2 style='margin: 0;color:blue'>练习：用 DPOTrainer 对 SmolLM2 做对齐训练</h2>\n",
+    "     <p>从 Hugging Face hub 上找一个数据集并在上面对齐模型。</p> \n",
+    "     <p><b>难度等级</b></p>\n",
+    "     <p>🐢 使用 `trl-lib/ultrafeedback_binarized` 数据集</p>\n",
+    "     <p>🐕 使用 `argilla/ultrafeedback-binarized-preferences` 数据集</p>\n",
+    "     <p>🦁 选用你感兴趣的真实世界数据集，或者使用你在<a href=\"../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb\">上一章</a> SFT 训练好的模型</p>\n",
     "</div>"
    ]
   },
@@ -41,7 +40,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Import libraries\n"
+    "## 准备好需要的 Python 库\n"
    ]
   },
   {
@@ -60,7 +59,7 @@
     "import os\n",
     "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
     "from datasets import load_dataset\n",
-    "from trl import DPOTrainer, DPOConfig"
+    "from trl import DPOTrainer, DPOConfig\n"
    ]
   },
   {
@@ -69,7 +68,7 @@
     "id": "d8CvUgROUDw-"
    },
    "source": [
-    "## Format dataset"
+    "## 配置数据集及其格式"
    ]
   },
   {
@@ -82,7 +81,7 @@
    "source": [
     "# Load dataset\n",
     "\n",
-    "# TODO: 🦁🐕 change the dataset to one of your choosing\n",
+    "# TODO: 🦁🐕 你也可以使用你感兴趣的其他数据集\n",
     "dataset = load_dataset(path=\"trl-lib/ultrafeedback_binarized\", split=\"train\")"
    ]
   },
@@ -92,20 +91,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# TODO: 🐕 If your dataset is not represented as conversation lists, you can use the `process_dataset` function to convert it."
+    "# TODO: 🐕 如果你的数据集不是对话列表形式，你可以使用 `process_dataset` 函数进行转化"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Select the model\n",
+    "## 选择一个经 SFT 训练过模型\n",
     "\n",
-    "We will use the SmolLM2-135M-Instruct model which has already been through a SFT training, so it it compatible with DPO. You can also use the model you trained in [1_instruction_tuning](../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb).\n",
+    "我们这里使用 SmolLM2-135M-Instruct 模型，因为它已经经过 SFT 了，所以可以接着进行 DPO 训练。你也可以使用你[第一章](../../1_instruction_tuning/notebooks/sft_finetuning_example_cn.ipynb) 训练好的模型。\n",
     "\n",
     "\n",
     "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; width:80%; color:black'>\n",
-    "     <p>🦁 change the model to the path or repo id of the model you trained in <a href=\"../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb\">1_instruction_tuning</a></p>\n",
+    "     <p>🦁 如果你想使用<a href=\"../../1_instruction_tuning/notebooks/sft_finetuning_example_cn.ipynb\">第一章</a>你自己训练的模型，你需要修改下面代码的 model_name 到指定的路径</p>\n",
     "</div>\n"
    ]
   },
@@ -115,14 +114,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# TODO: 🦁 change the model to the path or repo id of the model you trained in [1_instruction_tuning](../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb)\n",
-    "\n",
     "model_name = \"HuggingFaceTB/SmolLM2-135M-Instruct\"\n",
     "\n",
     "device = (\n",
     "    \"cuda\"\n",
     "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
+    "    else \"mps\"\n",
+    "    if torch.backends.mps.is_available()\n",
+    "    else \"cpu\"\n",
     ")\n",
     "\n",
     "# Model to fine-tune\n",
@@ -145,7 +144,7 @@
     "id": "DeT5eUK_UJgK"
    },
    "source": [
-    "## Train model with DPO"
+    "## 开始 DPO 训练"
    ]
   },
   {
@@ -242,45 +241,38 @@
    "source": [
     "# Training arguments\n",
     "training_args = DPOConfig(\n",
-    "    # Training batch size per GPU\n",
+    "    # 单 GPU 上 batch size 的大小\n",
     "    per_device_train_batch_size=4,\n",
-    "    # Number of updates steps to accumulate before performing a backward/update pass\n",
-    "    # Effective batch size = per_device_train_batch_size * gradient_accumulation_steps\n",
+    "    # 每经过多少步才进行一次反向传播、参数更新\n",
+    "    # 每个 GPU 实际起作用的 batch size 等于 per_device_train_batch_size 乘 gradient_accumulation_steps\n",
     "    gradient_accumulation_steps=4,\n",
-    "    # Saves memory by not storing activations during forward pass\n",
-    "    # Instead recomputes them during backward pass\n",
+    "    # 这个操作在前向传播时不保存中间结果激活值，可以节省内存\n",
+    "    # 在反向传播时，模型重新计算前向过程，获取计算梯度所需要的激活值\n",
     "    gradient_checkpointing=True,\n",
-    "    # Base learning rate for training\n",
+    "    # 训练时的基本学习率\n",
     "    learning_rate=5e-5,\n",
-    "    # Learning rate schedule - 'cosine' gradually decreases LR following cosine curve\n",
+    "    # 训练过程中学习率变化策略：\"cosine\" 策略根据余弦函数的曲线形状逐步递减学习率\n",
     "    lr_scheduler_type=\"cosine\",\n",
-    "    # Total number of training steps\n",
+    "    # 总的训练部数\n",
     "    max_steps=200,\n",
-    "    # Disables model checkpointing during training\n",
+    "    # 在模型训练过程中不去保存 ckeckpoint\n",
     "    save_strategy=\"no\",\n",
-    "    # How often to log training metrics\n",
+    "    # 打印 log 的频率\n",
     "    logging_steps=1,\n",
-    "    # Directory to save model outputs\n",
+    "    # 保存结果的目录\n",
     "    output_dir=\"smol_dpo_output\",\n",
-    "    # Number of steps for learning rate warmup\n",
+    "    # 学习率 warmup 的步数\n",
     "    warmup_steps=100,\n",
-    "    # Use bfloat16 precision for faster training\n",
+    "    # 是否使用 bfloat16 精度来加速训练\n",
     "    bf16=True,\n",
-    "    # Disable wandb/tensorboard logging\n",
-    "    report_to=\"none\",\n",
-    "    # Keep all columns in dataset even if not used\n",
+    "    # 这里我们不使用 wandb 或 tensorboard \n",
+    "    report_to=None,\n",
+    "    # 这里我们保留数据集里没用到的数据列\n",
     "    remove_unused_columns=False,\n",
-    "    # Enable MPS (Metal Performance Shaders) for Mac devices\n",
+    "    # 如果你不使用 Mac 电脑，这里就是 False\n",
     "    use_mps_device=device == \"mps\",\n",
-    "    # Model ID for HuggingFace Hub uploads\n",
+    "    # 如果你要将训练好的模型上传到 HuggingFace Hub，这里就是你的模型名字 \n",
     "    hub_model_id=finetune_name,\n",
-    "    # DPO-specific temperature parameter that controls the strength of the preference model\n",
-    "    # Lower values (like 0.1) make the model more conservative in following preferences\n",
-    "    beta=0.1,\n",
-    "    # Maximum length of the input prompt in tokens\n",
-    "    max_prompt_length=1024,\n",
-    "    # Maximum combined length of prompt + response in tokens\n",
-    "    max_length=1536,\n",
     ")"
    ]
   },
@@ -299,13 +291,13 @@
     "    train_dataset=dataset,\n",
     "    # Tokenizer for processing inputs\n",
     "    processing_class=tokenizer,\n",
-    "    # DPO-specific temperature parameter that controls the strength of the preference model\n",
-    "    # Lower values (like 0.1) make the model more conservative in following preferences\n",
-    "    # beta=0.1,\n",
-    "    # Maximum length of the input prompt in tokens\n",
-    "    # max_prompt_length=1024,\n",
-    "    # Maximum combined length of prompt + response in tokens\n",
-    "    # max_length=1536,\n",
+    "    # DPO 特有的 temperature 参数，控制参考模型的强度\n",
+    "    # 较小的值可以让模型更保守、更缓慢地学习偏好\n",
+    "    beta=0.1,\n",
+    "    # 提示语的最大长度\n",
+    "    max_prompt_length=1024,\n",
+    "    # 提示语 + 回答 的序列最大长度\n",
+    "    max_length=1536,\n",
     ")"
    ]
   },
@@ -330,13 +322,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 💐 You're done!\n",
+    "## 💐 完成\n",
     "\n",
-    "This notebook provided a step-by-step guide to fine-tuning the `HuggingFaceTB/SmolLM2-135M` model using the `DPOTrainer`. By following these steps, you can adapt the model to perform specific tasks more effectively. If you want to carry on working on this course, here are steps you could try out:\n",
+    "本教程带你一步步地学习了用 `DPOTrainer` 训练 `HuggingFaceTB/SmolLM2-135M`的过程。利用这些步骤，你可以高效快速地进行特定任务领域的偏好优化训练。感兴趣的话，你还可以：\n",
     "\n",
-    "- Try this notebook on a harder difficulty\n",
-    "- Review a colleagues PR\n",
-    "- Improve the course material via an Issue or PR."
+    "- 尝试本教程难度等级更高的任务\n",
+    "- 在 GitHub 上评审别人的 Pull Request\n",
+    "- 通过 Issue 或 PR 提出课程改进的意见"
    ]
   }
  ],
diff --git a/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb b/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
index a1ec3a18..39c1bd9a 100644
--- a/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
+++ b/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
@@ -4,17 +4,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Preference Alignment with Odds Ratio Preference Optimization (ORPO)\n",
+    "# 使用基于优势比的偏好优化（ORPO）进行偏好对齐\n",
     "\n",
-    "This notebook will guide you through the process of fine-tuning a language model using Odds Ratio Preference Optimization (ORPO). We will use the SmolLM2-135M model which has **not** been through SFT training, so it is not compatible with DPO. This means, you cannot use the model you trained in [1_instruction_tuning](../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb).\n",
+    "本教程将会带你使用基于优势比的偏好优化（ORPO）算法去微调一个语言模型。这里我们使用 SmolLM2-135M 模型，因为它还**没有**经过 SFT 的训练（所以这个模型是不能直接进行 DPO 训练的）。这里你就不能使用你在[第一章](../../1_instruction_tuning/notebooks/sft_finetuning_example_cn.ipynb)训练好的模型了。\n",
     "\n",
     "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "     <h2 style='margin: 0;color:blue'>Exercise: Aligning SmolLM2 with ORPOTrainer</h2>\n",
-    "     <p>Take a dataset from the Hugging Face hub and align a model on it. </p> \n",
-    "     <p><b>Difficulty Levels</b></p>\n",
-    "     <p>🐢 Use the `trl-lib/ultrafeedback_binarized` dataset</p>\n",
-    "     <p>🐕 Try out the `argilla/ultrafeedback-binarized-preferences` dataset</p>\n",
-    "     <p>🦁 Try on a subset of mlabonne's `orpo-dpo-mix-40k` dataset</p>\n",
+    "     <h2 style='margin: 0;color:blue'>练习：借助 ORPOTrainer 对齐 SmolLM2 模型</h2>\n",
+    "     <p>从 Hugging Face hub 上找一个数据集并在上面对齐模型。</p> \n",
+    "     <p><b>难度等级</b></p>\n",
+    "     <p>🐢 使用 `trl-lib/ultrafeedback_binarized` 数据集</p>\n",
+    "     <p>🐕 尝试使用 `argilla/ultrafeedback-binarized-preferences` 数据集</p>\n",
+    "     <p>🦁 尝试使用 `mlabonne/orpo-dpo-mix-40k` 数据集的一个子集进行训练</p>\n",
     "</div>\n",
     "\n"
    ]
@@ -23,7 +23,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Import libraries\n"
+    "## 准备好需要的 Python 库\n"
    ]
   },
   {
@@ -66,7 +66,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Format dataset"
+    "## 配置数据集及其格式"
    ]
   },
   {
@@ -86,7 +86,7 @@
    "source": [
     "# Load dataset\n",
     "\n",
-    "# TODO: 🦁🐕 change the dataset to one of your choosing\n",
+    "# TODO: 🦁🐕 你也可以使用你感兴趣的其他数据集\n",
     "dataset = load_dataset(path=\"trl-lib/ultrafeedback_binarized\")"
    ]
   },
@@ -96,14 +96,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# TODO: 🐕 If your dataset is not represented as conversation lists, you can use the `process_dataset` function to convert it."
+    "# TODO: 🐕 如果你的数据集不是对话列表形式，你可以使用 `process_dataset` 函数进行转化"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Define the model"
+    "## 定义好你要使用的模型"
    ]
   },
   {
@@ -279,7 +279,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Train model with ORPO"
+    "## 开始 ORPO 训练"
    ]
   },
   {
@@ -320,38 +320,38 @@
    "outputs": [],
    "source": [
     "orpo_args = ORPOConfig(\n",
-    "    # Small learning rate to prevent catastrophic forgetting\n",
+    "    # 使用较小的学习率可以预防“灾难性遗忘”\n",
     "    learning_rate=8e-6,\n",
-    "    # Linear learning rate decay over training\n",
+    "    # 学习率衰减策略\n",
     "    lr_scheduler_type=\"linear\",\n",
-    "    # Maximum combined length of prompt + completion\n",
+    "    # 提示词 + 回答 的最大长度（最大 token 数量）\n",
     "    max_length=1024,\n",
-    "    # Maximum length for input prompts\n",
+    "    # 输入的提示词的最大长度\n",
     "    max_prompt_length=512,\n",
-    "    # Controls weight of the odds ratio loss (λ in paper)\n",
+    "    # 优势比损失函数的权重（论文中的 λ 超参数）\n",
     "    beta=0.1,\n",
-    "    # Batch size for training\n",
+    "    # 单个 GPU 的 batch size\n",
     "    per_device_train_batch_size=2,\n",
     "    per_device_eval_batch_size=2,\n",
-    "    # Helps with training stability by accumulating gradients before updating\n",
+    "    # 每多少步前向过程才更新一次参数，调节这里可以稳定训练\n",
     "    gradient_accumulation_steps=4,\n",
-    "    # Memory-efficient optimizer for CUDA, falls back to adamw_torch for CPU/MPS\n",
+    "    # 使用 paged_adamw_8bit 优化器可以为 CUDA 节省内存，如果是 CPU 或 MPS 设备，则退回到使用 adamw_torch 优化器\n",
     "    optim=\"paged_adamw_8bit\" if device == \"cuda\" else \"adamw_torch\",\n",
-    "    # Number of training epochs\n",
+    "    # 训练的 epoch 数量\n",
     "    num_train_epochs=1,\n",
-    "    # When to run evaluation\n",
+    "    # 训练过程中验证的策略\n",
     "    evaluation_strategy=\"steps\",\n",
-    "    # Evaluate every 20% of training\n",
+    "    # 每完成 20% 训练就验证一次\n",
     "    eval_steps=0.2,\n",
-    "    # Log metrics every step\n",
+    "    # log 打印频率\n",
     "    logging_steps=1,\n",
-    "    # Gradual learning rate warmup\n",
+    "    # 学习率 warmup 的步数\n",
     "    warmup_steps=10,\n",
-    "    # Disable external logging\n",
-    "    report_to=\"none\",\n",
-    "    # Where to save model/checkpoints\n",
+    "    # 这里我们不使用 wandb 或 tensorboard，不使用外部打印 log 的功能\n",
+    "    report_to=None,\n",
+    "    # 保存训练结果的目录\n",
     "    output_dir=\"./results/\",\n",
-    "    # Enable MPS (Metal Performance Shaders) if available\n",
+    "    # 如果是苹果电脑，将会使用 MPS 硬件加速\n",
     "    use_mps_device=device == \"mps\",\n",
     "    hub_model_id=finetune_name,\n",
     ")"
@@ -392,13 +392,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 💐 You're done!\n",
+    "## 💐 完成\n",
     "\n",
-    "This notebook provided a step-by-step guide to fine-tuning the `HuggingFaceTB/SmolLM2-135M` model using the `ORPOTrainer`. By following these steps, you can adapt the model to perform specific tasks more effectively. If you want to carry on working on this course, here are steps you could try out:\n",
+    "本教程带你一步步地学习了用 `ORPOTrainer` 去训练 `HuggingFaceTB/SmolLM2-135M` 模型。利用这些步骤，你可以高效快速地进行特定任务领域的偏好优化训练。感兴趣的话，你还可以：\n",
     "\n",
-    "- Try this notebook on a harder difficulty\n",
-    "- Review a colleagues PR\n",
-    "- Improve the course material via an Issue or PR."
+    "- 尝试本教程难度等级更高的任务\n",
+    "- 在 GitHub 上评审别人的 Pull Request\n",
+    "- 通过 Issue 或 PR 提出课程改进的意见"
    ]
   }
  ],
diff --git a/2_preference_alignment/orpo.md b/2_preference_alignment/orpo.md
index 29ede4eb..a8808871 100644
--- a/2_preference_alignment/orpo.md
+++ b/2_preference_alignment/orpo.md
@@ -1,47 +1,46 @@
-# Odds Ratio Preference Optimization (ORPO)
+# 基于优势比的偏好优化（ORPO）
 
-ORPO (Odds Ratio Preference Optimization) is a novel fine-tuning technique that combines fine-tuning and preference alignment into a single unified process. This combined approach offers advantages in efficiency and performance compared to traditional methods like RLHF or DPO.
+基于优势比的偏好优化（Odds Ratio Preference Optimization）或 ORPO，是一种更新颖的偏好对齐方法，它把微调和偏好对齐结合，组成一个统一的过程。这个算法相比于 RLHF 和 DPO 有着更高的效率和更好的性能。
 
-## Understanding ORPO
+## 理解 ORPO
 
-Alignment with methods like DPO typically involve two separate steps: supervised fine-tuning to adapt the model to a domain and format, followed by preference alignment to align with human preferences. While SFT effectively adapts models to target domains, it can inadvertently increase the probability of generating both desirable and undesirable responses. ORPO addresses this limitation by integrating both steps into a single process, as illustrated in the comparison below:
+诸如 DPO 的对齐方法一般包含两个步骤：使用 SFT 先让模型适配如这个领域或回答格式，然后进行偏好对齐训练。虽然 SFT 已经将模型对齐到了特定任务领域，但模型不可避免可能会产生我们不期望的回答，所以我们还需要进行下一步的偏好对齐。ORPO 则合并了这两个步骤。下图取自 ORPO 论文，对比了 RLHF、DPO 和 ORPO 的差异：
 
 ![Alignment Techniques Comparison](https://argilla.io/images/blog/mantisnlp-rlhf/part-8-alignments.png)
-*Comparison of different model alignment techniques*
+*三种对齐算法的对比*
 
-## How ORPO Works
+## ORPO 工作原理
 
-The training process leverages a preference dataset similar to what we used for DPO, where each training example contains an input prompt along with two responses: one that is preferred, and another that is rejected. Unlike other alignment methods that require separate stages and reference models, ORPO integrates preference alignment directly into the supervised fine-tuning process. This monolithic approach makes it reference model-free, computationally more efficient, and memory efficient with fewer FLOPs.
+ORPO 训练使用的数据集和 DPO 相似：针对一个输入的问题包含两个可能输出：一个是“倾向的输出”，另一个是“不倾向的输出”。不同的是，ORPO 直接将偏好对齐加入到 SFT 中。这一整体性方法使得它无需 DPO 中的参考模型，同时也更高效、节省内存。
 
-ORPO creates a new objective by combining two main components:
+ORPO 的损失函数包含两个部分：
 
-1. **SFT Loss**: The standard negative log-likelihood loss used in language modeling, which maximizes the probability of generating reference tokens. This helps maintain the model's general language capabilities.
+1. **SFT Loss**：这里使用标准的负对数似然函数，和 DPO 中的类似，用于扩大想要的 token 的生成概率。这个损失函数也有助于模型保持通用的语言能力。
+2. **Odds Ratio Loss**：这是新提出的损失函数。由于上述 SFT Loss 不能惩罚不想要的输出，所以这个函数在激励倾向的输出的同时，也惩罚不倾向的输出。具体来说，这里定义了计算优势比（Odds Ratio）的公式，通过抬高倾向输出和不倾向输出两者的优势比比值，在奖励倾向输出的同时，也压低不倾向输出的生成概率。
 
-2. **Odds Ratio Loss**: A novel component that penalizes undesirable responses while rewarding preferred ones. This loss function uses odds ratios to effectively contrast between favored and disfavored responses at the token level.
+在两个损失函数共同作用下，模型不仅被适配进了相应的任务领域，也压低了不倾向的回答的生成概率。其中，优势比这个机制提供了一个很直观的方法，模拟了倾向回答和不倾向回答之间的差异程度。你也可以阅读 [ORPO 的论文](https://arxiv.org/abs/2403.07691)，进一步了解其中的数学理论。如果你对具体的实现感兴趣，你可以阅读 [TRL 中关于这部分的实现](https://github.com/huggingface/trl/blob/b02189aaa538f3a95f6abb0ab46c0a971bfde57e/trl/trainer/orpo_trainer.py#L660)。
 
-Together, these components guide the model to adapt to desired generations for the specific domain while actively discouraging generations from the set of rejected responses. The odds ratio mechanism provides a natural way to measure and optimize the model's preference between chosen and rejected outputs. If you want to deep dive into the math, you can read the [ORPO paper](https://arxiv.org/abs/2403.07691). If you want to learn about ORPO from the implementation perspective, you should check out how loss for ORPO is calculated in the [TRL library](https://github.com/huggingface/trl/blob/b02189aaa538f3a95f6abb0ab46c0a971bfde57e/trl/trainer/orpo_trainer.py#L660).
+## 训练结果
 
-## Performance and Results
-
-ORPO has demonstrated impressive results across various benchmarks. On MT-Bench, it achieves competitive scores across different categories:
+ORPO 在很多测试基准上都取得了不错的效果。以下是它在 MT-Bench 测试基准上的结果（根据任务类别划分）：
 
 ![MT-Bench Results](https://argilla.io/images/blog/mantisnlp-rlhf/part-8-mtbench.png)
-*MT-Bench results by category for Mistral-ORPO models*
+*Mistral-ORPO 模型在 MT-Bench 不同任务领域的结果*
 
-When compared to other alignment methods, ORPO shows superior performance on AlpacaEval 2.0:
+在 AlpacaEval 2.0 上，ORPO 展现了超越其它对齐算法的效果：
 
 ![AlpacaEval Results](https://argilla.io/images/blog/mantisnlp-rlhf/part-8-winrate.png)
-*AlpacaEval 2.0 scores across different alignment methods*
+*不同对齐算法在 AlpacaEval 2.0 的得分*
 
-Compared to SFT+DPO, ORPO reduces computational requirements by eliminating the need for a reference model and halving the number of forward passes per batch. Also, the training process is more stable across different model sizes and datasets, requiring fewer hyperparameters to tune. Performance-wise, ORPO matches larger models while showing better alignment with human preferences.
+相较于 SFT 加 DPO 的做法，ORPO 通过去除参考模型、减半前向推理的策略，大大降低了计算资源的要求。同时，训练过程也更稳定，需要调节的超参数也更少。在性能上，ORPO 对人类偏好的适配做得也更好。
 
-## Implementation 
+## 代码实现
 
-Successful implementation of ORPO depends heavily on high-quality preference data. The training data should follow clear annotation guidelines and provide a balanced representation of preferred and rejected responses across diverse scenarios. 
+成功训练 ORPO 也极度依赖高质量数据集。所以标注训练数据时，我们也需要清晰明确的标注标准，确保对话场景的多样性，同时倾向和不倾向的回答需要分布均匀。
 
-### Implementation with TRL
+### 用 TRL 实现 ORPO
 
-ORPO can be implemented using the Transformers Reinforcement Learning (TRL) library. Here's a basic example:
+以下代码提供了用 TRL 实现 ORPO 的基本示例：
 
 ```python
 from trl import ORPOConfig, ORPOTrainer
@@ -68,17 +67,17 @@ trainer = ORPOTrainer(
 trainer.train()
 ```
 
-Key parameters to consider:
-- `orpo_alpha`: Controls the strength of preference optimization
-- `orpo_beta`: Temperature parameter for the odds ratio calculation
-- `learning_rate`: Should be relatively small to prevent catastrophic forgetting
-- `gradient_accumulation_steps`: Helps with training stability
+关键参数：
+- `orpo_alpha`：用来控制偏好优化部分的权重
+- `orpo_beta`：计算优势比（Odds Ratio）时的 Temperature 参数
+- `learning_rate`：这里需要用较小的学习率，用来防治灾难性遗忘（catastrophic forgetting）
+- `gradient_accumulation_steps`：调节这个也能稳定训练
 
-## Next Steps
+## 接下来的学习
 
-⏩ Try the [ORPO Tutorial](./notebooks/orpo_finetuning_example.ipynb) to implement this unified approach to preference alignment.
+⏩ 学习 [ORPO 教程](./notebooks/orpo_finetuning_example.ipynb) 来实践 ORPO 算法。
 
-## Resources
-- [ORPO Paper](https://arxiv.org/abs/2403.07691)
-- [TRL Documentation](https://huggingface.co/docs/trl/index)
+## 学习资源
+- [ORPO 论文](https://arxiv.org/abs/2403.07691)
+- [TRL 官方文档](https://huggingface.co/docs/trl/index)
 - [Argilla RLHF Guide](https://argilla.io/blog/mantisnlp-rlhf-part-8/) 
\ No newline at end of file
diff --git a/3_parameter_efficient_finetuning/README.md b/3_parameter_efficient_finetuning/README.md
index 7dabe6a8..15d13900 100644
--- a/3_parameter_efficient_finetuning/README.md
+++ b/3_parameter_efficient_finetuning/README.md
@@ -1,39 +1,39 @@
-# Parameter-Efficient Fine-Tuning (PEFT)
+# 高效的参数微调（PEFT）
 
-As language models grow larger, traditional fine-tuning becomes increasingly challenging. A full fine-tuning of even a 1.7B parameter model requires substantial GPU memory, makes storing separate model copies expensive, and risks catastrophic forgetting of the model's original capabilities. Parameter-efficient fine-tuning (PEFT) methods address these challenges by modifying only a small subset of model parameters while keeping most of the model frozen.
+随着语言模型越来越大，使用传统的模型微调方法去微调 LLM 已经变得越来越难了。举例来说，微调一个 1.7B 参数量的模型需要把所有参数都放进 GPU 显存、保存模型优化和状态信息，甚至还需要保存模型备份，这就需要很大的 GPU 显存使用了。同时，微调所有参数还有很大的“灾难性遗忘”风险，可能损失模型原有的能力。针对此问题，高效的参数微调（Parameter-efficient fine-tuning 或 PEFT）被提出。它在微调模型时，保留大部分参数不变，只微调一小部分参数，大大节省了计算资源的需求。
 
-Traditional fine-tuning updates all model parameters during training, which becomes impractical for large models. PEFT methods introduce approaches to adapt models using fewer trainable parameters - often less than 1% of the original model size. This dramatic reduction in trainable parameters enables:
+传统微调需要更新所有模型参数，对大模型很不现实。而 PEFT 相关方法则发现，仅更新一小部分参数，就足以对模型进行适配，达到微调所期待的效果。这部分需要更新的参数甚至还不到总参数量的 1%。这一重大改进使得以下操作成为可能：
 
-- Fine-tuning on consumer hardware with limited GPU memory
-- Storing multiple task-specific adaptations efficiently
-- Better generalization in low-data scenarios
-- Faster training and iteration cycles
+- 在 GPU 显存受限的消费级显卡上微调 LLM
+- 高效地为不同任务领域保存不同的微调结果
+- 在数据量不足的微调场景下也可保持很好的泛化性
+- 微调训练耗时更少
+  
+## PEFT 相关算法
 
-## Available Methods
-
-In this module, we will cover two popular PEFT methods:
+在本章教程中，我们主要讲解两种比较常用的 PEFT 算法：
 
 ### 1️⃣ LoRA (Low-Rank Adaptation)
 
-LoRA has emerged as the most widely adopted PEFT method, offering an elegant solution to efficient model adaptation. Instead of modifying the entire model, **LoRA injects trainable matrices into the model's attention layers.** This approach typically reduces trainable parameters by about 90% while maintaining comparable performance to full fine-tuning. We will explore LoRA in the [LoRA (Low-Rank Adaptation)](./lora_adapters.md) section.
- 
+LoRA 可以说是最常用的 PEFT 算法了，它为高效模型微调提供了一个非常优雅的解决方案。LoRA 在需要更新的参数（一般是 attention layers 的参数）上插入可以训练的参数矩阵，训练时仅训练这部分参数。当模型训练好后，我们会利用这部分参数对原有模型进行重参数化（re-parameterization）。这样可以不改变 LLM 的整体结构和参数数量。通过这种方法，需要更新的参数量能至少减少 90%，同时性能也不差于全量参数微调。我们将在 [LoRA 低秩分解](./lora_adapters_cn.md)部分进一步讲解。
+
 ### 2️⃣ Prompt Tuning
 
-Prompt tuning offers an **even lighter** approach by **adding trainable tokens to the input** rather than modifying model weights. Prompt tuning is less popular than LoRA, but can be a useful technique for quickly adapting a model to new tasks or domains. We will explore prompt tuning in the [Prompt Tuning](./prompt_tuning.md) section.
+Prompt Tuning 则更加轻量化。它通过在输入部分加入**可训练的 token** 来微调，而不是改变模型的参数。Prompt Tuning 没有 LoRA 那么常用，但对于适配模型到新的任务领域来说，是个非常有用的技术。我们将在 [Prompt Tuning](./prompt_tuning_cn.md) 部分进一步讲解。
 
-## Exercise Notebooks
+## 实践练习
 
-| Title | Description | Exercise | Link | Colab |
+| 标题 | 简介 | 习题 | 链接 | Colab |
 |-------|-------------|----------|------|-------|
-| LoRA Fine-tuning | Learn how to fine-tune models using LoRA adapters | 🐢 Train a model using LoRA<br>🐕 Experiment with different rank values<br>🦁 Compare performance with full fine-tuning | [Notebook](./notebooks/finetune_sft_peft.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-| Load LoRA Adapters | Learn how to load and use trained LoRA adapters | 🐢 Load pre-trained adapters<br>🐕 Merge adapters with base model<br>🦁 Switch between multiple adapters | [Notebook](./notebooks/load_lora_adapter.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+| LoRA 微调 | 学习使用 LoRA adapters 微调模型 | 🐢 用 LoRA 训练一个模型 <br>🐕 试验不同低秩值的效果 <br>🦁 与全量参数微调的效果进行对比 | [Notebook](./notebooks/finetune_sft_peft.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+| 载入 LoRA Adapter | 学习如何加载并使用 LoRA adapters | 🐢 加载训练好的 adapter<br>🐕 将 adapter 融入原有模型中 <br>🦁 实现不同 adapter 的切换 | [Notebook](./notebooks/load_lora_adapter.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
 <!-- | Prompt Tuning | Learn how to implement prompt tuning | 🐢 Train soft prompts<br>🐕 Compare different initialization strategies<br>🦁 Evaluate on multiple tasks | [Notebook](./notebooks/prompt_tuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/prompt_tuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | -->
 
-## Resources
-- [PEFT Documentation](https://huggingface.co/docs/peft)
-- [LoRA Paper](https://arxiv.org/abs/2106.09685)
-- [QLoRA Paper](https://arxiv.org/abs/2305.14314)
-- [Prompt Tuning Paper](https://arxiv.org/abs/2104.08691)
-- [Hugging Face PEFT Guide](https://huggingface.co/blog/peft)
-- [How to Fine-Tune LLMs in 2024 with Hugging Face](https://www.philschmid.de/fine-tune-llms-in-2024-with-trl) 
-- [TRL](https://huggingface.co/docs/trl/index)
+## 参考资料
+- [PEFT 代码库官方文档](https://huggingface.co/docs/peft)
+- [LoRA 论文](https://arxiv.org/abs/2106.09685)
+- [QLoRA 论文](https://arxiv.org/abs/2305.14314)
+- [Prompt Tuning 论文](https://arxiv.org/abs/2104.08691)
+- [Hugging Face PEFT 代码库相关博客](https://huggingface.co/blog/peft)
+- [文章：How to Fine-Tune LLMs in 2024 with Hugging Face](https://www.philschmid.de/fine-tune-llms-in-2024-with-trl) 
+- [TRL 代码库官方文档](https://huggingface.co/docs/trl/index)
diff --git a/3_parameter_efficient_finetuning/lora_adapters.md b/3_parameter_efficient_finetuning/lora_adapters.md
index bc41b85c..147cd872 100644
--- a/3_parameter_efficient_finetuning/lora_adapters.md
+++ b/3_parameter_efficient_finetuning/lora_adapters.md
@@ -1,16 +1,16 @@
-# LoRA (Low-Rank Adaptation)
+# LoRA（低秩分解）
 
-LoRA has become the most widely adopted PEFT method. It works by adding small rank decomposition matrices to the attention weights, typically reducing trainable parameters by about 90%. 
+LoRA 是最常用的 PEFT 算法。它针对 attention 层的权重加入参数量较少、低秩分解过的参数矩阵，用模型原有参数和低秩分解参数计算出的激活值之和代表微调过后的激活值。这样当我们只更新低秩分解过的参数矩阵时，我们需要训练的参数量能减少大约 90%。
 
-## Understanding LoRA
+## 理解 LoRA
 
-LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning technique that freezes the pre-trained model weights and injects trainable rank decomposition matrices into the model's layers. Instead of training all model parameters during fine-tuning, LoRA decomposes the weight updates into smaller matrices through low-rank decomposition, significantly reducing the number of trainable parameters while maintaining model performance. For example, when applied to GPT-3 175B, LoRA reduced trainable parameters by 10,000x and GPU memory requirements by 3x compared to full fine-tuning. You can read more about LoRA in the [LoRA paper](https://arxiv.org/pdf/2106.09685).
+LoRA 全称是 Low-Rank Adaptation，或叫做“低秩分解”。它的基本做法是，在微调时，冻结所有预训练模型的参数，同时为需要微调的模型层注入额外的可训练的参数矩阵（通常称之为 Adapter）。通过对需要微调的层的参数矩阵进行低秩分解，可以得到两个参数量较小的新参数矩阵；而这一层的前向计算激活值则可以用“原有参数矩阵计算出的激活值”加上“低秩分解出的两个矩阵计算出的激活值”而得到。训练时，我们只需要训练两个低秩分解的矩阵即可，这样极大减少了所需微调的参数量，同时也能保持原有模型性能。例如，如果我们用 LoRA 微调 GPT-3 175B 模型，相比于全量参数的微调，LoRA 需要参与训练的参数量可减少至万分之一、GPU 现存需求可减少至三分之一。感兴趣的读者可以阅读 [LoRA 的论文](https://arxiv.org/pdf/2106.09685)。
 
-LoRA works by adding pairs of rank decomposition matrices to transformer layers, typically focusing on attention weights. During inference, these adapter weights can be merged with the base model, resulting in no additional latency overhead. LoRA is particularly useful for adapting large language models to specific tasks or domains while keeping resource requirements manageable.
+一般而言，LoRA 都是对 transformer 层的参数进行低秩分解的，尤其是在与注意力机制相关的参数上。在推理过程中，这些 adapter 的参数可以被直接融合进模型中，得到与原模型结构完全一致的新模型，无需增加新的层。得益于此，LoRA 尤其适合在低计算资源情况下，将大模型适配进入特定的任务领域。
 
-## Loading LoRA Adapters
+## 如何载入 LoRA 的 Adapters
 
-Adapters can be loaded onto a pretrained model with load_adapter(), which is useful for trying out different adapters whose weights aren’t merged. Set the active adapter weights with the set_adapter() function. To return the base model, you could use unload() to unload all of the LoRA modules. This makes it easy to switch between different task-specific weights.
+如果你使用 `peft` 库，你可以用 `load_adapter()` 载入 LoRA Adapters。这对你尝试不同的 adapter 非常管用，因为它还不会将参数融合进原模型。你可以使用 `set_adapter()` 指定哪个 LoRA Adapter 在生效。如果想返回原模型，你可以使用 `unload()` 卸载所有 LoRA 参数。这种设定使得在不同任务领域间切换模型变得非常容易。
 
 ```python
 from transformers import AutoModelForCausalLM
@@ -23,27 +23,25 @@ model = PeftModel.from_pretrained(base_model, peft_model_id)
 
 ![lora_load_adapter](./images/lora_adapter.png)
 
-## Merging LoRA Adapters
+## 将 LoRA 参数融入原模型
 
-After training with LoRA, you might want to merge the adapter weights back into the base model for easier deployment. This creates a single model with the combined weights, eliminating the need to load adapters separately during inference.
+如果在 LoRA 微调结束后，你想直接获取一套新的模型参数，而不是每次使用的时候都需要加载 LoRA 的 Adapter，你可以直接将 LoRA 的参数融入原模型中。
 
-The merging process requires attention to memory management and precision. Since you'll need to load both the base model and adapter weights simultaneously, ensure sufficient GPU/CPU memory is available. Using `device_map="auto"` in `transformers` will help with automatic memory management. Maintain consistent precision (e.g., float16) throughout the process, matching the precision used during training and saving the merged model in the same format for deployment. Before deploying, always validate the merged model by comparing its outputs and performance metrics with the adapter-based version.
+融合的时候，我们首先需要注意内存的管理以及参数的精度。因为我们要同时载入原模型和 LoRA 参数，需要注意 GPU 或 CPU 的内存是否够用。在 `transformers` 中使用 `device_map="auto"` 可以替我们自动进行内存管理。同时，要注意原模型、LoRA 参数的精度需保持一致。融合后，检查模型输出是否和未融合是一致也很重要。
 
-Adapters are also be convenient for switching between different tasks or domains. You can load the base model and adapter weights separately. This allows for quick switching between different task-specific weights. 
+## 代码实现
 
-## Implementation Guide
+在 `notebooks/` 目录下，有 PEFT 相关方法的实践教程以及练习题。我们首先会在 `load_lora_adapter_example.ipynb` 学习加载 LoRA Adapter 相关的内容，然后在 `lora_finetuning.ipynb` 中，我们将学习如果用 LoRA 进行 SFT。
 
-The `notebooks/` directory contains practical tutorials and exercises for implementing different PEFT methods. Begin with `load_lora_adapter_example.ipynb` for a basic introduction, then explore `lora_finetuning.ipynb` for a more detailed look at how to fine-tune a model with LoRA and SFT.
-
-When implementing PEFT methods, start with small rank values (4-8) for LoRA and monitor training loss. Use validation sets to prevent overfitting and compare results with full fine-tuning baselines when possible. The effectiveness of different methods can vary by task, so experimentation is key.
+一个比较好的 LoRA 训练流程应该是，首先从较低的秩开始，一般是 4 到 8，同时观察训练损失值。使用验证集及时查看避免过拟合。不同任务可能有较大差异，所有还是要以实际实验现象为准。
 
 ## OLoRA
 
-[OLoRA](https://arxiv.org/abs/2406.01775) utilizes QR decomposition to initialize the LoRA adapters. OLoRA translates the base weights of the model by a factor of their QR decompositions, i.e., it mutates the weights before performing any training on them. This approach significantly improves stability, accelerates convergence speed, and ultimately achieves superior performance.
+[OLoRA](https://arxiv.org/abs/2406.01775) 使用 QR 分解来初始化 LoRA 的 Adapter。该算法对原有的参数矩阵 W 分解为 Q 和 R 两个矩阵，其中 Q 矩阵包含 W 矩阵的 r 个正交向量，使得优化能够在一个较好的子空间进行。这样可以很大地提升收敛速度，同时也达到了非常好的效果。
 
-## Using TRL with PEFT
+##  TRL 与 PEFT 结合使用
 
-PEFT methods can be combined with TRL (Transformers Reinforcement Learning) for efficient fine-tuning. This integration is particularly useful for RLHF (Reinforcement Learning from Human Feedback) as it reduces memory requirements.
+PEFT 也可以和 TRL 库一起使用，这对 RLHF（Reinforcement Learning from Human Feedback）尤其实用。
 
 ```python
 from peft import LoraConfig
@@ -67,11 +65,14 @@ model = AutoModelForCausalLM.from_pretrained(
 )
 ```
 
-Above, we used `device_map="auto"` to automatically assign the model to the correct device. You can also manually assign the model to a specific device using `device_map={"": device_index}`. You could also scale training across multiple GPUs while keeping memory usage efficient.
+在上述代码中，我们用 `device_map="auto"` 自动分配模型到正确的计算设备上。关于具体计算设备，你也可以手动修改：`device_map={"": device_index}`；当然你也可以扩大训练规模，如实用多 GPU 训练等。
+
+## 基本的参数融合实现
+
 
-## Basic Merging Implementation
 
-After training a LoRA adapter, you can merge the adapter weights back into the base model. Here's how to do it:
+训练好 LoRA adapter 后，将权重融合回原模型的方法如下：
+
 
 ```python
 import torch
@@ -103,7 +104,7 @@ except RuntimeError as e:
 merged_model.save_pretrained("path/to/save/merged_model")
 ```
 
-If you encounter size discrepancies in the saved model, ensure you're also saving the tokenizer:
+保存的时候，你可能也需要保存 tokenizer 到相应目录。
 
 ```python
 # Save both model and tokenizer
@@ -112,13 +113,14 @@ merged_model.save_pretrained("path/to/save/merged_model")
 tokenizer.save_pretrained("path/to/save/merged_model")
 ```
 
-## Next Steps
+## 接下来
+
+⏩ 继续学习 [Prompt Tuning](prompt_tuning_cn.md)，了解这种微调方式如何运作。
 
-⏩ Move on to the [Prompt Tuning](prompt_tuning.md) guide to learn how to fine-tune a model with prompt tuning.
-⏩ Move on the [Load LoRA Adapters Tutorial](./notebooks/load_lora_adapter.ipynb) to learn how to load LoRA adapters.
+⏩ 实践 [加载 LoRA Adapters 的教程](./notebooks/load_lora_adapter_cn.ipynb) 练习加载 LoRA adapters。
 
-# Resources
+# 学习资源
 
 - [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/pdf/2106.09685)
-- [PEFT Documentation](https://huggingface.co/docs/peft)
-- [Hugging Face blog post on PEFT](https://huggingface.co/blog/peft)
+- [PEFT 官方文档](https://huggingface.co/docs/peft)
+- [Hugging Face 有关 PEFT 的博客](https://huggingface.co/blog/zh/peft)
diff --git a/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb b/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
index 9028d477..4e0e0bd2 100644
--- a/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
+++ b/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
@@ -6,30 +6,37 @@
     "id": "z-6LLOPZouLg"
    },
    "source": [
-    "# How to Fine-Tune LLMs with LoRA Adapters using Hugging Face TRL\n",
-    "\n",
-    "This notebook demonstrates how to efficiently fine-tune large language models using LoRA (Low-Rank Adaptation) adapters. LoRA is a parameter-efficient fine-tuning technique that:\n",
-    "- Freezes the pre-trained model weights\n",
-    "- Adds small trainable rank decomposition matrices to attention layers\n",
-    "- Typically reduces trainable parameters by ~90%\n",
-    "- Maintains model performance while being memory efficient\n",
-    "\n",
-    "We'll cover:\n",
-    "1. Setup development environment and LoRA configuration\n",
-    "2. Create and prepare the dataset for adapter training\n",
-    "3. Fine-tune using `trl` and `SFTTrainer` with LoRA adapters\n",
-    "4. Test the model and merge adapters (optional)\n"
+    "# 在 TRL 框架下用 LoRA 微调大语言模型\n",
+    "\n",
+    "这个 notebook 展示如何用 LoRA 高效微调大语言模型。LoRA 是一种高效的参数微调方法，有如下优点：\n",
+    "- 不更新预训练模型权重\n",
+    "- 仅在注意力层添加少量低秩分解矩阵作为训练参数\n",
+    "- 基本能减少 90% 训练参数\n",
+    "- 能保留模型原有的能力\n",
+    "\n",
+    "本文涵盖这些步骤：\n",
+    "1. 配置开发环境、设定 LoRA 相关配置\n",
+    "2. 准备数据集\n",
+    "3. 使用 `trl` 框架下的 `SFTTrainer` 进行 LoRA 微调\n",
+    "4. 测试模型性能、学习加载 adapter\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {
     "id": "fXqd9BXgouLi"
    },
    "source": [
-    "## 1. Setup development environment\n",
+    "## 1. 配置开发环境\n",
     "\n",
-    "Our first step is to install Hugging Face Libraries and Pytorch, including trl, transformers and datasets. If you haven't heard of trl yet, don't worry. It is a new library on top of transformers and datasets, which makes it easier to fine-tune, rlhf, align open LLMs.\n"
+    "我们首先需要安装 PyTorch 和 Hugging Face 相关的库，这包括 `trl`、`transformers`、`datasets`。其中 `trl` 基于 `transformers` 和 `datasets`，用以微调模型、进行 RLHF、对齐 LLM 等。"
    ]
   },
   {
@@ -58,7 +65,7 @@
     "id": "XHUzfwpKouLk"
    },
    "source": [
-    "## 2. Load the dataset"
+    "## 2. 载入数据集"
    ]
   },
   {
@@ -92,7 +99,7 @@
     "# Load a sample dataset\n",
     "from datasets import load_dataset\n",
     "\n",
-    "# TODO: define your dataset and config using the path and name parameters\n",
+    "# TODO: 你也可以用自己的数据集\n",
     "dataset = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")\n",
     "dataset"
    ]
@@ -103,27 +110,26 @@
     "id": "9TOhJdtsouLk"
    },
    "source": [
-    "## 3. Fine-tune LLM using `trl` and the `SFTTrainer` with LoRA\n",
-    "\n",
-    "The [SFTTrainer](https://huggingface.co/docs/trl/sft_trainer) from `trl` provides integration with LoRA adapters through the [PEFT](https://huggingface.co/docs/peft/en/index) library. Key advantages of this setup include:\n",
-    "\n",
-    "1. **Memory Efficiency**: \n",
-    "   - Only adapter parameters are stored in GPU memory\n",
-    "   - Base model weights remain frozen and can be loaded in lower precision\n",
-    "   - Enables fine-tuning of large models on consumer GPUs\n",
-    "\n",
-    "2. **Training Features**:\n",
-    "   - Native PEFT/LoRA integration with minimal setup\n",
-    "   - Support for QLoRA (Quantized LoRA) for even better memory efficiency\n",
-    "\n",
-    "3. **Adapter Management**:\n",
-    "   - Adapter weight saving during checkpoints\n",
-    "   - Features to merge adapters back into base model\n",
-    "\n",
-    "We'll use LoRA in our example, which combines LoRA with 4-bit quantization to further reduce memory usage without sacrificing performance. The setup requires just a few configuration steps:\n",
-    "1. Define the LoRA configuration (rank, alpha, dropout)\n",
-    "2. Create the SFTTrainer with PEFT config\n",
-    "3. Train and save the adapter weights\n"
+    "## 3. 在 `trl` 框架下用 `SFTTrainer` 实现大语言模型的 LoRA 微调\n",
+    "\n",
+    "在 `trl` 中，[SFTTrainer](https://huggingface.co/docs/trl/sft_trainer) 通过 [PEFT](https://huggingface.co/docs/peft/en/index) 提供了 LoRA Adapter 的集成。这样的设定有以下几个好处：\n",
+    "\n",
+    "1. **高效利用内存**：\n",
+    "   - 仅 Adapter 的参数会保存在 GPU 显存中\n",
+    "   - 原模型参数被冻结，所以可以用低精度载入\n",
+    "   - 这使得在消费级显卡上也可以微调大模型\n",
+    "2. **训练层面**：\n",
+    "   - 原生 PEFT/LoRA 集成，用户开发所需代码量少\n",
+    "   - 支持 QLoRA（量化版 LoRA），可以进一步减少内存使用\n",
+    "\n",
+    "3. **Adapter 管理**：\n",
+    "   - 可以方便地训练过程中保存 Adapter\n",
+    "   - 可以方便地把 Adapter 融合进原模型\n",
+    "\n",
+    "本文将会进行 LoRA 微调，当然你也可以尝试 4-bit 量化来进一步减少内存使用。配置步骤包含以下几步：\n",
+    "1. 定义好 LoRA 的相关参数（主要是 rank、alpha、dropout）\n",
+    "2. 创建一个 SFTTrainer 的实例\n",
+    "3. 训练模型、保存 adapter 的参数"
    ]
   },
   {
@@ -166,15 +172,15 @@
     "id": "ZbuVArTHouLk"
    },
    "source": [
-    "The `SFTTrainer`  supports a native integration with `peft`, which makes it super easy to efficiently tune LLMs using, e.g. LoRA. We only need to create our `LoraConfig` and provide it to the trainer.\n",
+    "由于 `SFTTrainer`  原生支持 `peft`，使用 LoRA 训练 LLM 就变得非常简单。我们需要配置的只有 `LoraConfig`。\n",
     "\n",
     "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Exercise: Define LoRA parameters for finetuning</h2>\n",
-    "    <p>Take a dataset from the Hugging Face hub and finetune a model on it. </p> \n",
-    "    <p><b>Difficulty Levels</b></p>\n",
-    "    <p>🐢 Use the general parameters for an abitrary finetune</p>\n",
-    "    <p>🐕 Adjust the parameters and review in weights & biases.</p>\n",
-    "    <p>🦁 Adjust the parameters and show change in inference results.</p>\n",
+    "    <h2 style='margin: 0;color:blue'>练习：为微调定义好 LoRA 相关参数</h2>\n",
+    "    <p>从 Hugging Face hub 找一个合适的数据然后微调模型 </p> \n",
+    "    <p><b>难度等级</b></p>\n",
+    "    <p>🐢 使用默认的 LoRA 超参数直接微调</p>\n",
+    "    <p>🐕 改变一些超参数，学习通过 weights & biases 平台查看</p>\n",
+    "    <p>🦁 改变一些超参数，训练后查看这些改变是否影响了推理性能</p>\n",
     "</div>"
    ]
   },
@@ -188,20 +194,20 @@
    "source": [
     "from peft import LoraConfig\n",
     "\n",
-    "# TODO: Configure LoRA parameters\n",
-    "# r: rank dimension for LoRA update matrices (smaller = more compression)\n",
+    "# TODO: 配置 LoRA 参数\n",
+    "# r: 低秩分解的秩，越小则需要训练的参数量越少\n",
     "rank_dimension = 6\n",
-    "# lora_alpha: scaling factor for LoRA layers (higher = stronger adaptation)\n",
+    "# lora_alpha: 将训练好的参数加到原模型上时的缩放倍数，越大则微调参数作用越明显\n",
     "lora_alpha = 8\n",
-    "# lora_dropout: dropout probability for LoRA layers (helps prevent overfitting)\n",
+    "# lora_dropout: LoRA 相关层的 dropout，可以用来应对过拟合\n",
     "lora_dropout = 0.05\n",
     "\n",
     "peft_config = LoraConfig(\n",
-    "    r=rank_dimension,  # Rank dimension - typically between 4-32\n",
-    "    lora_alpha=lora_alpha,  # LoRA scaling factor - typically 2x rank\n",
+    "    r=rank_dimension,  # 一般选择 4 到 32\n",
+    "    lora_alpha=lora_alpha,  # 一般是 rank 的 2 倍\n",
     "    lora_dropout=lora_dropout,  # Dropout probability for LoRA layers\n",
     "    bias=\"none\",  # Bias type for LoRA. the corresponding biases will be updated during training.\n",
-    "    target_modules=\"all-linear\",  # Which modules to apply LoRA to\n",
+    "    target_modules=\"all-linear\",  # 模型哪些层会添加 LoRA Adapter\n",
     "    task_type=\"CAUSAL_LM\",  # Task type for model architecture\n",
     ")"
    ]
@@ -212,7 +218,7 @@
     "id": "l5NUDPcaouLl"
    },
    "source": [
-    "Before we can start our training we need to define the hyperparameters (`TrainingArguments`) we want to use."
+    "此外我们还需定义训练的超参数（`TrainingArguments`）。"
    ]
   },
   {
@@ -249,7 +255,7 @@
     "    bf16=True,  # Use bfloat16 precision\n",
     "    # Integration settings\n",
     "    push_to_hub=False,  # Don't push to HuggingFace Hub\n",
-    "    report_to=\"none\",  # Disable external logging\n",
+    "    report_to=None,  # Disable external logging\n",
     ")"
    ]
   },
@@ -259,7 +265,7 @@
     "id": "cGhR7uFBouLl"
    },
    "source": [
-    "We now have every building block we need to create our `SFTTrainer` to start then training our model."
+    "配置完毕，我们可以创建 `SFTTrainer` 来训练模型了。"
    ]
   },
   {
@@ -294,7 +300,7 @@
     "id": "zQ_kRN24ouLl"
    },
    "source": [
-    "Start training our model by calling the `train()` method on our `Trainer` instance. This will start the training loop and train our model for 3 epochs. Since we are using a PEFT method, we will only save the adapted model weights and not the full model."
+    "通过启动 `train()` 函数，我们开始训练。本次训练包含 3 个 epoch。由于我们用了 PEFT，我们可以在训练过程中或结束后，只保存 adapter 的参数，无需保存原模型参数。"
    ]
   },
   {
@@ -343,8 +349,7 @@
     "id": "y4HHSYYzouLl"
    },
    "source": [
-    "The training with Flash Attention for 3 epochs with a dataset of 15k samples took 4:14:36 on a `g5.2xlarge`. The instance costs `1.21$/h` which brings us to a total cost of only ~`5.3$`.\n",
-    "\n"
+    "我们模型使用了 Flash Attention 加速训练。在当前数据集（15k 的样本量）训练了 3 轮，在一个 `g5.2xlarge` 机器上用了 4 小时 14 分钟 36 秒。该机器报价 `1.21$/h`，所以我们总花费仅 `5.3$`。\n"
    ]
   },
   {
@@ -353,13 +358,12 @@
     "id": "C309KsXjouLl"
    },
    "source": [
-    "### Merge LoRA Adapter into the Original Model\n",
+    "### 将 LoRA Adapter 融入原模型\n",
     "\n",
-    "When using LoRA, we only train adapter weights while keeping the base model frozen. During training, we save only these lightweight adapter weights (~2-10MB) rather than a full model copy. However, for deployment, you might want to merge the adapters back into the base model for:\n",
-    "\n",
-    "1. **Simplified Deployment**: Single model file instead of base model + adapters\n",
-    "2. **Inference Speed**: No adapter computation overhead\n",
-    "3. **Framework Compatibility**: Better compatibility with serving frameworks\n"
+    "训练 LoRA 时，我们只训练 adapter 里的参数，而不训练原模型。所以保存的参数也只有 adapter 里的参数（可能也就 2MB 到 10MB）。然而在部署阶段，你可能需要把 Adapter 融合进原模型：\n",
+    "1. **简化的部署流程**：仅载入一个模型参数文件即可，无需额外载入 adapter 参数文件\n",
+    "2. **推理速度提升**：adapter 引入的计算已经融合进了模型中\n",
+    "3. **框架的兼容性**：更能和服务框架适配"
    ]
   },
   {
@@ -391,20 +395,9 @@
     "id": "-yO6E9quouLl"
    },
    "source": [
-    "## 3. Test Model and run Inference\n",
+    "## 3. 测试模型、进行推理\n",
     "\n",
-    "After the training is done we want to test our model. We will load different samples from the original dataset and evaluate the model on those samples, using a simple loop and accuracy as our metric.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Bonus Exercise: Load LoRA Adapter</h2>\n",
-    "    <p>Use what you learnt from the ecample note book to load your trained LoRA adapter for inference.</p> \n",
-    "</div>"
+    "训练结束后，我们可能需要测试模型。可以从数据集找一些样本，然后看看模型在这些样本上的性能。"
    ]
   },
   {
@@ -449,7 +442,7 @@
     "id": "99uFDAuuouLl"
    },
    "source": [
-    "Lets test some prompt samples and see how the model performs."
+    "现在我们找些样本来测试："
    ]
   },
   {
@@ -486,6 +479,16 @@
     "    print(f\"    response:\\n{test_inference(prompt)}\")\n",
     "    print(\"-\" * 50)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>额外练习：载入自己训练的 LoRA Adapter</h2>\n",
+    "    <p>结束本教程后，你可以使用学到的技术，自己训练一个 LoRA，然后载入 LoRA Adapter</p> \n",
+    "</div>"
+   ]
   }
  ],
  "metadata": {
diff --git a/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb b/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
index b288a4a1..b879831b 100644
--- a/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
+++ b/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
@@ -156,7 +156,7 @@
         "id": "S65GcxNGA9kz"
       },
       "source": [
-        "## Load adapters from the Hub\n",
+        "## 从 HuggingFace Hub 加载 adapters\n",
         "\n"
       ]
     },
@@ -214,7 +214,7 @@
         ")\n",
         "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
         "\n",
-        "# Load the Lora model\n",
+        "# 载入 Lora \n",
         "model = PeftModel.from_pretrained(model, peft_model_id)"
       ]
     },
diff --git a/3_parameter_efficient_finetuning/prompt_tuning.md b/3_parameter_efficient_finetuning/prompt_tuning.md
index 66926167..2372354c 100644
--- a/3_parameter_efficient_finetuning/prompt_tuning.md
+++ b/3_parameter_efficient_finetuning/prompt_tuning.md
@@ -1,22 +1,22 @@
 # Prompt Tuning
 
-Prompt tuning is a parameter-efficient approach that modifies input representations rather than model weights. Unlike traditional fine-tuning that updates all model parameters, prompt tuning adds and optimizes a small set of trainable tokens while keeping the base model frozen.
+Prompt tuning 也是一个高效的微调手段，不同于微调模型参数，它改变的是输入模型的表征。具体来说，prompt tuning 在训练前会添加几个额外的 token，训练过程中，token 的 embedding 被更新，而模型参数一直保持不变。
 
-## Understanding Prompt Tuning
+## 理解 Prompt Tuning
 
-Prompt tuning is a parameter-efficient alternative to model fine-tuning that prepends trainable continuous vectors (soft prompts) to the input text. Unlike discrete text prompts, these soft prompts are learned through backpropagation while keeping the language model frozen. The method was introduced in ["The Power of Scale for Parameter-Efficient Prompt Tuning"](https://arxiv.org/abs/2104.08691) (Lester et al., 2021), which demonstrated that prompt tuning becomes more competitive with model fine-tuning as model size increases. Within the paper, at around 10 billion parameters, prompt tuning matches the performance of model fine-tuning while only modifying a few hundred parameters per task.
+Prompt tuning 通常把可以训练的连续向量（也称为soft prompts）接在输入文本的前面。与离散的文本提示词不同，这些 soft prompts 是通过训练过程中经反向传播更新的，而语言模型则在训练中不变。该方法在 [The Power of Scale for Parameter-Efficient Prompt Tuning](https://arxiv.org/abs/2104.08691) 中提出，展示了其在模型尺寸增大时的有效性：当模型尺寸在 10B 参数量左右时，prompt tuning 只更新 soft prompts 的几百个参数，即可达到模型全量微调的效果。
 
-These soft prompts are continuous vectors in the model's embedding space that get optimized during training. Unlike traditional discrete prompts that use natural language tokens, soft prompts have no inherent meaning but learn to elicit the desired behavior from the frozen model through gradient descent. The technique is particularly effective for multi-task scenarios since each task requires storing only a small prompt vector (typically a few hundred parameters) rather than a full model copy. This approach not only maintains a minimal memory footprint but also enables rapid task switching by simply swapping prompt vectors without any model reloading.
+Soft prompts 是模型的 embedding 空间中的一些连续数值的向量，它们会在微调过程中被更新。传统的 prompts 是一些离散的 tokens，在自然语言层面代表某些语义信息；soft prompt 则没有这些内在含义，经过参数更新后，它被用来从模型中引出一些特定行为。这种方法在多任务领域尤其有效，因为每个任务仅需保存一个 soft prompt 的向量（通常仅几百个参数），而不是对整个模型复制。这种做法不仅现存占用少，而且还能支持快速的任务切换，无需模型重新加载。
 
-## Training Process
+## 训练过程
 
-Soft prompts typically number between 8 and 32 tokens and can be initialized either randomly or from existing text. The initialization method plays a crucial role in the training process, with text-based initialization often performing better than random initialization.
+Soft prompts 一般包含 8 到 32 个 tokens，它的初始化可以是随机初始化，也可以是来自现有的文本。初始化过程很影响训练，后者的方法通常效果更好。
 
-During training, only the prompt parameters are updated while the base model remains frozen. This focused approach uses standard training objectives but requires careful attention to the learning rate and gradient behavior of the prompt tokens.
+训练过程中，仅这些 soft prompts 的参数会被更新；训练用的损失函数也没有变化；但学习率需要我们认真调节，同时也建议观察 soft prompts 上面的梯度信息，以免训练失败。
 
-## Implementation with PEFT
+## 基于 PEFT 的代码实现
 
-The PEFT library makes implementing prompt tuning straightforward. Here's a basic example:
+使用 PEFT 库实现 prompt tuning 非常简单直接，以下是一个简单的例子：
 
 ```python
 from peft import PromptTuningConfig, TaskType, get_peft_model
@@ -29,8 +29,8 @@ tokenizer = AutoTokenizer.from_pretrained("your-base-model")
 # Configure prompt tuning
 peft_config = PromptTuningConfig(
     task_type=TaskType.CAUSAL_LM,
-    num_virtual_tokens=8,  # Number of trainable tokens
-    prompt_tuning_init="TEXT",  # Initialize from text
+    num_virtual_tokens=8,  # soft prompts 的 token 数量
+    prompt_tuning_init="TEXT",  # 初始化方法为从文本初始化
     prompt_tuning_init_text="Classify if this text is positive or negative:",
     tokenizer_name_or_path="your-base-model",
 )
@@ -39,36 +39,36 @@ peft_config = PromptTuningConfig(
 model = get_peft_model(model, peft_config)
 ```
 
-## Comparison to Other Methods
+## 与其它方法的对比
 
-When compared to other PEFT approaches, prompt tuning stands out for its efficiency. While LoRA offers low parameter counts and memory usage but requires loading adapters for task switching, prompt tuning achieves even lower resource usage and enables immediate task switching. Full fine-tuning, in contrast, demands significant resources and requires separate model copies for different tasks.
+对比与其它 PEFT 方法，prompt tuning 胜在它的高效性。虽然 LoRA 也减少了训练参数以及所需显存，但反复加载 adapter 来切换任务就很麻烦。Prompt tuning 需要的训练参数更少，且任务切换更加方便。而全量参数微调则既需要超大的训练资源，也需要通过全量参数重新载入来切换任务。
 
-| Method | Parameters | Memory | Task Switching |
+| 方法 | 训练参数量 | 显存需求 | 任务切换难度 |
 |--------|------------|---------|----------------|
-| Prompt Tuning | Very Low | Minimal | Easy |
-| LoRA | Low | Low | Requires Loading |
-| Full Fine-tuning | High | High | New Model Copy |
+| Prompt Tuning | 很低 | 很低 | 非常简单 |
+| LoRA | 低 | 低 | 需要加载 Adapter |
+| 全量参数微调 | 很高 | 很高 | 加载所有参数 |
 
-When implementing prompt tuning, start with a small number of virtual tokens (8-16) and increase only if the task complexity demands it. Text initialization typically yields better results than random initialization, especially when using task-relevant text. The initialization strategy should reflect the complexity of your target task.
+在实际 prompt tuning 训练过程中，建议先从较小的 virtual tokens 数量开始（如 8 到 16 个），仅当任务复杂度增加时，再增加 virtual tokens 数量。从文本初始化通常比随机初始化更好，尤其是你使用和任务相关的文本时。初始化方法需要和你的任务复杂度匹配。
 
-Training requires slightly different considerations than full fine-tuning. Higher learning rates often work well, but careful monitoring of prompt token gradients is essential. Regular validation on diverse examples helps ensure robust performance across different scenarios.
+训练过程中，你还需注意学习率。如果使用较大的学习率，你需要时刻观察 soft prompt 更新时的梯度信息，以防训练崩溃。训练过程中，定期的验证也是确保性能的良好手段。
 
-## Application
+## 应用
 
-Prompt tuning excels in several scenarios:
+Prompt tuning 在这些场景下优势明显：
 
-1. Multi-task deployment
-2. Resource-constrained environments
-3. Rapid task adaptation
-4. Privacy-sensitive applications
+1. 多任务下的大语言模型部署
+2. 计算资源有限的训练场景
+3. 需要在不同任务间快速切换的场景
+4. 针对隐私敏感的应用场景
 
-As models get smaller, prompt tuning becomes less competitive compared to full fine-tuning. For example, on models like SmolLM2 scales prompt tuning is less relevant than full fine-tuning. 
+而当模型变小时，prompt tuning 就没有那么有竞争力了。比如在 SmolLM2 这样的模型尺寸下，prompt tuning 的意义就不大，可能还不如全量微调。
 
-## Next Steps
+## 接下来
 
-⏭️ Move on to the [LoRA Adapters Tutorial](./notebooks/finetune_sft_peft.ipynb) to learn how to fine-tune a model with LoRA adapters.
+⏭️ 学习 [LoRA Adapters 的教程](./notebooks/finetune_sft_peft.ipynb)，了解如何实操 LoRA 微调模型的过程。
 
-## Resources
-- [PEFT Documentation](https://huggingface.co/docs/peft)
-- [Prompt Tuning Paper](https://arxiv.org/abs/2104.08691)
-- [Hugging Face Cookbook](https://huggingface.co/learn/cookbook/prompt_tuning_peft)
+## 学习资源
+- [PEFT 官方文档](https://huggingface.co/docs/peft)
+- [Prompt Tuning 论文](https://arxiv.org/abs/2104.08691)
+- [Hugging Face Cookbook 中关于 prompt tuning 的部分](https://huggingface.co/learn/cookbook/prompt_tuning_peft)
diff --git a/4_evaluation/README.md b/4_evaluation/README.md
index 8140cd4c..aaa4700a 100644
--- a/4_evaluation/README.md
+++ b/4_evaluation/README.md
@@ -1,39 +1,40 @@
-# Evaluation
+# 评测
 
-Evaluation is a critical step in developing and deploying language models. It helps us understand how well our models perform across different capabilities and identify areas for improvement. This module covers both standard benchmarks and domain-specific evaluation approaches to comprehensively assess your smol model.
+在开发和部署语言模型的过程中，模型评测是一个非常关键的步骤。它有助于我们理解模型在不同方面的能力究竟如何，并找到进一步提示改进的空间。本章将涵盖标准基准测试和特定领域的评估方法，以全面评估你的 smol 模型。
 
-We'll use [`lighteval`](https://github.com/huggingface/lighteval), a powerful evaluation library developed by Hugging Face that integrates seamlessly with the Hugging Face ecosystem. For a deeper dive into evaluation concepts and best practices, check out the evaluation [guidebook](https://github.com/huggingface/evaluation-guidebook).
+我们将使用 [`lighteval`](https://github.com/huggingface/lighteval) 这个强大的评测代码库。它由 HuggingFace 开发，并完美地集成入了 HuggingFace 的生态系统。我们还提供了[指南书籍](https://github.com/huggingface/evaluation-guidebook)以便读者想要深入学习评测的相关概念和最佳实践。
 
-## Module Overview 
+## 章节总览
 
-A thorough evaluation strategy examines multiple aspects of model performance. We assess task-specific capabilities like question answering and summarization to understand how well the model handles different types of problems. We measure output quality through factors like coherence and factual accuracy. Safety evaluation helps identify potential harmful outputs or biases. Finally, domain expertise testing verifies the model's specialized knowledge in your target field.
+一个全面的评估策略会检查模型多个方面的性能。我们将评估模型特定领域的能力，比如回答问题、概括总结，来理解模型处理不同问题的能力。我们通过生成的连贯性和事实准确性等因素来衡量输出质量。同时，我们也需要安全评测，防治模型输出有害的信息或带有偏见的观点。最后，我们还可以进行特定领域的专业性测试，来确认模型是否在特定领域掌握了专业知识。
 
-## Contents
+## 目录
 
-### 1️⃣ [Automatic Benchmarks](./automatic_benchmarks.md)
+### 1️⃣ [自动化基准测试](./automatic_benchmarks_cn.md)
 
-Learn to evaluate your model using standardized benchmarks and metrics. We'll explore common benchmarks like MMLU and TruthfulQA, understand key evaluation metrics and settings, and cover best practices for reproducible evaluation.
+学习如何使用标准的测试基准和指标来评估模型。我们将会学习常见的测试基准，如 MMLU 和 TruthfulQA，理解重要指标和相关配置，同时为可复现的评估结果提供最佳实践。
 
+### 2️⃣ [自定义领域的评测](./custom_evaluation_cn.md)
 
-### 2️⃣ [Custom Domain Evaluation](./custom_evaluation.md)
-Discover how to create evaluation pipelines tailored to your specific use case. We'll walk through designing custom evaluation tasks, implementing specialized metrics, and building evaluation datasets that match your requirements.
+学习怎样为你的特定任务领域量身定做评估流程。我们将学习设计自定义评估任务、代码实现特定的指标，以及构建符合你要求的评估数据集。
 
-### 3️⃣ [Domain Evaluation Project](./project/README.md)
-Follow a complete example of building a domain-specific evaluation pipeline. You'll learn to generate evaluation datasets, use Argilla for data annotation, create standardized datasets, and evaluate models using LightEval.
+### 3️⃣ [领域评估的项目示例](./project/README_CN.md)
 
-### Exercise Notebooks
+通过一个完整的例子，学习构建特定领域的评测流程。这包含：生成评测数据集、使用 Argilla 平台进行数据标注、构建标准化的数据集、用 LightEval 评测模型。
 
-| Title | Description | Exercise | Link | Colab |
+### Notebook 练习
+
+| 标题 | 简述 | 习题 | 链接 | Colab |
 |-------|-------------|----------|------|-------|
-| Evaluate and Analyze Your LLM | Learn how to use LightEval to evaluate and compare models on specific domains | 🐢 Use medical domain tasks to evaluate a model <br> 🐕 Create a new domain evaluation with different MMLU tasks <br> 🦁 Create a custom evaluation task for your domain | [Notebook](./notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+| 评测并分析你的大语言模型 | 学习使用 LightEval 在特定领域评测、比较模型 | 🐢 使用医学相关领域的任务评估模型 <br> 🐕 Create a new domain evaluation with different MMLU tasks <br> 🦁 为你的特定任务领域创建一个自定义的评测任务 | [Notebook](./notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
 
 
 ## Resources
 
-- [Evaluation Guidebook](https://github.com/huggingface/evaluation-guidebook) - Comprehensive guide to LLM evaluation
-- [LightEval Documentation](https://github.com/huggingface/lighteval) - Official docs for the LightEval library
-- [Argilla Documentation](https://docs.argilla.io) - Learn about the Argilla annotation platform
-- [MMLU Paper](https://arxiv.org/abs/2009.03300) - Paper describing the MMLU benchmark
-- [Creating a Custom Task](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
-- [Creating a Custom Metric](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
-- [Using existing metrics](https://github.com/huggingface/lighteval/wiki/Metric-List)
\ No newline at end of file
+- [评测的指南书籍](https://github.com/huggingface/evaluation-guidebook) - 大语言模型评测领域的全面指南
+- [LightEval 文档](https://github.com/huggingface/lighteval) - LightEval 官方文档
+- [Argilla 文档](https://docs.argilla.io) - 了解 Argilla 标注平台
+- [MMLU 论文](https://arxiv.org/abs/2009.03300) - 关于 MMLU 测评基准的论文
+- [LightEval 如何添加自定义任务](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
+- [LightEval 如何添加自定义测评指标](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
+- [LightEval 如何使用现有的测评指标](https://github.com/huggingface/lighteval/wiki/Metric-List)
\ No newline at end of file
diff --git a/4_evaluation/automatic_benchmarks.md b/4_evaluation/automatic_benchmarks.md
index 6a980fa4..9d63a003 100644
--- a/4_evaluation/automatic_benchmarks.md
+++ b/4_evaluation/automatic_benchmarks.md
@@ -1,76 +1,81 @@
-# Automatic Benchmarks
+# 自动化基准测试
 
-Automatic benchmarks serve as standardized tools for evaluating language models across different tasks and capabilities. While they provide a useful starting point for understanding model performance, it's important to recognize that they represent only one piece of a comprehensive evaluation strategy.
+自动化基准测试可以作为一个标准化的工具，来衡量语言模型在不同任务上的能力。不过，虽然它可以有效地用来了解模型当前的性能，但我们还要认识到，这些评测结果仅仅是模型全面评估的一小部分，不能完全反映模型性能。
 
-## Understanding Automatic Benchmarks
+## 理解自动化基准评测
 
-Automatic benchmarks typically consist of curated datasets with predefined tasks and evaluation metrics. These benchmarks aim to assess various aspects of model capability, from basic language understanding to complex reasoning. The key advantage of using automatic benchmarks is their standardization - they allow for consistent comparison across different models and provide reproducible results.
+自动化基准评测一般是在定义好的领域和评测指标下，对特定的数据集进行测试。这种基准测试会评估模型多方面的能力，从最基础的语言理解到复杂的逻辑推理。其最主要的优点还是标准型方面，不同模型都使用相同标准来测试，可以用来对比不同模型的效果，同时测评出的结果也是可复现的。
 
-However, it's crucial to understand that benchmark performance doesn't always translate directly to real-world effectiveness. A model that excels at academic benchmarks may still struggle with specific domain applications or practical use cases.
+然而，我们也需要知道，这种基准测试也不是完全反映模型的现实能力的。比如，一个在学术评测基准上表现优异的模型，也许在其它应用领域或实践层面表现很差。
 
-## Benchmarks and Their Limitations
+## 现有评测基准及其局限性
 
-### General Knowledge Benchmarks
+### 通识层面的评测基准
 
-MMLU (Massive Multitask Language Understanding) tests knowledge across 57 subjects, from science to humanities. While comprehensive, it may not reflect the depth of expertise needed for specific domains. TruthfulQA evaluates a model's tendency to reproduce common misconceptions, though it can't capture all forms of misinformation.
+MMLU（Massive Multitask Language Understanding）这个评测基准，涵盖了从科学到人文共 57 个学科的知识，是一个通识层面的评测基准。但虽然全面，它可能在某些领域的专业深度还不算够。另一方面，TruthfulQA 这个评测基准，涵盖 38 个学科问答，则会评测模型输出的真实性如何。
 
-### Reasoning Benchmarks
-BBH (Big Bench Hard) and GSM8K focus on complex reasoning tasks. BBH tests logical thinking and planning, while GSM8K specifically targets mathematical problem-solving. These benchmarks help assess analytical capabilities but may not capture the nuanced reasoning required in real-world scenarios.
+### 推理层面的评测基准
 
-### Language Understanding
-HELM provides a holistic evaluation framework, while WinoGrande tests common sense through pronoun disambiguation. These benchmarks offer insights into language processing capabilities but may not fully represent the complexity of natural conversation or domain-specific terminology.
+BBH（Big Bench Hard）和 GSM8K 这两个评测基准重点关注复杂的推理任务。BBH 主要测试逻辑思考和规划能力，GSM8K 则特别关注数学问题的求解。这些评测基准可以用来评测模型的分析问题能力，但人在现实世界中微妙的推理细节可能会被忽略。
 
-## Alternative Evaluation Approaches
+### 语言理解层面的评测基准
 
-Many organizations have developed alternative evaluation methods to address the limitations of standard benchmarks:
+在语言理解层面，HELM 提供了一个全面的评测框架，而 WinoGrande 则通过代词指代的歧义消除，测试模型在常识层面的能力。这些评测基准让我们能深入了解模型在语言处理层面的水平，但缺点是暂未模仿到人与人之间对话的复杂性，同时暂未测评到专业术语。
 
-### LLM-as-Judge
-Using one language model to evaluate another's outputs has become increasingly popular. This approach can provide more nuanced feedback than traditional metrics, though it comes with its own biases and limitations.
+## 其它评测方法
 
-### Evaluation Arenas
-Platforms like Anthropic's Constitutional AI Arena allow models to interact and evaluate each other in controlled environments. This can reveal strengths and weaknesses that might not be apparent in traditional benchmarks.
+除了上述评基准，很多机构也开发了其它评测方法，以应对标准化基准测试的缺陷：
 
-### Custom Benchmark Suites
-Organizations often develop internal benchmark suites tailored to their specific needs and use cases. These might include domain-specific knowledge tests or evaluation scenarios that mirror actual deployment conditions.
+### 用大语言模型作为评审
+
+用一个大语言模型去评测另一个大语言模型的输出，这种方法最近开始常用起来。相比于传统的评测指标，这种方法可以提供更细致入微的反馈。缺点是作为评审的大语言模型自己也有偏见和局限性，可能导致评测结果不够好。
+
+### 在竞技场内相互评测
+
+像 Anthropic's Constitutional AI Arena 这样的平台，可以让模型在里面相互互动、评测。这样的评测场景也有助于模型发现各自的强项和弱点。
+
+### 自定义评测基准
+
+很多组织也会自己开发对内的评测基准，通常是针对特定的需求或应用场景开发的。这样开发出来的评测基准一般包含特定的专业领域知识、反映产品的实际应哟过场景。
 
 ## Creating Your Own Evaluation Strategy
 
-Remember that while LightEval makes it easy to run standard benchmarks, you should also invest time in developing evaluation methods specific to your use case.
+虽然使用 LightEval 可以很方便地进行标准化的基准评测，但作为一个 LLM 开发者，你必须也要针对你们产品的应用场景开发自己的评测方案。标准化的基准评测仅仅是一测评开始的第一步，你绝不能只用它进行模型测试。
 
-While standard benchmarks provide a useful baseline, they shouldn't be your only evaluation method. Here's how to develop a more comprehensive approach:
+如何自定义你的方位测评？方法如下：
 
-1. Start with relevant standard benchmarks to establish a baseline and enable comparison with other models.
+1. 首先从相关的标准化基准测试中开始，建立一个基准，保证能够和其它模型进行对比。
 
-2. Identify the specific requirements and challenges of your use case. What tasks will your model actually perform? What kinds of errors would be most problematic?
+2. 针对你的应用场景的独特需求，确认你的模型将会应对的挑战。例如，你的模型上线后将会主要执行什么任务？有可能出现哪些问题？哪些 bad case 是最应该避免的？
 
-3. Develop custom evaluation datasets that reflect your actual use case. This might include:
-   - Real user queries from your domain
-   - Common edge cases you've encountered
-   - Examples of particularly challenging scenarios
+3. 开发你自己的测试数据集，以便专门应对你的测试场景。这可能包括：
+   - 在你的特定领域里，真实用户的请求
+   - 常见的边缘案例
+   - 可能发生的有挑战性的情况
 
-4. Consider implementing a multi-layered evaluation strategy:
-   - Automated metrics for quick feedback
-   - Human evaluation for nuanced understanding
-   - Domain expert review for specialized applications
-   - A/B testing in controlled environments
+4. 也需要考虑开发一个多层级的评测策略：
+   - 首先，为了能快速获取反馈，你可以设置一个自动化的评测指标
+   - 针对细微的语言理解能力，考虑人工测评
+   - 针对专业领域的应用，引入行业专家的评审
+   - 在控制变量的环境下，进行 A/B 测试
 
-## Using LightEval for Benchmarking
+## 使用 LightEval 进行基准评测
 
-LightEval tasks are defined using a specific format:
+LightEval 评测任务通过以下格式定义：
 ```
 {suite}|{task}|{num_few_shot}|{auto_reduce}
 ```
 
-- **suite**: The benchmark suite (e.g., 'mmlu', 'truthfulqa')
-- **task**: Specific task within the suite (e.g., 'abstract_algebra')
-- **num_few_shot**: Number of examples to include in prompt (0 for zero-shot)
-- **auto_reduce**: Whether to automatically reduce few-shot examples if prompt is too long (0 or 1)
+- **suite**：哪一套基准评测方法（比如 'mmlu'、'truthfulqa'）
+- **task**：这一套基准评测方法中的哪个任务（比如 'abstract_algebra'）
+- **num_few_shot**：提示词中加入的示例的数量（如果是 0，那就是 zero-shot 测试）
+- **auto_reduce**：当提示词太长时，是否自动减少提示词中 few-shot 的样本量
 
-Example: `"mmlu|abstract_algebra|0|0"` evaluates on MMLU's abstract algebra task with zero-shot inference.
+举例来说，`"mmlu|abstract_algebra|0|0"` 就会评测 MMLU 的 abstract algebra 任务，推理是 zero-shot 形式。
 
-### Example Evaluation Pipeline
+### 评测代码示例
 
-Here's a complete example of setting up and running an evaluation on automatic benchmarks relevant to one specific domain:
+以下代码就是一个在某个领域进行自动化评测的示例：
 
 ```python
 from lighteval.tasks import Task, Pipeline
@@ -114,7 +119,8 @@ results = pipeline.get_results()
 pipeline.show_results()
 ```
 
-Results are displayed in a tabular format showing:
+测评结果会以表格形式打印出来：
+
 ```
 |                  Task                  |Version|Metric|Value |   |Stderr|
 |----------------------------------------|------:|------|-----:|---|-----:|
@@ -124,8 +130,8 @@ Results are displayed in a tabular format showing:
 |leaderboard:mmlu:high_school_biology:5  |      0|acc   |0.1500|±  |0.0819|
 ```
 
-You can also handle the results in a pandas DataFrame and visualise or represent them as you want.
+使用 pandas 的 `DataFrame` 或其它可视化方式呈现结果也是可以的。
 
-# Next Steps
+# 接下来
 
-⏩ Explore [Custom Domain Evaluation](./custom_evaluation.md) to learn how to create evaluation pipelines tailored to your specific needs
+⏩ 学习 [Custom Domain Evaluation](./custom_evaluation_cn.md)，了解如何根据你的特定需求创建自定义的评测流程。
diff --git a/4_evaluation/custom_evaluation.md b/4_evaluation/custom_evaluation.md
index c82868a7..eb1451f9 100644
--- a/4_evaluation/custom_evaluation.md
+++ b/4_evaluation/custom_evaluation.md
@@ -1,16 +1,16 @@
-# Custom Domain Evaluation
+# 在特定领域进行自定义评测
 
-While standard benchmarks provide valuable insights, many applications require specialized evaluation approaches tailored to specific domains or use cases. This guide will help you create custom evaluation pipelines that accurately assess your model's performance in your target domain.
+虽然标准化的评测基准让我们对模型的性能有了初步的认识，但针对特定的应用场景，我们还需要专门制定评测方法，考察模型在特定领域的表现。本文将带你创建自定义的评测流程，针对你的目标领域对模型进行精准评测。
 
-## Designing Your Evaluation Strategy
+## 设计评测策略
 
-A successful custom evaluation strategy starts with clear objectives. Consider what specific capabilities your model needs to demonstrate in your domain. This might include technical knowledge, reasoning patterns, or domain-specific formats. Document these requirements carefully - they'll guide both your task design and metric selection.
+成功创建自定义评测策略的第一步是确定清晰的目标。你需要考虑在你的特定领域，哪些特殊能力是你的模型需要具备的？这可能涉及技术层面的知识、推理的模式、特定的格式等。你需要认真记录好这些需求，然后参考这些需求去设计测试任务、选择评测指标。
 
-Your evaluation should test both standard use cases and edge cases. For example, in a medical domain, you might evaluate both common diagnostic scenarios and rare conditions. In financial applications, you might test both routine transactions and complex edge cases involving multiple currencies or special conditions.
+测试的样例不仅需要包含标准应用场景，也要考虑边缘场景。举例来说，如果是医学领域，常见的诊断场景和罕见情况都是需要考虑的。在金融领域，除了常规交易，复杂交易（比如设计多种货币或特殊条件的情况）的处理能力也需要被测试到。
 
-## Implementation with LightEval
+## 使用 LightEval 的代码实现
 
-LightEval provides a flexible framework for implementing custom evaluations. Here's how to create a custom task:
+LightEval 是一个非常灵活的框架，可以用来实现自定义的测评任务。下面代码展示了如何创建自定义测试任务：
 
 ```python
 from lighteval.tasks import Task, Doc
@@ -34,9 +34,9 @@ class CustomEvalTask(Task):
         return response.strip() == ref.strip()
 ```
 
-## Custom Metrics
+## 自定义评价指标
 
-Domain-specific tasks often require specialized metrics. LightEval provides a flexible framework for creating custom metrics that capture domain-relevant aspects of performance:
+特定领域的测试任务通常也需要特殊的评价指标。LightEval 也可以灵活地做到这一点：
 
 ```python
 from aenum import extend_enum
@@ -72,7 +72,7 @@ custom_metric_group = SampleLevelMetricGrouping(
 extend_enum(Metrics, "custom_metric_name", custom_metric_group)
 ```
 
-For simpler cases where you only need one metric value per sample:
+如果每个样例只有一个指标，代码可以是这样：
 
 ```python
 def simple_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> bool:
@@ -92,41 +92,44 @@ simple_metric_obj = SampleLevelMetric(
 extend_enum(Metrics, "simple_metric", simple_metric_obj)
 ```
 
-You can then use your custom metrics in your evaluation tasks by referencing them in the task configuration. The metrics will be automatically computed across all samples and aggregated according to your specified functions.
+实现完代码后，你就可以在你的评测任务中引用这些指标的名称，然后在你的评测任务中使用。这些指标会在测试过程中自动在每个样本上计算，并最终统计数值。
 
-For more complex metrics, consider:
-- Using metadata in your formatted documents to weight or adjust scores
-- Implementing custom aggregation functions for corpus-level statistics
-- Adding validation checks for your metric inputs
-- Documenting edge cases and expected behavior
+如果需要使用更复杂的评测指标，你还可以实现这些功能：
 
-For a complete example of custom metrics in action, see our [domain evaluation project](./project/README.md).
+- 使用元数据，对不同测试样本的分数进行加权或其它调整
+- 对所有样本的指标进行统计时，你可以实现一个自定义的函数（上述示例中 corpus-level 统计使用了取平均的方法）
+- 对输入到你的评测指标函数中的数据进行格式检查
+- 记录边缘场景及其期望的行为
 
-## Dataset Creation
 
-High-quality evaluation requires carefully curated datasets. Consider these approaches for dataset creation:
 
-1. Expert Annotation: Work with domain experts to create and validate evaluation examples. Tools like [Argilla](https://github.com/argilla-io/argilla) make this process more efficient.
+你可以学习本章 [domain evaluation project](./project/README_CN.md) 这个项目课程，真正地动手实践一下自定义测评。
 
-2. Real-World Data: Collect and anonymize real usage data, ensuring it represents actual deployment scenarios.
+## 测试数据集的创建
 
-3. Synthetic Generation: Use LLMs to generate initial examples, then have experts validate and refine them.
+高质量的测评需要高质量的测试数据集。在创建数据集时，需要考虑以下方面：
 
-## Best Practices
+1. 专家级的标注：与领域专家一起创建和检验测试样本。你可以用 [Argilla](https://github.com/argilla-io/argilla) 高效地进行标注。
 
-- Document your evaluation methodology thoroughly, including any assumptions or limitations
-- Include diverse test cases that cover different aspects of your domain
-- Consider both automated metrics and human evaluation where appropriate
-- Version control your evaluation datasets and code
-- Regularly update your evaluation suite as you discover new edge cases or requirements
+2. 真实世界的数据：收集真实数据并进行脱敏，确保这些样本能代表真实部署模型的场景。
 
-## References
+3. 借助合成数据：使用 LLM 生成一些初始样本，然后让领域专家检查、修改。这样可以助你快速创建数据集。
 
-- [LightEval Custom Task Guide](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
-- [LightEval Custom Metrics](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
-- [Argilla Documentation](https://docs.argilla.io) for dataset annotation
-- [Evaluation Guidebook](https://github.com/huggingface/evaluation-guidebook) for general evaluation principles
+## 最佳实践
 
-# Next Steps
+- 全面记录你的测试方法，包括各种假设和局限
+- 保证测试样本的多样性，确保你的领域内各个方面都能被测试到
+- 如有需要，自动化的测试指标和人工评测都要用上
+- 对测评数据集和代码进行版本控制
+- 定期更新你的评测流程，不断加入新的边缘场景、完善新的需求
 
-⏩ For a complete example of implementing these concepts, see our [domain evaluation project](./project/README.md).
\ No newline at end of file
+## 参考资料
+
+- [LightEval 如何添加自定义任务](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
+- [LightEval 如何添加自定义测评指标](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
+- [Argilla 文档](https://docs.argilla.io) 可以用来进行数据标注
+- [评测的指南书籍](https://github.com/huggingface/evaluation-guidebook) 大语言模型评测领域的全面指南
+- 
+# 接下来
+
+⏩ 完整的自定义测评请见本章 [domain evaluation project](./project/README_CN.md)。
\ No newline at end of file
diff --git a/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb b/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
index 9597634b..592b84ce 100644
--- a/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
+++ b/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
@@ -6,24 +6,24 @@
         "id": "TZVw9f5QYWPL"
       },
       "source": [
-        "# lighteval is your AI evaluation library\n",
+        "# 使用 lighteval 作为你的 AI 测评框架\n",
         "\n",
-        "This notebook explores how you can use lighteval to evaluate and compare LLMs.\n",
+        "本文将带你实践如何使用 lighteval 测评大语言模型，并与其它模型进行对比。\n",
         "\n",
-        "`lighteval` has been around a while and it's a great tool for getting eval score on major benchmarks. It's just been refactored to support being used like a library in Python, which makes it great for comparing models across benchmarks.\n",
+        "使用 `lighteval` 可以很方便地在主流评测基准上计算评测分数。最近，`lighteval` 被进行了一次重构，以便在 Python 中最为一个库进行使用。\n",
         "\n",
-        "So let's dig in to some eval scores.\n",
+        "接下来，我们将试着计算一些分数。\n",
         "\n",
         "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-        "    <h2 style='margin: 0;color:blue'>Exercise: Evaluate Your Own Model</h2>\n",
-        "    <p>Now that you've seen how to evaluate models on specific domains, try evaluating a model on a domain that interests you.</p>\n",
-        "    <p><b>Difficulty Levels</b></p>\n",
-        "    <p>🐢 Use the existing medical domain tasks but evaluate a different model from the Hugging Face hub</p>\n",
-        "    <p>🐕 Create a new domain evaluation by selecting different MMLU tasks (e.g., computer science, mathematics, physics)</p>\n",
-        "    <p>🦁 Create a custom evaluation task using LightEval's task framework and evaluate models on your specific domain</p>\n",
+        "    <h2 style='margin: 0;color:blue'>练习：评测你的模型</h2>\n",
+        "    <p>基于前面的学习，你可以试着选一个你感兴趣的领域，在上面测试模型。</p>\n",
+        "    <p><b>难度等级</b></p>\n",
+        "    <p>🐢 从 Hugging Face hub 另找一个模型，在已有的医学领域测试模型</p>\n",
+        "    <p>🐕 选择一个不同的 MMLU 任务（如 comupter science、mathematics、physcics），进行模型测试</p>\n",
+        "    <p>🦁 用 LightEval 自定义评测任务，测试你选的模型</p>\n",
         "</div>\n",
         "\n",
-        "## Install dependencies"
+        "## 安装依赖"
       ]
     },
     {
@@ -63,17 +63,17 @@
         "id": "TDKs5ShvXw8K"
       },
       "source": [
-        "## Setup `lighteval` evaluation\n",
+        "## 配置 `lighteval` 评测环境和评测流程\n",
         "\n",
-        "We need to setup the evaluation environment and pipeline. Much of this we will disable because we're keeping things in the notebook, but we could also use `push_to_hub` or `push_to_tensorboard`.\n",
+        "我们首先需要配置 `lighteval` 评测环境和评测流程。注意 `push_to_hub` 和 `push_to_tensorboard` 的用法。\n",
         "\n",
         "### `push_to_hub`\n",
         "\n",
-        "This is useful if we're evaluating a model and want to persist its evaluation with weights and configuration on the Hugging Face hub.\n",
+        "如果我们想要把评测结果和模型的配置和权重绑定在一起，我们可以用 `push_to_hub` 将其保存到 Hugging Face hub 上。\n",
         "\n",
         "### `push_to_tensorboard`\n",
         "\n",
-        "This would be useful if we were building an evaluation tool or script, where we wanted to view results within tensorboard."
+        "如果我们建立了评测工具或脚本用来可视化结果，我们可以使用 `push_to_tensorboard` 来进行可视化。"
       ]
     },
     {
@@ -206,21 +206,21 @@
         "id": "nsNjwzCtltkA"
       },
       "source": [
-        "# Compares models with `lighteval`\n",
+        "# 用 `lighteval` 对比不同模型\n",
         "\n",
-        "We are going to compare two small LLMs on a domain. We will use `Qwen2.5-0.5B` and `SmolLM2-360M-Instruct` and we will evaluate them on a medical domain.\n",
+        "这里我们尝试对比两个模型：`Qwen2.5-0.5B` 和 `SmolLM2-360M-Instruct`。我们选择的领域是医学领域。\n",
         "\n",
-        "We can create a domain evaluation from a subset of MMLU evaluations, by defining the evaluation tasks. In lighteval, tasks are described as strings.\n",
+        "这里我们是用 MMLU 的一个字任务创建评测的。在 lighteval 中，不同测试任务可以用字符串指代，格式如下：\n",
         "\n",
         "`{suite}|{task}:{subtask}|{num_few_shot}|{0 or 1 to reduce num_few_shot if prompt is too long}`\n",
         "\n",
-        "Therefore, we will pass our list of medicine related tasks like this:\n",
+        "所以，针对我们选择的医学领域，我们可以传入一系列任务，用逗号分隔开：\n",
         "\n",
         "```\n",
         "\"leaderboard|mmlu:anatomy|5|0,leaderboard|mmlu:professional_medicine|5|0,leaderboard|mmlu:high_school_biology|5|0,leaderboard|mmlu:high_school_chemistry|5|0\"\n",
         "```\n",
         "\n",
-        "Which can be translated to :\n",
+        "上述字符串代表以下四个任务：\n",
         "\n",
         "| Suite | Task | Num Fewshot Example | Limit Fewshots |\n",
         "|---|---|---|---|\n",
@@ -229,7 +229,7 @@
         "| leaderboard | mmlu:high_school_biology | 5 | False |\n",
         "| leaderboard | mmlu:high_school_chemistry | 5 | False |\n",
         "\n",
-        "For a full list of lighteval supported tasks. Checkout this page in [the documentation](https://github.com/huggingface/lighteval/wiki/Available-Tasks)."
+        "完整的任务列表，可以参考[这篇文档](https://github.com/huggingface/lighteval/wiki/Available-Tasks)。"
       ]
     },
     {
@@ -249,7 +249,7 @@
         "id": "XwcJklSFX4H6"
       },
       "source": [
-        "# Evaluate Qwen2.5 0.5B"
+        "# 评测 Qwen2.5 0.5B"
       ]
     },
     {
@@ -716,7 +716,7 @@
         "id": "CIwCaCxJX_hA"
       },
       "source": [
-        "# Evaluate SmolLM 360M"
+        "# 评测 SmolLM 360M"
       ]
     },
     {
@@ -877,9 +877,9 @@
         "id": "0HD8aFwSYGHu"
       },
       "source": [
-        "# Visualize Results\n",
+        "# 可视化结果\n",
         "\n",
-        "Now that we have results from the two models we can visualize them side-by-side. We'll keep visualisation simple here, but with this data structure you could represent scores in many ways."
+        "得到结果后，我们可以可视化一下，并列在一起对比。这里我们使用了 `pandas` 简单地实现了可视化操作，不过你也可以使用不同的可视化方法："
       ]
     },
     {
@@ -909,13 +909,13 @@
         "id": "qJEbQeYDplKX"
       },
       "source": [
-        "# 💐 That's it!\n",
+        "# 💐 就是这些！\n",
         "\n",
-        "You have a handy notebook to view model evals. You could use this to:\n",
+        "本文提供了标准化评测的简单代码，使用这些代码，你还可以：\n",
         "\n",
-        "- select the right model for your inference use case\n",
-        "- evaluate checkpoints during training\n",
-        "- share model scores"
+        "- 选择模型进行推理\n",
+        "- 在训练过程中针对不同阶段保存的模型实时测试\n",
+        "- 分享你的测试成绩到开源社区"
       ]
     },
     {
@@ -924,9 +924,9 @@
         "id": "jWdS38syaipm"
       },
       "source": [
-        "🏃Next Steps\n",
+        "🏃接下来\n",
         "\n",
-        "- If you want to go deeper into your evaluation results check out this [notebook](https://github.com/huggingface/evaluation-guidebook/blob/main/contents/examples/comparing_task_formulations.ipynb)\n"
+        "- 如果你还想深入学习，可以看看这个 [notebook](https://github.com/huggingface/evaluation-guidebook/blob/main/contents/examples/comparing_task_formulations.ipynb)\n"
       ]
     }
   ],
diff --git a/4_evaluation/project/README.md b/4_evaluation/project/README.md
index 98d32eca..c896e4af 100644
--- a/4_evaluation/project/README.md
+++ b/4_evaluation/project/README.md
@@ -1,72 +1,70 @@
-# Domain Specific Evaluation with Argilla, Distilabel, and LightEval
+# 用 Argilla、Distilabel 和 LightEval 进行特定领域评测
 
-Most popular benchmarks look at very general capabilities (reasoning, math, code), but have you ever needed to study more specific capabilities? 
+绝大多数的常用评测基准关注的都是模型非常基本的能力，比如推理、数学、编程等，而忽略了特定的专业领域能力。如何进行专业领域（如金融、法律、医学等）的模型评测呢？
 
-What should you do if you need to evaluate a model on a **custom domain** relevant to your use-cases? (For example, financial, legal, medical use cases)  
+本教程将会展示**自定义领域**模型测试的完整流程。我们重点关注数据部分。在相关数据收集、标注测试数据方面，我们会使用 [Argilla](https://github.com/argilla-io/argilla)、[Distilabel](https://github.com/argilla-io/distilabel) 和 [LightEval](https://github.com/huggingface/lighteval) 作为工具，生成考试问题相关的数据。
 
-This tutorial shows you the full pipeline you can follow, from creating relevant data and annotating your samples to evaluating your model on them, with the easy to use [Argilla](https://github.com/argilla-io/argilla), [distilabel](https://github.com/argilla-io/distilabel), and [lighteval](https://github.com/huggingface/lighteval). For our example, we'll focus on generating exam questions from multiple documents. 
 
-## Project Structure
+## 项目结构
 
-For our process, we will follow 4 steps, with a script for each: generating a dataset, annotating it, extracting relevant samples for evaluation from it, and actually evaluating models.
+本项目包含四份 Python 代码文件。我们分四个步骤完成模型在自定义领域的测评，每份代码对应一个步骤。这四个步骤分别是：数据生成、数据标注、相关测试样本的提取，以及模型评测。
 
-| Script Name | Description |
+| 代码文件 | 概述 |
 |-------------|-------------|
-| generate_dataset.py | Generates exam questions from multiple text documents using a specified language model. |
-| annotate_dataset.py | Creates an Argilla dataset for manual annotation of the generated exam questions. |
-| create_dataset.py | Processes annotated data from Argilla and creates a Hugging Face dataset. |
-| evaluation_task.py | Defines a custom LightEval task for evaluating language models on the exam questions dataset. |
+| generate_dataset.py | 使用一个专门的语言模型，从多个文本文档中生成考试问题。 |
+| annotate_dataset.py | 用 Argilla 创建一个数据集，手动为生成的考试问题数据进行标注。 |
+| create_dataset.py | 处理标注过的数据，并创建对应的 HuggingFace 数据集。 |
+| evaluation_task.py | 自定义了一个 LightEval 任务，在前面建立好的测试数据上测试。 |
 
-## Steps
+## 步骤
 
-### 1. Generate Dataset
+### 1. 数据集的生成
 
-The `generate_dataset.py` script uses the distilabel library to generate exam questions based on multiple text documents. It uses the specified model (default: Meta-Llama-3.1-8B-Instruct) to create questions, correct answers, and incrorect answers (known as distractors). You should add you own data samples and you might wish to use a different model.
+使用 `generate_dataset.py`，我们可以用 `distilabel` 这个库根据几个文本文档生成一些考试问题。一个特定的模型（这里默认使用 Meta-Llama-3.1-8B-Instruct）被拿来用以生成问题、正确答案和错误答案（错误答案用来作为干扰项）。你需要加入你自己的数据，也可以切换使用别的模型。
 
-To run the generation:
+通过以下命令可以开始生成：
 
 ```sh
 python generate_dataset.py --input_dir path/to/your/documents --model_id your_model_id --output_path output_directory
 ```
 
-This will create a [Distiset](https://distilabel.argilla.io/dev/sections/how_to_guides/advanced/distiset/) containing the generated exam questions for all documents in the input directory. 
+代码运行中将会创建一个 [Distiset](https://distilabel.argilla.io/dev/sections/how_to_guides/advanced/distiset/)，它包含根据文档生成的考试问题，其中保存文档的目录是 `input_dir`。
 
-### 2. Annotate Dataset
+### 2. 标注数据集
 
-The `annotate_dataset.py` script takes the generated questions and creates an Argilla dataset for annotation. It sets up the dataset structure and populates it with the generated questions and answers, randomizing the order of answers to avoid bias. Once in Argilla, you or a domain expert can validate the dataset with the correct answers.
+使用 `annotate_dataset.py` 可以将生成的问题创建为一个 Argilla 数据集，用以标注。该程序会构建起数据集结构，并把生成的问题和回答填充进统一结构中，还可以改变数据样本的顺序来避免偏向性。使用 Argilla，你或者一个领域专家可以用选择的方式给出每个问题的正确回答。
 
-You will see suggested correct answers from the LLM in random order and you can approve the correct answer or select a different one. The duration of this process will depend on the scale of your evaluation dataset, the complexity of your domain data, and the quality of your LLM. For example, we were able to create 150 samples within 1 hour on the domain of transfer learning, using Llama-3.1-70B-Instruct, mostly by approving the correct answer and discarding the incorrect ones.
+在标注界面中，已生成的几个回答会随机排列着，供标注人员选择正确答案。这其中，LLM 会提供一个建议的正确回答，你可以选择你认为正确的回答，也可以对选中的正确回答进行编辑改进。标注过程的耗时取决于你的数据集规模、领域内数据的复杂度，以及 LLM 的能力强弱。举例来说，我们是可以借助 Llama-3.1-70B-Instruct 在 1 小时内标注好迁移学习领域 150 个样本的，大多数时候，直接选择正确答案即可。
+
+通过以下命令可以开始标注：
 
-To run the annotation process:
 
 ```sh
 python annotate_dataset.py --dataset_path path/to/distiset --output_dataset_name argilla_dataset_name
 ```
 
-This will create an Argilla dataset that can be used for manual review and annotation.
+这将会创建一个 Argilla 数据集，用来手工检查和标注数据。
 
 ![argilla_dataset](./images/domain_eval_argilla_view.png)
 
-If you're not using Argilla, deploy it locally or on spaces following this [quickstart guide](https://docs.argilla.io/latest/getting_started/quickstart/).
+可以参考[这里的指引](https://docs.argilla.io/latest/getting_started/quickstart/)，在本地或 Hugging Face 的 space 里部署标注任务。
 
-### 3. Create Dataset
+### 3. 创建数据集
 
-The `create_dataset.py` script processes the annotated data from Argilla and creates a Hugging Face dataset. It handles both suggested and manually annotated answers. The script will create a dataset with the question, possible answers, and the column name for the correct answer. To create the final dataset:
+使用 `create_dataset.py` 可以进一步处理 Argilla 标注的数据，并创建一个 Hugging Face 数据集。这个数据集里每条数据包含这些内容：问题、可能的回答、正确回答（所在的列的名字）。运行以下命令即可创建最终的数据集：
 
 ```sh
 huggingface_hub login
 python create_dataset.py --dataset_path argilla_dataset_name --dataset_repo_id your_hf_repo_id
 ```
 
-This will push the dataset to the Hugging Face Hub under the specified repository. You can view the sample dataset on the hub [here](https://huggingface.co/datasets/burtenshaw/exam_questions/viewer/default/train), and a preview of the dataset looks like this:
+最终，数据集会被推送到 Hugging Face Hub 里。本示例的数据集已经上传了，可以在[这里](https://huggingface.co/datasets/burtenshaw/exam_questions/viewer/default/train)查看，大致是这样：
 
 ![hf_dataset](./images/domain_eval_dataset_viewer.png)
 
-### 4. Evaluation Task
-
-The `evaluation_task.py` script defines a custom LightEval task for evaluating language models on the exam questions dataset. It includes a prompt function, a custom accuracy metric, and the task configuration. 
+### 4. 开始测评
 
-To evaluate a model using lighteval with the custom exam questions task:
+使用 `evaluation_task.py`，你可以自定义一个 LightEval 任务，用来在前面创建的数据集上测试模型。具体执行命令如下：
 
 ```sh
 lighteval accelerate \
@@ -76,10 +74,8 @@ lighteval accelerate \
     --output_dir "./evals"
 ```
 
-You can find detailed guides in lighteval wiki about each of these steps: 
-
-- [Creating a Custom Task](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
-- [Creating a Custom Metric](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
-- [Using existing metrics](https://github.com/huggingface/lighteval/wiki/Metric-List)
-
+此外，lighteval 的 wiki 也提供了更详细的讲解：
 
+- [自定义评测任务](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
+- [自定义评测指标](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
+- [使用已有的评测指标](https://github.com/huggingface/lighteval/wiki/Metric-List)
diff --git a/5_vision_language_models/README.md b/5_vision_language_models/README.md
index 48d95fed..cc087c22 100644
--- a/5_vision_language_models/README.md
+++ b/5_vision_language_models/README.md
@@ -1,34 +1,34 @@
-# Vision Language Models
+# 视觉语言模型
 
-## 1. VLM Usage
+## 1. 视觉语言模型的用处
 
-Vision Language Models (VLMs) process image inputs alongside text to enable tasks like image captioning, visual question answering, and multimodal reasoning.  
+视觉语言模型（Vision Language Models 或简称 VLM）是一种不仅仅接收语言输入、而且可以处理图片输入的模型，它可以支持诸如图像文本描述生成（image captioning）、视觉问答（visual question answering）、多模态推理（multimodal reasoning）等任务。
 
-A typical VLM architecture consists of an image encoder to extract visual features, a projection layer to align visual and textual representations, and a language model to process or generate text. This allows the model to establish connections between visual elements and language concepts.
+一个典型的 VLM 架构包含一个图像编码器（用来提取视觉特征）、一个映射层（用来对齐视觉特征和文本表征）以及一个语言模型（用以处理视觉语言特征并输出文本）。这使得模型得以在视觉元素和语言概念之间建立起连接。
 
-VLMs can be used in different configurations depending on the use case. Base models handle general vision-language tasks, while chat-optimized variants support conversational interactions. Some models include additional components for grounding predictions in visual evidence or specializing in specific tasks like object detection.
+VLM 可以有很多用处。基本的用途包括通用的视觉语言任务，而那些针对聊天对话场景优化过的 VLM 则可以支持对话式的人机互动。还有些模型可以根据图像中的证据预测事实，或者进行特定的任务，如物体检测。
 
-For more on the technicality and usage of VLMs, refer to the [VLM Usage](./vlm_usage.md) page.  
+关于 VLM 的更多技术和使用，建议读者在 [VLM 的使用](./vlm_usage_cn.md)这一节中学习。
 
-## 2. VLM Fine-Tuning  
+## 2. 视觉语言模型的微调
 
-Fine-tuning a VLM involves adapting a pre-trained model to perform specific tasks or to operate effectively on a particular dataset. The process can follow methodologies such as supervised fine-tuning, preference optimization, or a hybrid approach that combines both, as introduced in Modules 1 and 2.  
+微调一个 VLM 的过程，通常是指选择一个预训练过的模型，在一个新的数据集上学习处理特定领域的任务。这个过程可以参考本教程第 1 和 2 章的相关方法，比如有监督微调、偏好优化等。
 
-While the core tools and techniques remain similar to those used for LLMs, fine-tuning VLMs requires additional focus on data representation and preparation for images. This ensures the model effectively integrates and processes both visual and textual data for optimal performance. Given that the demo model, SmolVLM, is significantly larger than the language model used in the previous module, it's essential to explore methods for efficient fine-tuning. Techniques like quantization and PEFT can help make the process more accessible and cost-effective, allowing more users to experiment with the model. 
+虽然核心工具与技术和大语言模型（LLMs）所用的大致相同，但微调视觉语言模型（VLMs）需要格外关注图像的数据表示与准备。如此才能确保模型有效整合并处理视觉与文本数据，以实现最佳性能。鉴于演示模型 SmolVLM 比前一模块使用的语言模型大得多，探索高效的微调方法至关重要。量化和参数高效微调（PEFT）等技术，能让这一过程更易于操作且成本更低，使更多用户能够对该模型进行试验。
 
-For detailed guidance on fine-tuning VLMs, visit the [VLM Fine-Tuning](./vlm_finetuning.md) page.  
+如果你想了解详细的微调 VLM 的技术，你可以学习 [VLM 微调](./vlm_finetuning_cn.md) 这一节。  
 
 
 ## Exercise Notebooks  
 
 
-| Title | Description | Exercise | Link | Colab |
+| 标题 | 概述 | 练习 | 链接 | Colab |
 |-------|-------------|----------|------|-------|
-| VLM Usage | Learn how to load and use a pre-trained VLM for various tasks | 🐢 Process an image<br>🐕 Process multiple images with batch handling <br>🦁 Process a full video| [Notebook](./notebooks/vlm_usage_sample.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/5_vision_language_models/notebooks/vlm_usage_sample.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-| VLM Fine-Tuning | Learn how to fine-tune a pre-trained VLM for task-specific datasets | 🐢 Use a basic dataset for fine-tuning<br>🐕 Try a new dataset<br>🦁 Experiment with alternative fine-tuning methods | [Notebook](./notebooks/vlm_sft_sample.ipynb)| <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/5_vision_language_models/notebooks/vlm_sft_sample.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | 
+| VLM 的使用 | 学习如何载入并使用一个预训练过的 VLM 来处理各种任务 | 🐢 尝试处理一张图片 <br>🐕 尝试用 batch 的方式处理多个图片 <br>🦁 尝试处理一整个视频 | [Notebook](./notebooks/vlm_usage_sample_cn.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/5_vision_language_models/notebooks/vlm_usage_sample.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+| VLM 微调 | 学习针对特定任务数据集来微调一个预训练过的 VLM | 🐢 使用一个基本数据集进行微调<br>🐕 尝试使用新数据集 <br>🦁 试验一种不同的微调方法 | [Notebook](./notebooks/vlm_sft_sample_cn.ipynb)| <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/5_vision_language_models/notebooks/vlm_sft_sample.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | 
 
 
-## References  
+## 参考资料  
 - [Hugging Face Learn: Supervised Fine-Tuning VLMs](https://huggingface.co/learn/cookbook/fine_tuning_vlm_trl)
 - [Hugging Face Learn: Supervised Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_smol_vlm_sft_trl)  
 - [Hugging Face Learn: Preference Optimization Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_vlm_dpo_smolvlm_instruct)  
diff --git a/5_vision_language_models/notebooks/vlm_sft_sample.ipynb b/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
index 5af7d6a2..9ddf65a6 100644
--- a/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
+++ b/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
@@ -4,17 +4,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Fine-Tuning a VLM \n",
+    "# 微调一个 VLM \n",
     "\n",
-    "This notebook demonstrates how to fine-tune the `HuggingFaceTB/SmolVLM-Instruct` model using the `SFTTrainer` from the `trl` library. The notebook cells run and will finetune the model. You can select your difficulty by trying out different datasets.\n",
+    "本文将展示如何用 `trl` 库的 `SFTTrainer` 微调 `HuggingFaceTB/SmolVLM-Instruct` 模型。文中的代码均可运行，你可选择不同的微调数据集尝试训练。\n",
     "\n",
     "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Exercise: Fine-Tuning SmolVLM with SFTTrainer</h2>\n",
-    "    <p>Take a dataset from the Hugging Face hub and finetune a model on it. </p> \n",
-    "    <p><b>Difficulty Levels</b></p>\n",
-    "    <p>🐢 Use the `HuggingFaceM4/ChartQA` dataset for SFT training run.</p>\n",
-    "    <p>🐕 Use the fine-tuned to model generate a response, and improve upon the base example.</p>\n",
-    "    <p>🦁 Try out the other datasets and show improvement.</p>\n",
+    "    <h2 style='margin: 0;color:blue'>练习：用 SFTTrainer 微调 SmolVLM </h2>\n",
+    "    <p>从 Hugging Face hub 找一个数据，在该数据集上微调模型 </p> \n",
+    "    <p><b>难度等级</b></p>\n",
+    "    <p>🐢 用 `HuggingFaceM4/ChartQA` 数据集进行 SFT </p>\n",
+    "    <p>🐕 用微调过的模型生成回答，和未微调的模型进行效果对比</p>\n",
+    "    <p>🦁 尝试使用不同的数据集进行微调训练，并对比效果</p>\n",
     "</div>"
    ]
   },
@@ -92,11 +92,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Dataset Preparation\n",
+    "## 数据集准备\n",
     "\n",
-    "We will load a sample dataset and format it for training. The dataset should be structured with input-output pairs, where each input is a prompt and the output is the expected response from the model.\n",
+    "我们将加载一个示例数据集，并对其进行格式化以用于训练。该数据集应构建为输入-输出对的形式，其中每个输入是一个提示语（包含文本和图像），输出是模型预期的响应。\n",
     "\n",
-    "**TRL will format input messages based on the model's chat templates.** They need to be represented as a list of dictionaries with the keys: `role` and `content`,."
+    "**TRL 将根据模型的聊天模板对输入消息进行格式化。** 它们会被整理成一个字典列表，字典包含的 key 有 `role` 和 `content`。"
    ]
   },
   {
@@ -209,7 +209,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Set Up LoRA"
+    "## 配置 LoRA"
    ]
   },
   {
@@ -249,9 +249,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Configuring the Trainer\n",
+    "## 配置 Trainer\n",
     "\n",
-    "The `Trainer` is configured with various parameters that control the training process. These include the number of training steps, batch size, learning rate, and evaluation strategy. Adjust these parameters based on your specific requirements and computational resources."
+    "通过调整 `Trainer` 的各种参数，我们可以控制训练过程。这些参数包括训练步数、batch size、学习率以及评估策略。你可根据具体需求和计算资源来调整这些参数。\n"
    ]
   },
   {
@@ -379,9 +379,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Training the Model\n",
+    "## 训练模型\n",
     "\n",
-    "With the trainer configured, we can now proceed to train the model. The training process will involve iterating over the dataset, computing the loss, and updating the model's parameters to minimize this loss."
+    "训练器配置完成后，我们现在就可以着手训练模型了。训练过程将包括对数据集进行迭代，计算损失值，并更新模型参数以最小化该损失。 "
    ]
   },
   {
@@ -427,13 +427,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 💐 You're done!\n",
+    "## 💐 完成了！\n",
     "\n",
-    "This notebook provided a step-by-step guide to fine-tuning the `HuggingFaceTB/SmolVLM` model using the `SFTTrainer`. By following these steps, you can adapt the model to perform specific tasks more effectively. If you want to carry on working on this course, here are steps you could try out:\n",
+    "本文提供了一份使用 `SFTTrainer` 对 `HuggingFaceTB/SmolVLM` 模型进行微调的分步指南。按照这些步骤操作，你可以让模型更有效地执行特定任务。如果你想继续学习本课程，不妨尝试以下步骤：\n",
     "\n",
-    "- Try this notebook on a harder difficulty\n",
-    "- Review a colleagues PR\n",
-    "- Improve the course material via an Issue or PR."
+    "- 尝试更高难度等级的练习题。\n",
+    "- 对 GitHub 上的 PRs 进行review。\n",
+    "- 通过 GitHub 提出 issue 或 PR，为完善本课程材料贡献力量。\n"
    ]
   }
  ],
diff --git a/5_vision_language_models/notebooks/vlm_usage_sample.ipynb b/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
index 59f29d42..7554d217 100644
--- a/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
+++ b/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
@@ -4,14 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Processing images and text with VLMs \n",
+    "# 用 VLM 处理图片或视频相关的多模态任务\n",
     "\n",
-    "This notebook demonstrates how to utilize the `HuggingFaceTB/SmolVLM-Instruct` 4bit-quantized model for various multimodal tasks such as:\n",
-    "- Visual Question Answering (VQA): Answering questions based on image content.\n",
-    "- Text Recognition (OCR): Extracting and interpreting text in images.\n",
-    "- Video Understanding: Describing videos through sequential frame analysis.\n",
+    "本文以 `HuggingFaceTB/SmolVLM-Instruct` 的 4bit 量化模型为例，展示如何处理以下多模态任务：\n",
+    "- 视觉问题回答（VQA）：基于图片内容回答问题\n",
+    "- 文本识别（OCR）：提取并识别图片中的文本文字\n",
+    "- 视频理解：通过分析一系列的视频帧，来生成对视频的描述\n",
     "\n",
-    "By structuring prompts effectively, you can leverage the model for many applications, such as scene understanding, document analysis, and dynamic visual reasoning."
+    "通过有效地组织提示语（文本 + 视觉），你可以将该模型应用于诸多领域，如场景理解、文档分析以及动态视觉推理。"
    ]
   },
   {
@@ -76,15 +76,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Processing Images"
+    "## 图像级输入的情况"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's start with generating captions and answering questions about an image. We'll also explore processing multiple images.\n",
-    "### 1. Single Image Captioning"
+    "我们先探索生成图片的文本描述，以及针对图片提问的情况。后面我们也会探索多图输入的情况。\n",
+    "\n",
+    "### 1. 单副图片的文本描述生成"
    ]
   },
   {
@@ -182,8 +183,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 2. Comparing Multiple Images\n",
-    "The model can process and compare multiple images. Let's determine the common theme between two images."
+    "### 2. 对比多幅图片\n",
+    "模型也可以处理并对比多幅图片。我们接下来让模型总结一下两张图片的共同主题。"
    ]
   },
   {
@@ -243,9 +244,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 🔠 Text Recognition (OCR)\n",
-    "VLM can also recognize and interpret text in images, making it suitable for tasks like document analysis.\n",
-    "You could try experimenting on images with denser text."
+    "### 🔠 文字识别（OCR）\n",
+    "\n",
+    "VLM 也可以识别并提取文字，这使得它也可以用来处理诸如文档分析之类的任务。\n",
+    "你也可以试试使用文字更密集的图片。"
    ]
   },
   {
@@ -310,14 +312,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Processing videos\n",
+    "## 视频级输入的情况\n",
     "\n",
-    "Visual-Language Models (VLMs) can process videos indirectly by extracting keyframes and reasoning over them in temporal order. While VLMs lack the temporal modeling capabilities of dedicated video models, they can still:\n",
-    "- Describe actions or events by analyzing sampled frames sequentially.\n",
-    "- Answer questions about videos based on representative keyframes.\n",
-    "- Summarize video content by combining textual descriptions of multiple frames.\n",
+    "视觉语言模型（VLMs）可以通过提取关键帧并按时间顺序对其进行推理，从而间接处理视频。虽然视觉语言模型缺乏专用视频模型的时间建模能力，但它们仍然能够：\n",
     "\n",
-    "Let experiment on an example:\n",
+    "- 通过按顺序分析采样帧来描述动作或事件。\n",
+    "- 根据具有代表性的关键帧回答有关视频的问题。\n",
+    "- 通过组合多个帧的文本描述来总结视频内容。\n",
+    "\n",
+    "我们用这个视频作为例子：\n",
     "\n",
     "<video width=\"640\" height=\"360\" controls>\n",
     "  <source src=\"https://cdn.pixabay.com/video/2023/10/28/186794-879050032_large.mp4\" type=\"video/mp4\">\n",
@@ -432,16 +435,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 💐 You're Done!\n",
+    "## 💐 完成了！\n",
+    "\n",
+    "本文展示了使用视觉语言模型的多个案例，按照文中的步骤操作，你可以对视觉语言模型展开很多试验，探索其应用场景。\n",
     "\n",
-    "This notebook demonstrated how to use a Visual-Language Model (VLM) such as formating prompts for multimodal tasks. By following the steps outlined here, you can experiment with VLMs and their applications.\n",
+    "后续你还可以：\n",
     "\n",
-    "### Next Steps to Explore:\n",
-    "- Experiment with more use cases of VLMs.\n",
-    "- Collaborate with a colleague by reviewing their pull requests (PRs).\n",
-    "- Contribute to improving this course material by opening an issue or submitting a PR to introduce new use cases, examples, or concepts.\n",
+    "- 尝试视觉语言模型（VLMs）更多的用例。\n",
+    "- 对 GitHub 上的 PRs 进行review。\n",
+    "- 通过 GitHub 提出 issue 或 PR，为完善本课程材料贡献力量。\n",
     "\n",
-    "Happy exploring! 🌟"
+    "祝你学习愉快！ 🌟"
    ]
   }
  ],
diff --git a/5_vision_language_models/vlm_finetuning.md b/5_vision_language_models/vlm_finetuning.md
index 90ed8f76..45ea6cd6 100644
--- a/5_vision_language_models/vlm_finetuning.md
+++ b/5_vision_language_models/vlm_finetuning.md
@@ -1,14 +1,18 @@
-# VLM Fine-Tuning
-## Efficient Fine-Tuning
+# VLM 微调
 
-### Quantization
-Quantization reduces the precision of model weights and activations, significantly lowering memory usage and speeding up computations. For example, switching from `float32` to `bfloat16` halves memory requirements per parameter while maintaining performance. For more aggressive compression, 8-bit and 4-bit quantization can be used, further reducing memory usage, though at the cost of some accuracy. These techniques can be applied to both the model and optimizer settings, enabling efficient training on hardware with limited resources.
+## 高效微调
+
+### 量化
+
+量化可降低模型权重与激活值的精度，大幅减少内存使用并加快计算速度。例如，从 `float32` 转换为 `bfloat16`，每个参数的内存需求减半，同时还能维持性能。若要进行更激进的压缩，可采用 8 位和 4 位量化，进一步降低内存使用，不过这会牺牲一定的准确性。这些技术既能应用于模型，也能用于优化器设置，从而使在资源有限的硬件上高效训练成为可能。
 
 ### PEFT & LoRA
-As introduced in Module 3, LoRA (Low-Rank Adaptation) focuses on learning compact rank-decomposition matrices while keeping the original model weights frozen. This drastically reduces the number of trainable parameters, cutting resource requirements significantly. LoRA, when integrated with PEFT, enables fine-tuning of large models by only adjusting a small, trainable subset of parameters. This approach is particularly effective for task-specific adaptations, reducing billions of trainable parameters to just millions while maintaining performance.
 
-### Batch Size Optimization
-To optimize the batch size for fine-tuning, start with a large value and reduce it if out-of-memory (OOM) errors occur. Compensate by increasing `gradient_accumulation_steps`, effectively maintaining the total batch size over multiple updates. Additionally, enable `gradient_checkpointing` to lower memory usage by recomputing intermediate states during the backward pass, trading computation time for reduced activation memory requirements. These strategies maximize hardware utilization and help overcome memory constraints.
+专注于学习紧凑的秩分解矩阵，同时保持原始模型的权重不变。这极大地减少了可训练参数的数量，显著降低了资源需求。当 LoRA 与 PEFT 相结合时，仅通过调整一小部分可训练参数就能对大型模型进行微调。这种方法对于特定任务的微调特别有效，它能将数十亿个可训练参数减少到仅数百万个，同时维持性能。
+
+### Batch Size 的优化
+
+为优化微调时的 batch size 大小，可先从较大的值开始，如果出现内存不足（OOM）错误，则将其减小。可通过增加 `gradient_accumulation_steps` 来补偿，从而在多次更新过程中有效地维持总批量大小。此外，启用 `gradient_checkpointing` 可通过在反向传播期间重新计算中间状态来降低内存使用，以计算时间为代价来减少激活内存需求。这些策略可使硬件利用率最大化，并有助于克服内存限制。
 
 ```python
 from transformers import TrainingArguments
@@ -26,77 +30,73 @@ training_args = TrainingArguments(
 )
 ```
 
-## **Supervised Fine-Tuning (SFT)**
-
-Supervised Fine-Tuning (SFT) adapts a pre-trained Vision Language Model (VLM) to specific tasks by leveraging labeled datasets containing paired inputs, such as images and corresponding text. This method enhances the model's ability to perform domain-specific or task-specific functions, such as visual question answering, image captioning, or chart interpretation.
+## **有监督微调（SFT）**
 
-### **Overview**
-SFT is essential when you need a VLM to specialize in a particular domain or solve specific problems where the base model's general capabilities may fall short. For example, if the model struggles with unique visual features or domain-specific terminology, SFT allows it to focus on these areas by learning from labeled data.
+有监督微调（Supervised Fine-tuning 或 SFT）通过利用包含配对输入（例如图像和相应文本）的标注数据集，使预训练的视觉语言模型（VLM）适应特定任务。这种方法可增强模型执行特定领域或特定任务功能的能力，如视觉问答（VQA）、图像文本描述生成、图表解析。
 
-While SFT is highly effective, it has notable limitations:
-- **Data Dependency**: High-quality, labeled datasets tailored to the task are necessary.
-- **Computational Resources**: Fine-tuning large VLMs is resource-intensive.
-- **Risk of Overfitting**: Models can lose their generalization capabilities if fine-tuned too narrowly.
+### **简介**
 
-Despite these challenges, SFT remains a robust technique for enhancing model performance in specific contexts.
-
-
-### **Usage**
-1. **Data Preparation**: Start with a labeled dataset that pairs images with text, such as questions and answers. For example, in tasks like chart analysis, the dataset `HuggingFaceM4/ChartQA` includes chart images, queries, and concise responses.
-
-2. **Model Setup**: Load a pre-trained VLM suitable for the task, such as `HuggingFaceTB/SmolVLM-Instruct`, and a processor for preparing text and image inputs. Configure the model for supervised learning and suitability for your hardware.
-
-3. **Fine-Tuning Process**:
-   - **Formatting Data**: Structure the dataset into a chatbot-like format, pairing system messages, user queries, and corresponding answers.
-   - **Training Configuration**: Use tools like Hugging Face's `TrainingArguments` or TRL's `SFTConfig` to set up training parameters. These include batch size, learning rate, and gradient accumulation steps to optimize resource usage.
-   - **Optimization Techniques**: Use **gradient checkpointing** to save memory during training. Use quantized model to reduce memory requirements and speed up computations.
-   - Employ `SFTTrainer` trainer from the TRL library, to streamline the training process.
+当你需要视觉语言模型（VLM）专门从事某个特定领域，或解决基础模型无法解决的问题时，有监督微调（SFT）就显得至关重要。例如，如果该模型在处理特殊的视觉特征或特定领域的术语方面表现不佳，有监督微调可让它从标注数据中学习，获得处理这些领域的任务的能力。
 
+While SFT is highly effective, it has notable limitations:
+虽然 SFT 非常高效，但它也有很多明显的局限性：
+- **数据依赖**：针对特定任务的高质量标注数据是必须要有的
+- **计算资源**：对大型的 VLM 进行微调需耗费大量计算资源
+- **过拟合的风险**：如果微调范围过窄，模型可能会失去其泛化能力
 
-## Preference Optimization
+尽管存在这些挑战，有监督微调（SFT）仍是一种强大的技术，可在特定场景中提升模型性能。
 
-Preference Optimization, particularly Direct Preference Optimization (DPO), trains a Vision Language Model (VLM) to align with human preferences. Instead of strictly following predefined instructions, the model learns to prioritize outputs that humans subjectively prefer. This approach is particularly useful for tasks involving creative judgment, nuanced reasoning, or varying acceptable answers.
+### **实践方法**
 
+1. **数据准备**：从一个将图像与文本（如问题和答案）进行配对的标注数据集入手。例如，在图表分析这类任务中，“HuggingFaceM4/ChartQA” 数据集包含图表图像、查询内容以及简洁的回答。
+2. **模型设置**：加载适合该任务的预训练视觉语言模型（VLM），比如 `HuggingFaceTB/SmolVLM - Instruct`，以及用于处理文本和图像输入的模型。针对监督学习并结合硬件条件对模型进行配置。
+3. **微调过程**：
+  - **数据格式化**：将数据集构建成类似聊天机器人的格式，把系统消息、用户查询以及相应答案进行配对。
+  - **训练配置**：使用诸如 Hugging Face 的 `TrainingArguments` 或 TRL 的 `SFTConfig` 等工具来设置训练参数。这些参数包括 batch size、学习率和梯度累积步数，以优化资源使用。
+  - **优化技术**：在训练过程中使用 **gradient checkpointing** 来节省内存。采用量化模型降低内存需求并加快计算速度。
+  - 利用 TRL 库中的 `SFTTrainer` 训练器来简化训练流程。
 
-### **Overview**
-Preference Optimization addresses scenarios where subjective human preferences are central to task success. By fine-tuning on datasets that encode human preferences, DPO enhances the model's ability to generate responses that are contextually and stylistically aligned with user expectations. This method is particularly effective for tasks like creative writing, customer interactions, or multi-choice scenarios.
+## 偏好优化
 
-Despite its benefits, Preference Optimization has challenges:
-- **Data Quality**: High-quality, preference-annotated datasets are required, often making data collection a bottleneck.
-- **Complexity**: Training can involve sophisticated processes such as pairwise sampling of preferences and balancing computational resources.
+偏好优化，尤其是直接偏好优化（DPO），旨在训练视觉语言模型（VLM）使其与人类偏好保持一致。该模型并非严格遵循预先设定的指令，而是学习对人类主观上更青睐的输出结果作为优先选择。这种方法在涉及创造性判断、精细化推理或存在多种可接受答案的任务中尤为实用。
 
-Preference datasets must capture clear preferences between candidate outputs. For example, a dataset may pair a question with two responses—one preferred and the other less acceptable. The model learns to predict the preferred response, even if it's not entirely correct, as long as it's better aligned with human judgment.
+### **简介**
 
+偏好优化适用于那些人类主观偏好对任务成功起着关键作用的场景。通过在反映人类偏好的数据集上进行微调，直接偏好优化（DPO）提升了模型生成在上下文和风格上都符合用户期望的回复的能力。这种方法在创意写作、客户互动或多项选择等场景的任务中特别有效。
 
-### **Usage**
-1. **Dataset Preparation**  
-   A preference-labeled dataset is crucial for training. Each example typically consists of a prompt (e.g., an image and question) and two candidate responses: one chosen (preferred) and one rejected. For example:
+尽管偏好优化有诸多益处，但它也面临一些挑战：
 
-   - **Question**: How many families?  
-     - **Rejected**: The image does not provide any information about families.  
-     - **Chosen**: The image shows a Union Organization table setup with 18,000 families.  
+- **数据质量**：需要高质量且带有偏好注释的数据集，这往往使数据收集成为瓶颈。
+- **复杂性**：训练可能涉及复杂的过程，比如对偏好进行成对采样以及平衡计算资源。
 
-   The dataset teaches the model to prioritize better-aligned responses, even if they aren’t perfect. 
+偏好数据集必须清晰体现候选输出之间的偏好差异。例如，一个数据集可能会将一个问题与两个回复配对，一个是受偏好的，另一个则不太能被接受。模型会学习预测出那个受偏好的回复，即便它并非完全正确，只要它更符合人类的判断即可。
 
-2. **Model Setup**  
-   Load a pre-trained VLM and integrate it with Hugging Face's TRL library, which supports DPO, and a processor for preparing text and image inputs. Configure the model for supervised learning and suitability for your hardware.
+### **实践方法**
+1. **数据准备**  
+   准备好一个带有偏好注释的数据集对训练至关重要。每个数据样本一般包含一个问题和一副图片，以及两个候选回答：一个是受偏好的，另一个则不太能被接受。比如：
+   - **问题**: How many families?  
+     - **不倾向的回答**: The image does not provide any information about families.  
+     - **倾向的回答**: The image shows a Union Organization table setup with 18,000 families.  
 
-3. **Training Pipeline**  
-   Training involves configuring DPO-specific parameters. Here's a summary of the process:
+   该数据集引导模型优先选择偏好的回答，即便这些回答并非尽善尽美。
 
-   - **Format Dataset**: Structure each sample with prompts, images, and candidate answers.
-   - **Loss Function**: Use a preference-based loss function to optimize the model for selecting the preferred output.
-   - **Efficient Training**: Combine techniques like quantization, gradient accumulation, and LoRA adapters to optimize memory and computation.
+2. **模型配置**  
+   加载一个预训练的视觉语言模型，准备好处理文本和图像输入的模型，结合 Hugging Face 的 `TRL` 库（支持 DPO 算法）写训练程序。针对监督学习进行模型配置，并使其适配你的硬件。
 
+3. **训练流程** 
+   训练需要配置 DPO 相关参数，主要有这些方向需要注意：
 
+   - **数据集格式**：对每个样本进行结构化，整理好问题、图片、候选回答。
+   - **损失函数**：需要使用和偏向性相关的函数，以此训练模型去选择倾向的回答。
+   - **高效训练**：结合量化、梯度累积、LoRA 等技术，减少显存使用和计算量。
 
-## Resources
+## 学习资源
 
 - [Hugging Face Learn: Supervised Fine-Tuning VLMs](https://huggingface.co/learn/cookbook/fine_tuning_vlm_trl) 
 - [Hugging Face Learn: Supervised Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_smol_vlm_sft_trl)  
 - [Hugging Face Learn: Preference Optimization Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_vlm_dpo_smolvlm_instruct)  
 - [Hugging Face Blog: Preference Optimization for VLMs](https://huggingface.co/blog/dpo_vlm)
 
-## Next Steps
+## 接下来
 
-⏩ Try the [vlm_finetune_sample.ipynb](./notebooks/vlm_finetune_sample.ipynb) to implement this unified approach to preference alignment.
\ No newline at end of file
+⏩ 学习 [vlm_finetune_sample_cn.ipynb](./notebooks/vlm_finetune_sample.ipynb)，动手实现 VLM 的偏好对齐训练。
\ No newline at end of file
diff --git a/5_vision_language_models/vlm_usage.md b/5_vision_language_models/vlm_usage.md
index ef812927..e35e1091 100644
--- a/5_vision_language_models/vlm_usage.md
+++ b/5_vision_language_models/vlm_usage.md
@@ -1,46 +1,50 @@
-# Visual Language Models
+# 视觉语言模型
 
-Visual Language Models (VLMs) bridge the gap between images and text, enabling advanced tasks like generating image captions, answering questions based on visuals, or understanding the relationship between textual and visual data. Their architecture is designed to process both modalities seamlessly.
+视觉语言模型（VLMs）弥合了图像与文本之间的鸿沟，使得诸如生成图像的文字描述、基于视觉内容回答问题，以及理解文本与视觉数据之间的关系等高级任务得以实现。其架构旨在无缝处理这两种模态的数据。
 
-### Architecture
+### 模型架构
 
-VLMs combine image-processing components with text-generation models to achieve a unified understanding. The primary elements of their architecture are:
+视觉语言模型将图像处理组件与文本生成模型相结合，以实现统一的理解。其架构的主要组成部分包括：
 
 ![VLM Architecture](./images/VLM_Architecture.png)
 
-- **Image Encoder**: Transforms raw images into compact numerical representations. Pretrained encoders such as CLIP or vision transformers (ViT) are commonly used.
-- **Embedding Projector**: Maps image features into a space compatible with textual embeddings, often using dense layers or linear transformations.
-- **Text Decoder**: Acts as the language-generation component, translating fused multimodal information into coherent text. Examples include generative models like Llama or Vicuna.
-- **Multimodal Projector**: Provides an additional layer to blend image and text representations. It is critical for models like LLaVA to establish stronger connections between the two modalities.
+- **图像编码器（Image Encoder）**：用来将图片转化为用数值表示的特征，通常使用经训练过的 CLIP 或 ViT。
+- **特征映射模块（Embedding Projector）**：用来将图片特征映射到文本特征空间，使其与文本特征兼容，通常使用全联接层或线性变换实现。
+- **文本解码器（Text Decoder）**：作为文本生成模块，将融合的多模态信息转换成连贯的文字。举例来说，这部分可以用 Llama、Vicuna 等生成模型实现。
+- **多模态映射模块（Multimodal Projector）**：: 提供了一个额外的层，来融合图像与文本的表征。对于像 LLaVA 这样的模型而言，这一层对于在两种模态之间建立更紧密的联系至关重要。
 
-Most VLMs leverage pretrained image encoders and text decoders and align them through additional fine-tuning on paired image-text datasets. This approach makes training efficient while allowing the models to generalize effectively.
+大多数视觉语言模型（VLMs）会利用预训练的图像编码器和文本解码器，并通过在图像-文本这样的成对数据上进行微调，使二者的特征相互对齐。这种方法既能提高训练效率，又能让模型有效地实现泛化。
 
-### Usage
+### 使用方法
 
 ![VLM Process](./images/VLM_Process.png)
 
-VLMs are applied to a range of multimodal tasks. Their adaptability allows them to perform in diverse domains with varying levels of fine-tuning:
+视觉语言模型（VLMs）被应用于一系列多模态任务。凭借其适应性，只需进行不同程度的微调，就能在多个领域发挥作用：
+
+- **图像字幕生成**：为图像生成描述性文字。
+- **视觉问答（VQA）**：针对图像内容回答相关问题。
+- **跨模态检索**：为给定图像找到对应的文本，或反之，为给定文本找到对应的图像。
+- **创意应用**：辅助设计、艺术创作，或生成引人入胜的多媒体内容。
+
 
-- **Image Captioning**: Generating descriptions for images.
-- **Visual Question Answering (VQA)**: Answering questions about the content of an image.
-- **Cross-Modal Retrieval**: Finding corresponding text for a given image or vice versa.
-- **Creative Applications**: Assisting in design, art generation, or creating engaging multimedia content.
 
 ![VLM Usage](./images/VLM_Usage.png)
 
-Training and fine-tuning VLMs depend on high-quality datasets that pair images with text annotations. Tools like Hugging Face's `transformers` library provide convenient access to pretrained VLMs and streamlined workflows for custom fine-tuning.
+训练和微调视觉语言模型（VLMs）依赖于高质量的数据集，这些数据集将图像与文本注释进行配对。像 Hugging Face 的 `transformers` 库这样的工具，为获取预训练的视觉语言模型提供了便捷途径，同时也简化了自定义微调的工作流程。
 
 ### Chat Format
 
-Many VLMs are structured to interact in a chatbot-like manner, enhancing usability. This format includes:
+很多视觉语言模型也是通过像聊天机器人一样的方式与用户互动的。这种格式包含以下信息：
+
+
 
-- A **system message** that sets the role or context for the model, such as "You are an assistant analyzing visual data."
-- **User queries** that combine text inputs and associated images.
-- **Assistant responses** that provide text outputs derived from the multimodal analysis.
+- **系统信息（system message）**：为模型设定角色、上下文，例如“你是一个小助理，将会帮我分析视觉数据”这样。
+- **用户查询（User queries）**：用户询问模型时输入给模型的信息，这包括视觉信息和文本信息
+- **模型回答（Assistant responses）**：模型给出的文本形式的回复
 
-This conversational structure is intuitive and aligns with user expectations, especially for interactive applications like customer service or educational tools.
+这种对话结构直观易懂，符合用户预期，尤其适用于诸如客服或教育工具这类交互式应用程序。
 
-Here’s an example of how a formatted input might look:
+下面就是一个格式化的输入示例：
 
 ```json
 [
@@ -62,15 +66,15 @@ Here’s an example of how a formatted input might look:
 ]
 ```
 
-**Working with Multiple Images and Videos**
+**多图输入或视频输入**
 
-VLMs can also process multiple images or even videos by adapting the input structure to accommodate sequential or parallel visual inputs. For videos, frames can be extracted and processed as individual images, while maintaining temporal order.
+视觉语言模型（VLMs）还能够处理多张图像，甚至是视频。方法是调整输入结构，以适应连续或并行的视觉输入。对于视频，可以提取多帧并将其作为图像进行处理，同时保持时间顺序。
 
-## Resources
+## 学习资源
 
 - [Hugging Face Blog: Vision Language Models](https://huggingface.co/blog/vlms)
 - [Hugging Face Blog: SmolVLM](https://huggingface.co/blog/smolvlm) 
 
-## Next Steps
+## 接下来
 
-⏩ Try the [vlm_usage_sample.ipynb](./notebooks/vlm_usage_sample.ipynb) to try different usages of SMOLVLM.
\ No newline at end of file
+⏩ 你可以尝试 [vlm_usage_sample_cn.ipynb](./notebooks/vlm_usage_sample.ipynb)，了解如何使用 SmolVLM 来处理各种任务。
\ No newline at end of file
diff --git a/6_synthetic_datasets/README.md b/6_synthetic_datasets/README.md
index 57e0d92a..4a760d18 100644
--- a/6_synthetic_datasets/README.md
+++ b/6_synthetic_datasets/README.md
@@ -1,39 +1,39 @@
-# Synthetic Datasets
+# 合成数据集
 
-Synthetic data is artificially generated data that mimics real-world usage. It allows overcoming data limitations by expanding or enhancing datasets. Even though synthetic data was already used for some use cases, large language models have made synthetic datasets more popular for pre- and post-training, and the evaluation of language models.
+合成数据是人工生成的数据，用于模拟现实世界中的真实情况。它能够通过扩充或强化数据集，克服数据方面的局限。尽管合成数据此前已在某些应用场景中有所运用，但大语言模型使得合成数据集在语言模型的预训练、后训练以及评估过程中变得更为流行。
 
-We'll use [`distilabel`](https://distilabel.argilla.io/latest/), a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers. For a deeper dive into the package and best practices, check out the [documentation](https://distilabel.argilla.io/latest/).
+我们将使用 [`distilabel`](https://distilabel.argilla.io/latest/)，这是一个为工程师提供合成数据和人工智能反馈的框架。如果需要基于经验证的研究论文来构建快速、可靠且可扩展的流程，该框架十分适用。如果你想深入了解该框架并学习最佳实践，你可以阅读[官方文档](https://distilabel.argilla.io/latest/)。
 
-## Module Overview
+## 章节概览
 
-Synthetic data for language models can be categorized into three taxonomies: instructions, preferences and critiques. We will focus on the first two categories, which focus on the generation of datasets for instruction tuning and preference alignment. In both categories, we will cover aspects of the third category, which focuses on improving existing data with model critiques and rewrites.
+语言模型领域的合成数据可以分为三类：指令（instructions）、偏好（preferences）和评论（critiques）。我们将重点关注前两类，因为它们涉及为指令微调和偏好对齐生成人工数据。在学习这两类的同时，我们也会涉及第三类某些方面的内容，因为这关乎借助模型评论和重写，来提升现有数据的质量。
 
 ![Synthetic Data Taxonomies](./images/taxonomy-synthetic-data.png)
 
-## Contents
+## 内容目录
 
-### 1. [Instruction Datasets](./instruction_datasets.md)
+### 1. [指令数据集](./instruction_datasets_cn.md)
 
-Learn how to generate instruction datasets for instruction tuning. We will explore creating instruction tuning datasets thorugh basic prompting and using prompts more refined techniques from papers. Instruction tuning datasets with seed data for in-context learning can be created through methods like SelfInstruct and Magpie. Additionally, we will explore instruction evolution through EvolInstruct. [Start learning](./instruction_datasets.md).
+你将学习如何为指令微调（instruction tuning）生成指令数据集，这包括基本的提示语方式以及最近论文提出的更精细的方式。这里面，通过上下文学习并借助种子数据来生成指令微调数据集的方法包括 SelfInstruct 和 Magpie；进化改进指令微调数据的方法包括 EvolInstruct。你可以点击[这里](./instruction_datasets_cn.md)开始学习。
 
-### 2. [Preference Datasets](./preference_datasets.md)
+### 2. [偏好数据集](./preference_datasets_cn.md)
 
-Learn how to generate preference datasets for preference alignment. We will build on top of the methods and techniques introduced in section 1, by generating additional responses. Next, we will learn how to improve such responses with the EvolQuality prompt. Finally, we will explore how to evaluate responses with the the UltraFeedback prompt which will produce a score and critique, allowing us to create preference pairs. [Start learning](./preference_datasets.md).
+你讲学习如何为偏好对齐（preference alignment）去生成偏好数据集。我们将在上一节的基础上，额外生成别的回答。接下来，我们还会学习如何通过 EvolQuality 来优化这些回答。最终，我们会学习如何用 UltraFeedback 生成分数和评论，来评估这些回答，以此来获取偏好数据对。你可以点击[这里](./preference_datasets_cn.md)开始学习。
 
 ### Exercise Notebooks
 
-| Title | Description | Exercise | Link | Colab |
+| 标题 | 概述 | 练习 | 链接 | Colab |
 |-------|-------------|----------|------|-------|
-| Instruction Dataset | Generate a dataset for instruction tuning | 🐢 Generate an instruction tuning dataset <br> 🐕 Generate a dataset for instruction tuning with seed data <br> 🦁 Generate a dataset for instruction tuning with seed data and with instruction evolution | [Link](./notebooks/instruction_sft_dataset.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb) |
-| Preference Dataset | Generate a dataset for preference alignment | 🐢 Generate a preference alignment dataset <br> 🐕 Generate a preference alignment dataset with response evolution <br> 🦁 Generate a preference alignment dataset with response evolution and critiques  | [Link](./notebooks/preference_alignment_dataset.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/6_synthetic_datasets/notebooks/preference_alignment_dataset.ipynb) |
+| 指令微调数据集 | 为指令微调生成数据集 | 🐢 为指令微调生成数据集 <br> 🐕 借助种子数据为指令微调生成数据集 <br> 🦁 借助种子数据和 EvolInstruct 为指令微调生成数据集 | [Link](./notebooks/instruction_sft_dataset_cn.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb) |
+| 偏好数据集 | 为偏好对齐生成数据集 | 🐢 为偏好对齐生成数据集 <br> 🐕 为偏好对齐生成数据集，对回答进行迭代优化 <br> 🦁 为偏好对齐生成数据集，对回答进行迭代优化，并借助对回答的评价 | [Link](./notebooks/preference_alignment_dataset_cn.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/6_synthetic_datasets/notebooks/preference_alignment_dataset.ipynb) |
 
 ## Resources
 
-- [Distilabel Documentation](https://distilabel.argilla.io/latest/)
+- [Distilabel 官方文档](https://distilabel.argilla.io/latest/)
 - [Synthetic Data Generator is UI app](https://huggingface.co/blog/synthetic-data-generator)
 - [SmolTalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk)
-- [Self-instruct](https://arxiv.org/abs/2212.10560)
-- [Evol-Instruct](https://arxiv.org/abs/2304.12244)
-- [Magpie](https://arxiv.org/abs/2406.08464)
-- [UltraFeedback](https://arxiv.org/abs/2310.01377)
-- [Deita](https://arxiv.org/abs/2312.15685)
+- [Self-instruct 论文](https://arxiv.org/abs/2212.10560)
+- [Evol-Instruct 论文](https://arxiv.org/abs/2304.12244)
+- [Magpie 论文](https://arxiv.org/abs/2406.08464)
+- [UltraFeedback 论文](https://arxiv.org/abs/2310.01377)
+- [Deita 论文](https://arxiv.org/abs/2312.15685)
diff --git a/6_synthetic_datasets/instruction_datasets.md b/6_synthetic_datasets/instruction_datasets.md
index c909ee64..f402081a 100644
--- a/6_synthetic_datasets/instruction_datasets.md
+++ b/6_synthetic_datasets/instruction_datasets.md
@@ -1,16 +1,16 @@
-# Generating Instruction Datasets
+# 合成指令微调数据集
 
-Within [the chapter on instruction tuning](../1_instruction_tuning/README.md), we learned about fine-tuning models with Supervised Fine-tuning. In this section, we will explore how to generate instruction datasets for SFT. We will explore creating instruction tuning datasets through basic prompting and using more refined techniques from papers. Instruction tuning datasets with seed data for in-context learning can be created through methods like SelfInstruct and Magpie. Additionally, we will explore instruction evolution through EvolInstruct. Lastly, we will explore how to generate a dataset for instruction tuning using a distilabel pipeline.
+在[指令微调](../1_instruction_tuning/README_CN.md)这一章节，我们学习了通过 SFT 的方法来微调模型。在本文中，我们将学习如何为 SFT 合成指令数据集，这涵盖：使用基本的提示语还生成指令微调数据集，以及使用一些论文中提出的更精细的方法。我们还可以使用种子数据进行上下文学习（in-context learning）来合成数据，相关的方法包括 SelfInstruct 和 Magpie。此外我们还会学习使用 EvolInstruct 来改进现有数据。最后，我们将学习如何使用 `distilabel` 的 `pipeline` 来规范化地进行指令微调数据集的生成。
 
-## From Prompt to Data
+## 从提示语到数据
 
-Synthetic data sounds fancy, but it can be simplified as creating data through effective prompting to extract knowledge from a model. In turn, you can think of this as a way to generate data for a specific task. The challenge is prompting effectively while ensuring the data is diverse and representative. Fortunately, many papers have explored this problem, and we will explore some of the useful ones during this course. First things first, we will explore how to generate synthetic data through manual prompting.
+合成数据听起来很高级，但它可以简单理解为通过有效的提示语从模型中提取知识来创建数据。反过来，你可以将此视为为特定任务生成数据的一种方式。挑战在于要进行有效提示，同时确保数据具有多样性和代表性。幸运的是，已有许多论文探讨了这个问题，在本文中，我们将研究其中一些有价值的论文。首先，我们将探索如何通过手动地提供提示语来生成合成数据。
 
-### Basic Prompting
+### 基本的提示语方式
 
-Let's start with a basic example and load the [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) model using the `transformers` integration of the `distilabel` library. We will use the `TextGeneration` class to generate a synthetic `prompt` and use that to generate a `completion`.
+我们用一个基础的例子开始合成数据的学习。这里我们使用 [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) 模型，并使用 `distilabel` 中已经集成的 `transformers` 库来载入这个模型。在示例中，我们用 `TextGeneration` 类来生成一个 `prompt` 并基于此再生成 `completion`，这样一个问答就生成完毕了。
 
-Next, we will load the model using the `distilabel` library.
+首先，通过 `distilabel` 库来加载模型：
 
 ```python
 from distilabel.llms import TransformersLLM
@@ -21,28 +21,28 @@ gen = TextGeneration(llm=llm)
 gen.load()
 ```
 
-!!! note
-    Distilabel loads the `llm` into memory, so, when working in a notebook, we need to `gen.unload()` after we are done with it to avoid memory issues.
+!!! 注意
+    `distilabel` 会把 `llm` 加载进内存。当我们在一个 notebook 中跑该代码时，我们还需要在结束时执行 `gen.unload()` 来防止内存持续占用。
 
-We will now use the `llm` to generate a `prompt` for instruction tuning.
+接下来，我们开始用 `llm` 来生成一个 `prompt` 作为问题。
 
 ```python
 next(gen.process([{"instruction": "Generate a questions about the Hugging Face Smol-Course on small AI models."}]))
 # What is the purpose of Smol-Course?
 ```
 
-Lastly, we can use that same `prompt` as input to generate a `completion`.
+最后，用 `prompt` 作为问题输入，来生成一个 `completion` 作为回答。
 
 ```python
 next(gen.process([{"instruction": "What is the purpose of Smol-Course?"}]))
 # The Smol-Course is a platform designed to learning computer science concepts.
 ```
 
-Cool! We can generated a synthetic `prompt` and a corresponding `completion`. Re-using this simple approach at scale will allow us to generate a lot more data however, the quality of the data is not that great and does not take into account the nuances of our course or domain. Additionally, re-running the current code shows us the data is not that diverse. Luckily, there are ways to solve this problem.
+这样我们就得到了一个 `prompt` 和一个对应的 `completion`。重复这一简单步骤，就可以得到更多的类似数据。然而，这些数据的质量并不高，且没有考虑到不同任务领域的微妙差别。此外，重复这个过程使得我们的数据集缺乏多样性。不过，我们有办法解决这个问题。
 
 ### SelfInstruct
 
-SelfInstruct is a prompt that generates new instructions based on a seed dataset. This seed data can be a single instruction or a piece of context. The process begins with a pool of initial seed data. The language model is then prompted to generate new instructions based on this seed data using in-context learning. The prompt is [implemented in distilabel](https://github.com/argilla-io/distilabel/blob/main/src/distilabel/steps/tasks/templates/self-instruct.jinja2) and a simplified version is shown below:
+SelfInstruct 基于一个种子数据集生成新的指令数据。一条种子数据可以是一个单一的指令或者一段上下文。该过程通常从一系列初始的种子数据开始。然后语言模型被要求基于种子数据进行上下文学习，针对上下文提出新的问题。在 [distilabel 中的模板](https://github.com/argilla-io/distilabel/blob/main/src/distilabel/steps/tasks/templates/self-instruct.jinja2) 中包含了要求模型执行这一任务的提示语，大致是这样：
 
 ```
 # Task Description
@@ -54,7 +54,7 @@ Develop {{ num_instructions }} user queries that can be received by the given AI
 # Output
 ```
 
-To use it, we need to pass the `llm` to the [SelfInstruct class](https://distilabel.argilla.io/dev/components-gallery/tasks/selfinstruct/). Let's use the text from the [Prompt to Data section](#prompt-to-data) as context and generate a new instruction.
+具体到代码实现上，我们首先要将 `llm` 传入 [SelfInstruct](https://distilabel.argilla.io/dev/components-gallery/tasks/selfinstruct/) 这个类的构造函数。这里我们假设用一段描述如何合成人工数据集的英文文字（就是代码中的`text`）作为上下文，来生成新的指令：
 
 ```python
 from distilabel.steps.tasks import SelfInstruct
@@ -62,17 +62,17 @@ from distilabel.steps.tasks import SelfInstruct
 self_instruct = SelfInstruct(llm=llm)
 self_instruct.load()
 
-context = "<prompt_to_data_section>"
+text = "..."
 
 next(self_instruct.process([{"input": text}]))["instructions"][0]
 # What is the process of generating synthetic data through manual prompting?
 ```
 
-The generated instruction is a lot better already and it fits our actual content and domain. However, we can do even better by improving the prompt through evolution.
+这样生成的指令数据会好很多，且符合我们的实际任务领域。不过，我们还可以做得更好，方法是通过不断进化改进提示语。
 
 ### EvolInstruct
 
-EvolInstruct is a prompting technique that takes an input instruction and evolves it into a better version of the same instruction. This better version is defined according to a set of criteria and adds constraints, deepening, concretizing, reasoning or complications to the original instruction. The process can be repeated multiple times to create various evolutions of the same instruction, ideally leading to a better version of the original instruction. The prompt is [implemented in distilabel](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/evol_instruct) and a simplified version is shown below:
+EvolInstruct 是一种提示语技术，它以已有的指令数据作为输入，不断改进，使得原数据进化成为更好的版本。这里，“更好”是因为我们对数据生成过程添加了新的标准和限制条件，且进行了深化、具体化、推理或复杂化处理。该过程可以重复多次，针对相同的指令数据生成不同的进化版本，获取比原始数据更好的数据。相关的系统提示语[在 distilabel 中也有实现](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/evol_instruct)，大致是这样：
 
 ```
 I want you act as a Prompt Rewriter.
@@ -86,7 +86,7 @@ Complicate the prompt based on the following criteria:
 # Output
 ```
 
-To use it, we need to pass the `llm` to the [EvolInstruct class](https://distilabel.argilla.io/dev/components-gallery/tasks/evolinstruct/). Let's use the synthetic prompt from [the SelfInstruct section](#selfinstruct) as input and evolve it into a better version. For this example, we will only evolve for one generation.
+具体到代码实现上，我们首先要将 `llm` 传入 [SelfInstruct](https://distilabel.argilla.io/dev/components-gallery/tasks/evolinstruct/) 这个类的构造函数。这里我们使用在 [SelfInstruct](#selfinstruct) 这一小节生成的 `prompt` 来作为输入（就是代码中的 `text`），利用它得到一个进化过的版本。本示例中我们只进化一次：
 
 ```python
 from distilabel.steps.tasks import EvolInstruct
@@ -101,13 +101,13 @@ next(evol_instruct.process([{"instruction": text}]))
 # And, how does the artificial intelligence system, GPT4, use machine learning algorithms to manipulate the input data into synthetic data?
 ```
 
-The instruction is now more complex but has lost some of the original meaning. So, take into account that evolving can be a double-edged sword and we need to be careful with the quality of the data we generate.
+经过进化，指令数据变得更复杂了，不过也丧失了一些原有数据的信息。所以我们也要知道，该方法也是一个双刃剑，我们需要对进化过的数据的质量格外关注。
 
 ### Magpie
 
-Magpie is a technique that relies on the auto-regressive factors of language model and the [chat-template](../1_instruction_tuning/chat_templates.md) that has been using during the instruction tuning process. As you might remember, the chat-template is a format that structures conversations with clear role indicators (system, user, assistant). During the instruction tuning phase, the language model has been optimized to reproduce this format and that is exactly what magpie takes advantage of. It starts with a pre-query-prompt based on the chat-template but it stops before the user message indicator, e.g. `<|im_start|>user\n`, and then it uses the language model to generate the user prompt until the end of the assistant indicator, e.g. `<|im_end|>`. This approach allows us to generate a lot of data in a very efficient way and it can even be scaled up to multi-turn conversations. It is hypothesized this generated data reproduces training data from the instruction tuning phase of the model used.
+Magpie 技术利用了语言模型自回归的特性以及前面学习到过的[聊天模板](../1_instruction_tuning/chat_templates_cn.md)。由于聊天模板在指令微调过程中为对话设定了固定的格式，清晰指出了话语是哪个角色说出来的（系统、用户、助手），在预测过程中，模型的输出也会遵循这个格式去走。这正是 Magpie 所利用的点。一开始，Magpie 直接输入给大语言模型一个聊天模板，但这个聊天模板只包含系统信息，结束于用户开始提问的地方（例如在 `<|im_start|>user\n` 这里结束）。然后利用大语言模型的自回归特性，模型会自己不停输出文本，直到输出了助手回答结束符那里（比如输出到 `<|im_end|>` 终止）。这样一段完整的聊天模板就生成了。这种方法可以让我们高效生成数据，并可以扩展到多轮对话情况。很多人也认为这个方法是可以让大语言模型吐出自己指令微调时的训练数据的。
 
-In this scenario, prompt templates differ per model because they are based on the chat-template format. But we can walk through a simplified version of the process step-by-step.
+聊天模板因不同的大语言模型而异，这里我们可以通过下面一个简单的模板，来了解这一技术的具体做法。
 
 ```bash
 # Step 1: provide the pre-query-prompt
@@ -121,7 +121,7 @@ What is the purpose of Smol-Course?
 <|im_end|>
 ```
 
-To use it in distilabel, we need to pass the `llm` to the [Magpie class](https://distilabel.argilla.io/dev/components-gallery/tasks/magpie/).
+具体到代码实现上，我们首先要将 `llm` 传入 [Magpie class](https://distilabel.argilla.io/dev/components-gallery/tasks/magpie/) 这个类的构造函数。
 
 ```python
 from distilabel.steps.tasks import Magpie
@@ -140,24 +140,24 @@ next(magpie.process([{"system_prompt": "You are a helpful assistant."}]))
 # }]
 ```
 
-
-We immediately get a dataset with a `prompt` and `completion` . To improve the performance on our own domain, we can inject additional context into the `system_prompt`. For the LLM to generate specific domain data in combination with Magpie, it helps describing in the system prompt what the users queries will be. This is then used in the  pre-query-prompt before we start generating the user prompt and bias the LLM to generate user queries of that domain.
+通过上述代码，我们可以马上生成一个包含 `prompt` 和 `completion` 的数据集。但如果我们想在特定领域提升数据质量，我们还可以在 `system_prompt` 加入额外的上下文信息。这样可以使得大语言模型在生成数据时，知道用户会接下来问什么问题。具体来说，特定领域信息加在系统提示语中，多多少少可以让模型产出的数据更偏向这一领域。
 
 ```
 You're an AI assistant that will help users solving math problems.
 ```
 
-It's important to write the system prompt as shown above instead of something like:
+例如我们应该按上述方法添加系统信息的内容，而不是下面这种写法。
 
 ```
 You're an AI assistant that generates math problems
 ```
 
-Generally, language models are less optimized for passing additional context to the `system_prompt` so this does not always work as well for customisation as other techniques.
+但总的来说，在训练时，语言模型一般也较少使用包含上下文信息的 `system_prompt`，所以这个方法在自定义情形下也并不是都适用。
+
 
-### From Prompts to Pipelines
+### 从 Prompts 到 Pipelines
 
-The classes we've seen so far are all standalone classes that can be used in a pipeline. This is a good start, but we can do even better by using the `Pipeline` class to generate a dataset. We will use the `TextGeneration` step to generate a synthetic dataset for instruction tuning. The pipeline will consist of a `LoadDataFromDicts` step to load the data, a `TextGeneration` step to generate the `prompt` and a `completion` for that prompt. We will connect the steps and flow the data through the pipeline using the `>>` operator. Within the [documentation of distilabel](https://distilabel.argilla.io/dev/components-gallery/tasks/textgeneration/#input-output-columns) we can see input and output columns of the step. We to ensure that the data flow correctly through the pipeline, we will use the `output_mappings` parameter to map the output columns to the input columns of the next step.
+上面的技术都是通过 `distilabel` 里特定的类去实现的。但为了更规范，我们还可以用  `distilabel` 里的 `Pipeline` 生成数据集。在下面的例子中，我们将使用 `Pipeline` 来为指令为题生成数据集。这个 `Pipeline` 包含 `LoadDataFromDicts` 用以载入种子数据，以及 `TextGeneration` 用以生成 `prompt` 和 `completion`。从种子数据到问题（`prompt`）再到回答（`completion`）的流程，可以用 `>>` 这个运算符串联起来。
 
 ```python
 from distilabel.llms import TransformersLLM
@@ -181,24 +181,24 @@ if __name__ == "__main__":
 # }]
 ```
 
-Under the hood, this pipeline has a lot of cool features. It automatically caches generation results, so we can don't have to re-run the generation steps. There is included fault-tolerance, so if the generation steps fail, the pipeline will continue to run. And the pipeline exexutes all generation steps in parallel, so the generation is faster. We can even visualise the pipeline using the `draw` method. Here you can see how the data flows through the pipeline and how the `output_mappings` are used to map the output columns to the input columns of the next step.
+在 pipeline 的内部，其实也有很多好用的功能。它可以自动缓存生成结果，所以有些时候我们可以不去重新跑生成步骤。它也有容错机制，某一步生成失败了，也会继续跑。此外，pipeline 的数据生成是并行运行，所以速度也快。我们还可以用 `draw` 方法对 pipeline 进行可视化。下图就展示了数据流的走向，也展示了 `output_mappings` 是如何将当前环节的输出作为输入送给下一环节的。
 
 ![Pipeline](./images/pipeline.png)
 
-## Best Practices
+## 最佳实践
 
-- Ensure you have a diverse seed data to cover a wide range of scenarios
-- Regularly evaluate the dataset to ensure generated data is diverse and of high quality
-- Iterate on the (system)prompt to improve the quality of the data
+- 需要确保你的种子数据足够多样，来覆盖足够多的对话场景
+- 定时检查数据集，确保生成的数据足够多样、质量足够好
+- 对系统提示词进行优化，可以改善生成数据的质量
 
-## Next Steps
+## 接下来
 
-👨🏽‍💻 Code -[Exercise Notebook](./notebooks/instruction_sft_dataset.ipynb) to generate a dataset for instruction tuning
-🧑‍🏫 Learn - About [generating preference datasets](./preference_datasets.md)
+👨🏽‍💻 代码 - 通过[练习](./notebooks/instruction_sft_dataset_cn.ipynb)生成一个指令微调的数据集
+🧑‍🏫 阅读 - 学习[合成偏好数据集](./preference_datasets.md)部分
 
 ## References
 
-- [Distilabel Documentation](https://distilabel.argilla.io/latest/)
-- [Self-instruct](https://arxiv.org/abs/2212.10560)
-- [Evol-Instruct](https://arxiv.org/abs/2304.12244)
-- [Magpie](https://arxiv.org/abs/2406.08464)
\ No newline at end of file
+- [Distilabel 官方文档](https://distilabel.argilla.io/latest/)
+- [Self-instruct 论文](https://arxiv.org/abs/2212.10560)
+- [Evol-Instruct 论文](https://arxiv.org/abs/2304.12244)
+- [Magpie 论文](https://arxiv.org/abs/2406.08464)
\ No newline at end of file
diff --git a/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb b/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb
index 54584722..7a88d00a 100644
--- a/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb
+++ b/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb
@@ -4,19 +4,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Generate a dataset for instruction tuning\n",
+    "# 合成指令微调数据集\n",
     "\n",
-    "This notebook will guide you through the process of generating a dataset for instruction tuning. We'll use the `distilabel` package to generate a dataset for instruction tuning.\n",
     "\n",
-    "So let's dig in to some instruction tuning datasets.\n",
+    "本文将介绍如何为指令微调构建人造的数据集。我们将使用 `distilabel` 这个库。\n",
+    "\n",
+    "接下来我们开始实践学习。\n",
     "\n",
     "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Exercise: Generate a dataset for instruction tuning</h2>\n",
-    "    <p>Now that you've seen how to generate a dataset for instruction tuning, try generating a dataset for instruction tuning.</p>\n",
-    "    <p><b>Difficulty Levels</b></p>\n",
-    "    <p>🐢 Generate an instruction tuning dataset</p>\n",
-    "    <p>🐕 Generate a dataset for instruction tuning with seed data</p>\n",
-    "    <p>🦁 Generate a dataset for instruction tuning with seed data and with instruction evolution</p>\n",
+    "    <h2 style='margin: 0;color:blue'>练习：为指令微调生成数据集\t</h2>\n",
+    "    <p>通过学习，你可以尝试为指令微调生成数据集</p>\n",
+    "    <p><b>难度等级</b></p>\n",
+    "    <p>🐢 为指令微调生成数据集</p>\n",
+    "    <p>🐕 借助种子数据为指令微调生成数据集</p>\n",
+    "    <p>🦁 借助种子数据和 EvolInstruct 为指令微调生成数据集</p>\n",
     "</div>"
    ]
   },
@@ -28,9 +29,9 @@
     }
    },
    "source": [
-    "## Install dependencies\n",
+    "## 安装依赖\n",
     "\n",
-    "Instead of transformers, you can also install `vllm` or `hf-inference-endpoints`."
+    "在 `transformers` 之外，你也可以安装 `vllm` 或 `hf-inference-endpoints`。"
    ]
   },
   {
@@ -46,13 +47,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Start synthesizing\n",
+    "## 开始合成数据\n",
+    "\n",
+    "前面已经学过了，我们可以用 `distilabel` 的 pipeline 去执行指令微调数据集的生成。一个最小化的 pipeline 已经提供过了。你可以扩展这个 pipeline 生成更大的数据集。你可以试验一下改变模型、修改生成参数等操作如何影响合成数据集的质量。先在小规模上试验，得出结论后再扩展到大规模生成上。\n",
     "\n",
-    "As we've seen in the previous course content, we can create a distilabel pipelines for instruction dataset generation. The bare minimum pipline is already provided. Make sure to scale up this pipeline to generate a large dataset for instruction tuning. Swap out models, model providers and generation arguments to see how they affect the quality of the dataset. Experiment small, scale up later.\n",
     "\n",
-    "Check out the [distilabel components gallery](https://distilabel.argilla.io/latest/components-gallery/) for information about the processing classes and how to use them. \n",
     "\n",
-    "An example of loading data from the Hub instead of dictionaries is provided below.\n",
+    "你可以阅读 [distilabel](https://distilabel.argilla.io/latest/components-gallery/) 相关文档学习更多处理方法。\n",
+    "\n",
+    "你可以从 Hugging Face Hub 载入种子数据集，而不是像之前提供一个字典类数据。\n",
     "\n",
     "```python\n",
     "from datasets import load_dataset\n",
@@ -65,7 +68,7 @@
     "    distiset = pipeline.run(dataset=dataset)\n",
     "```\n",
     "\n",
-    "Don't forget to push your dataset to the Hub after running the pipeline!"
+    "跑完后，你也可以将你生成的数据上传到 Hugging Face Hub。"
    ]
   },
   {
@@ -95,17 +98,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 🌯 That's a wrap\n",
+    "## 🌯 完成\n",
+    "\n",
+    "现在你已经学会如何为指令微调生成数据集了，你可以用你学到的东西来：\n",
     "\n",
-    "You've now seen how to generate a dataset for instruction tuning. You could use this to:\n",
+    "- 为指令微调生成训练数据集\n",
+    "- 为指令微调生成评测数据集\n",
     "\n",
-    "- Generate a dataset for instruction tuning.\n",
-    "- Create evaluation datasets for instruction tuning.\n",
+    "接下来\n",
     "\n",
-    "Next\n",
+    "🧑‍🏫 阅读 - 学习[合成偏好数据集](./preference_datasets_cn.md)这一节\n",
     "\n",
-    "🧑‍🏫 Learn - About [generating preference datasets](./preference_datasets.md)\n",
-    "🏋️‍♂️ Fine-tune a model for instruction tuning with a synthetic dataset based on the [instruction tuning chapter](../../1_instruction_tuning/README.md)\n"
+    "🏋️‍♂️ 参考[第一章的内容](../../1_instruction_tuning_cn/README.md)，用合成数据集对一个模型进行指令微调\n"
    ]
   }
  ],
diff --git a/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb b/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb
index cd9747df..2e84619f 100644
--- a/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb
+++ b/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb
@@ -4,19 +4,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Generate a dataset for preference alignment\n",
+    "# 为偏好对齐生成数据集\n",
     "\n",
-    "This notebook will guide you through the process of generating a dataset for preference alignment. We'll use the `distilabel` package to generate a dataset for preference alignment.\n",
+    "本文将介绍如何为偏好对齐构建人造的数据集。我们将使用 `distilabel` 这个库。\n",
     "\n",
-    "So let's dig in to some preference alignment datasets.\n",
+    "接下来我们开始实践学习。\n",
     "\n",
     "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Exercise: Generate a dataset for preference alignment</h2>\n",
-    "    <p>Now that you've seen how to generate a dataset for preference alignment, try generating a dataset for preference alignment.</p>\n",
-    "    <p><b>Difficulty Levels</b></p>\n",
-    "    <p>🐢 Generate a dataset for preference alignment</p>\n",
-    "    <p>🐕 Generate a dataset for preference alignment with response evolution</p>\n",
-    "    <p>🦁 Generate a dataset for preference alignment with response evolution and model pooling</p>\n",
+    "    <h2 style='margin: 0;color:blue'>练习：为偏好对齐生成数据集</h2>\n",
+    "    <p>通过学习，你可以尝试为偏好对齐生成数据集</p>\n",
+    "    <p><b>难度等级</b></p>\n",
+    "    <p>🐢 为偏好对齐生成数据集</p>\n",
+    "    <p>🐕 为偏好对齐生成数据集，对回答进行迭代优化</p>\n",
+    "    <p>🦁 为偏好对齐生成数据集，对回答进行迭代优化，并使用 model pooling 技术</p>\n",
     "</div>"
    ]
   },
@@ -28,9 +28,9 @@
     }
    },
    "source": [
-    "## Install dependencies\n",
+    "## 安装依赖\n",
     "\n",
-    "Instead of transformers, you can also install `vllm` or `hf-inference-endpoints`."
+    "在 `transformers` 之外，你也可以安装 `vllm` 或 `hf-inference-endpoints`。"
    ]
   },
   {
@@ -46,13 +46,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Start synthesizing\n",
+    "## 开始合成数据\n",
     "\n",
-    "As we've seen in the previous notebook, we can create a distilabel pipeline for preference dataset generation. The bare minimum pipline is already provided. You can continue work on this pipeline to generate a large dataset for preference alignment. Swap out models, model providers and generation arguments to see how they affect the quality of the dataset. Experiment small, scale up later.\n",
+    "前面已经学过了，我们可以用 `distilabel` 的 pipeline 去执行偏好数据集的生成。一个最小化的 pipeline 已经提供过了。你可以扩展这个 pipeline 生成更大的数据集。你可以试验一下改变模型、修改生成参数等操作如何影响合成数据集的质量。先在小规模上试验，得出结论后再扩展到大规模生成上。\n",
     "\n",
-    "Check out the [distilabel components gallery](https://distilabel.argilla.io/latest/components-gallery/) for information about the processing classes and how to use them. \n",
+    "你可以阅读 [distilabel](https://distilabel.argilla.io/latest/components-gallery/) 相关文档学习更多处理方法。\n",
     "\n",
-    "An example of loading data from the Hub instead of dictionaries is provided below.\n",
+    "你可以从 Hugging Face Hub 载入种子数据集，而不是像之前提供一个字典类数据。\n",
     "\n",
     "```python\n",
     "from datasets import load_dataset\n",
@@ -65,7 +65,7 @@
     "    distiset = pipeline.run(dataset=dataset)\n",
     "```\n",
     "\n",
-    "Don't forget to push your dataset to the Hub after running the pipeline!"
+    "跑完后，你也可以将你生成的数据上传到 Hugging Face Hub。"
    ]
   },
   {
@@ -97,16 +97,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 🌯 That's a wrap\n",
+    "## 🌯 完成\n",
     "\n",
-    "You've now seen how to generate a dataset for preference alignment. You could use this to:\n",
+    "现在你已经学会如何为指令微调生成数据集了，你可以用你学到的东西来：\n",
     "\n",
-    "- Generate a dataset for preference alignment.\n",
-    "- Create evaluation datasets for preference alignment.\n",
+    "- 为偏好对齐生成训练数据集\n",
+    "- 为偏好对齐生成评测数据集\n",
     "\n",
-    "Next\n",
-    "\n",
-    "🏋️‍♂️ Fine-tune a model with preference alignment with a synthetic dataset based on the [preference tuning chapter](../../2_preference_alignment/README.md) \n"
+    "🏋️‍♂️ 参考[第二章的内容](../../2_preference_alignment/README.md)，用合成数据集对一个模型进行偏好对齐\n"
    ]
   }
  ],
diff --git a/6_synthetic_datasets/preference_datasets.md b/6_synthetic_datasets/preference_datasets.md
index 38bb0e02..a1f3a4f8 100644
--- a/6_synthetic_datasets/preference_datasets.md
+++ b/6_synthetic_datasets/preference_datasets.md
@@ -1,16 +1,16 @@
-# Generating Preference Datasets
+# 合成偏好数据集
 
-Within [the chapter on preference alignment](../2_preference_alignment/README.md), we learned about Direct Preference Optimization. In this section, we will explore how to generate preference datasets for methods like DPO. We will build on top of the methods that were introduced in [generating instruction datasets](./instruction_datasets.md). Additionally, we will show how to add extra completions to the dataset using basic prompting or by using EvolQuality to improve the quality of responses. Lastly, we will show how UltraFeedback can be used to generate scores and critiques.
+在[前面章节](../2_preference_alignment/README_CN.md)，我们学习了直接偏好优化（DPO）。这里我们将学习如何为 DPO 之类的偏好优化算法生成训练数据集。我们的方法将基于前面[合成指令微调数据集](./instruction_datasets_cn.md)部分。此外，我们将展示如何通过基本提示或使用 EvolQuality 来添加额外的回答，以提高回答质量。最后，我们将展示如何使用 UltraFeedback 生成评分和评论。
 
-## Creating multiple completions
+## 生成多个回答
 
-Preference data is a dataset with multiple `completions` for the same `instruction`. We can add more `completions` to a dataset by prompting a model to generate them. When doing this, we need to ensure that the second completion is not too similar to the first completion in terms of overall quality and phrasing. This is important because the model needs to be optimized for a clear preference. We want to know which completion is preferred over the other, normally referred to as `chosen` and `rejected`. We will go into more detail about determining chosen and rejected completions in the [section on creating scores](#creating-scores).
+偏好数据集需要针对一个问题（`instruction`）的多个回答（`completions`）。通过提示语让模型生成两个回答当然是可行的，但我们要确保第二个回答不要和第一个过于相似（从质量和词汇上说）。这对训练非常重要，因为模型需要通过明显的差异来区分人类偏好。这里我们还需要判断哪个回答是我们倾向的（`chosen`）、哪个是我们不倾向的（`rejected`）。在[生成分数](#生成分数)部分我们会讲解。 
 
 ### Model pooling
 
-You can use models from different model families to generate a second completion, which is called model pooling. To further improve the quality of the second completion, you can use different generation arguments, like tweaking the `temperature`. Lastly, you can use different prompt templates or system prompts to generate a second completion to ensure diversity based on specific characteristics defined in the template. In theory, we could take two models of varying quality and use the better one as the `chosen` completion.
+Model pooling 是一个很简单的方法：你可以使用不同的模型针对同意问题生成不同的回答。如果想进一步改进第二个回答的质量，你还可以调节不同的生成参数，如 `temperature`。通过不同的提示语模板或系统提示语，你也可以生成有多样性的回答。理论上讲，用两个不同质量的模型生成两个回答，选择质量好的那个回答就可以完成这件事情。
 
-Let's start with model pooling by loading the [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) and [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) models using the `transformers` integration of the `distilabel` library. Using these models, we will create two synthetic `responses` for a given `prompt`. We will create another pipeline with `LoadDataFromDicts`, `TextGeneration`, and `GroupColumns`. We will first load data, then use two generation steps, and then group the results. We connect the steps and flow the data through the pipeline using the `>>` operator and `[]`, which means that we want to use the output of the previous step as the input for both steps within the list.
+这里我们通过 [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) 和 [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) 这两个模型进行 model pooling。针对每一个问题，我们都可以生成两个回答。同样地，我们首先用 `LoadDataFromDicts` 载入种子数据，然后用 `>>` 运算符串联 pipeline 的数据流，用 `[]` 取结果。
 
 ```python
 from distilabel.llms import TransformersLLM
@@ -36,13 +36,13 @@ if __name__ == "__main__":
 # ]}
 ```
 
-As you can see, we have two synthetic `completions` for the given `prompt`. We could have boosted diversity by initializing the `TextGeneration` steps with a specific `system_prompt` or by passing generation arguments to the `TransformersLLM`. Let's now see how we can improve the quality of the `completions` using EvolQuality.
+可以看到，我们这里获取到了两个回答。如果想提升回答的多样性，还可以在 `TextGeneration` 这一步提供 `system_prompt` 或给 `TransformersLLM` 传递生成参数。接下来我们看看怎样用 EvolQuality 进一步提升回答的质量。
 
 ### EvolQuality
 
-EvolQuality is similar to [EvolInstruct](./instruction_datasets.md#evolinstruct) - it is a prompting technique but it evolves `completions` instead of the input `prompt`. The task takes both a `prompt` and `completion` and evolves the `completion` into a version that better responds to the `prompt` based on a set of criteria. This better version is defined according to criteria for improving helpfulness, relevance, deepening, creativity, or details. Because this automatically generates a second completion, we can use it to add more `completions` to a dataset. In theory, we could even assume the evolution is better than the original completion and use it as the `chosen` completion out of the box.
+EvolQuality 和 [EvolInstruct](./instruction_datasets_cn.md#evolinstruct) 类似，都是提示语技术。但 EvolQuality 改进的是回答的质量，而不是输入的问题。EvolQuality 会把问题和回答都作为输入，然后根据提供的标准不断修改进化回答。根据有帮助度、相关性、深度、创造性和其它细节，我们定义一个更好的回答。这个更好的回答就可以加入数据集。理论上，我们可以认为这个“更好的回答”就是我们偏好优化时倾向的回答。
 
-The prompt is [implemented in distilabel](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/evol_quality) and a simplified version is shown below:
+具体的提示语[在 distilabel 已有实现](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/evol_quality)，这里我们展示一个简化版本：
 
 ```bash
 I want you act as a Response Rewriter.
@@ -59,7 +59,7 @@ Complicate the prompt based on the following criteria:
 # Improved Response
 ```
 
-Let's use the [EvolQuality class](https://distilabel.argilla.io/dev/components-gallery/tasks/evolquality/) to evolve the synthetic `prompt` and `completion` from [the Model Pooling section](#model-pooling) into a better version. For this example, we will only evolve for one generation.
+代码实现上，我们需要用 [EvolQuality](https://distilabel.argilla.io/dev/components-gallery/tasks/evolquality/) 这个类。这里代码只进化迭代一次。
 
 ```python
 from distilabel.llms import TransformersLLM
@@ -79,21 +79,21 @@ next(evol_quality.process([{
 # The process of generating synthetic data through manual prompting involves creating artificial data sets that mimic real-world usage patterns.
 ```
 
-The `response` is now more complex and specific to the `instruction`. This is a good start, but as we have seen with EvolInstruct, evolved generations are not always better. Hence, it is important to use additional evaluation techniques to ensure the quality of the dataset. We will explore this in the next section.
+可以看到，回答与问题对应，且已经更复杂了。这是个好的开端，但如果你做过 `EvolInstruct` 的实验，就会知道这种算法得到的“更好”回答有时并没有变好。所以我们还需要加入额外的测评技术，确保数据集不出现错误标注。接下来部分就会讨论这一技术。
 
-## Creating Scores
+## 生成分数
 
-Scores are a measure of how much one response is preferred over another. In general, these scores can be absolute, subjective, or relative. For this course, we will focus on the first two because they are most valuable for creating preference datasets. This scoring is a way of judging and evaluating using language models and therefore has some overlap with the evaluation techniques we have seen in [the chapter on evaluation](../3_evaluation/README.md). As with the other evaluation techniques, scores and evaluations normally require larger models to better align with human preferences.
+分数是衡量一个回答相较于另一个回答受偏好程度的指标。一般来说，这些分数可以是绝对的、主观的或相对的。在本课程中，我们将重点关注前两种，因为它们对于创建偏好数据集最为有用。这种评分方式是一种利用语言模型进行判断和评估的方法，因此与我们在[评测](../4_evaluation/README_CN.md)这一章中看到的评估技术存在一些重叠。与其他评估技术一样，分数和评估通常需要更大的模型，以便更好地契合人类偏好。
 
 ### UltraFeedback
 
-UltraFeedback is a technique that generates scores and critiques for a given `prompt` and its `completion`.
+UltraFeedback 可以对给出的问题和回答生成分数和评价。
 
-The scores are based on the quality of the `completion` according to a set of criteria. There are four fine-grained criteria: `helpfulness`, `relevance`, `deepening`, and `creativity`. These are useful but generally speaking, using the overall criteria is a good start, which allows us to simplify the process of generating scores. The scores can be used to determine which `completion` is the `chosen` and which is the `rejected` one. Because they are absolute, they can also be used as interesting filters for outliers in the dataset, either finding the worst completions or the pairs with more or less difference.
+回答的分数一般基于一定的标准给出。有四类细化的标准：有用性、相关性、深度、创造性。借助这些标准，可以简化评分过程。得到分数后，我们就可以决定哪个回答是倾向的（`chosen`），哪个是不倾向的（`rejected`）。因为分数是绝对的，它也可以被用来过滤质量极低或区分度不高的数据。
 
-The critiques are added to provide reasoning for the score. They can be used as extra context to help us understand the differences between the scores. The language model generates extensive critiques which is very useful, but this also introduces extra cost and complexity to the process because generating critiques is more expensive than generating a single token to represent a score.
+此外，评价信息可以为得分提供一些逻辑道理，可以用来理解分数造成差异的原因。
 
-The prompt is [implemented in distilabel](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/templates/ultrafeedback) and a simplified version is shown below:
+UltraFeedback 对应的提示语在 [distilabel 中也有实现](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/templates/ultrafeedback)，这里提供简化版本：
 
 ```bash
 Evaluate the model's outputs based on various criteria: Helpfulness, Relevance, Deepening, Creativity
@@ -111,7 +111,7 @@ Answer with the following format: score - rationale
 # Score - Rationale
 ```
 
-Let's use the [UltraFeedback class](https://distilabel.argilla.io/dev/components-gallery/tasks/ultrafeedback/) to evaluate the synthetic `prompt` and `completion` from [the Model Pooling section](#model-pooling).
+在代码层面，我们使用 [UltraFeedback](https://distilabel.argilla.io/dev/components-gallery/tasks/ultrafeedback/) 类，评估取自 [Model Pooling](#model-pooling) 这一部分的合成的 `prompt` 和 `completion`。
 
 ```python
 from distilabel.llms import TransformersLLM
@@ -137,19 +137,19 @@ next(ultrafeedback.process([{
 # ]
 ```
 
-## Best Practices
+## 最佳实践
 
-- Overall scores are cheaper and easier to generate than critiques and specific scores
-- Use bigger models to generate scores and critiques
-- Use a diverse set of models to generate scores and critiques
-- Iterate on configuration of the `system_prompt` and models
+- 相比于生成分数和评价，只生成分数一般会更简单、更少耗费资源
+- 用更大的模型去生成分数和评价
+- 建议使用一系列有多样性的模型，去生成分数和评价
+- 不断优化 `system_prompt` 和模型
 
-## Next Steps
+## 接下来
 
-👨🏽‍💻 Code -[Exercise Notebook](./notebooks/preference_dpo_dataset.ipynb) to generate a dataset for instruction tuning
+👨🏽‍💻 代码 - 通过[练习](./notebooks/preference_dpo_dataset.ipynb) 去生成一个偏好对齐的数据集
 
-## References
+## 参考资料
 
-- [Distilabel Documentation](https://distilabel.argilla.io/latest/)
-- [Deita](https://arxiv.org/abs/2312.15685)
-- [UltraFeedback](https://arxiv.org/abs/2310.01377)
+- [Distilabel 官方文档](https://distilabel.argilla.io/latest/)
+- [Deita 论文](https://arxiv.org/abs/2312.15685)
+- [UltraFeedback 论文](https://arxiv.org/abs/2310.01377)
diff --git a/7_inference/README.md b/7_inference/README.md
index 41df86d5..d05ff2fc 100644
--- a/7_inference/README.md
+++ b/7_inference/README.md
@@ -1,36 +1,36 @@
-# Inference
+# 大模型推理
 
-Inference is the process of using a trained language model to generate predictions or responses. While inference might seem straightforward, deploying models efficiently at scale requires careful consideration of various factors like performance, cost, and reliability. Large Language Models (LLMs) present unique challenges due to their size and computational requirements.
+推理是指用经过训练的语言模型来进行预测或生成回答的过程。尽管推理看似简单直接，但要大规模高效部署模型，就需要仔细考量诸如性能、成本和可靠性等多种因素。由于大语言模型（LLMs）的规模和计算需求，模型推理存在着一些独特的挑战。
 
-We'll explore both simple and production-ready approaches using the [`transformers`](https://huggingface.co/docs/transformers/index) library and [`text-generation-inference`](https://github.com/huggingface/text-generation-inference), two popular frameworks for LLM inference. For production deployments, we'll focus on Text Generation Inference (TGI), which provides optimized serving capabilities.
+这里，我们借助 [`transformers`](https://huggingface.co/docs/transformers/index) 和 [`text-generation-inference`](https://github.com/huggingface/text-generation-inference) 来探索简单的推理方法以及适用于生产环境的推理方法。针对生产环境的模型部署，我们将重点关注 Text Generation Inference 这个工具包（简称 TGI），它提供了优化过的服务能力。
 
-## Module Overview
+## 章节概览
 
-LLM inference can be categorized into two main approaches: simple pipeline-based inference for development and testing, and optimized serving solutions for production deployments. We'll cover both approaches, starting with the simpler pipeline approach and moving to production-ready solutions.
+大语言模型的推理可以分为两类：简单的使用 pipeline 进行推理，这适用于开发和测试阶段；优化过的服务级推理方案，者适用于生产环境的部署。我们将会讲解这两类方法，从简单的 pipeline 方法开始，再逐步深入到生产环境部署方案。
 
-## Contents
+## 内容目录
 
-### 1. [Basic Pipeline Inference](./pipeline_inference.md)
+### 1. [基本的 Pipeline 推理](./inference_pipeline_cn.md)
 
-Learn how to use the Hugging Face Transformers pipeline for basic inference. We'll cover setting up pipelines, configuring generation parameters, and best practices for local development. The pipeline approach is perfect for prototyping and small-scale applications. [Start learning](./pipeline_inference.md).
+这一节你将了解使用 Hugging Face Transformers 的 pipeline 进行基本的推理方法。我们将讲解 pipeline 的设置、生成参数的配置，以及本地部署的最佳实践。该方法适用于原型设计和小规模应用。
 
-### 2. [Production Inference with TGI](./tgi_inference.md)
+### 2. [使用 TGI 进行生产环境部署](./text_generation_inference_cn.md)
 
-Learn how to deploy models for production using Text Generation Inference. We'll explore optimized serving techniques, batching strategies, and monitoring solutions. TGI provides production-ready features like health checks, metrics, and Docker deployment options. [Start learning](./text_generation_inference.md).
+这一节你讲学习如何使用 Text Generation Inference 这个工具包进行生产环境的模型部署。我们将探索服务端模型部署的优化技术、组 batch 推理的策略，以及如何监控。TGI 提供了很多适用于生产环境的功能，如健康监测、评估指标、Docker 部署等。
 
-### Exercise Notebooks
+### 练习
 
-| Title | Description | Exercise | Link | Colab |
+| 标题 | 概述 | 练习 | 链接 | Colab |
 |-------|-------------|----------|------|-------|
-| Pipeline Inference | Basic inference with transformers pipeline | 🐢 Set up a basic pipeline <br> 🐕 Configure generation parameters <br> 🦁 Create a simple web server | [Link](./notebooks/basic_pipeline_inference.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/7_inference/notebooks/basic_pipeline_inference.ipynb) |
-| TGI Deployment | Production deployment with TGI | 🐢 Deploy a model with TGI <br> 🐕 Configure performance optimizations <br> 🦁 Set up monitoring and scaling | [Link](./notebooks/tgi_deployment.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/7_inference/notebooks/tgi_deployment.ipynb) |
+| 使用 Pipeline 推理 | 使用 transformers 的 pipeline 进行推理 | 🐢 创建一个 pipeline <br> 🐕 配置生成参数 <br> 🦁 创建一个简单的网页端服务 | [Link](./notebooks/basic_pipeline_inference_cn.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/7_inference/notebooks/basic_pipeline_inference.ipynb) |
+| 使用 TGI 部署 | 在生产环境进行 TGI 部署 | 🐢 用 TGI 部署一个模型 <br> 🐕 调节参数，优化性能 <br> 🦁 监控和扩展 | [Link](./notebooks/tgi_deployment_cn.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/7_inference/notebooks/tgi_deployment.ipynb) |
 
-## Resources
+## 参考资料
 
-- [Hugging Face Pipeline Tutorial](https://huggingface.co/docs/transformers/en/pipeline_tutorial)
-- [Text Generation Inference Documentation](https://huggingface.co/docs/text-generation-inference/en/index)
-- [Pipeline WebServer Guide](https://huggingface.co/docs/transformers/en/pipeline_tutorial#using-pipelines-for-a-webserver)
-- [TGI GitHub Repository](https://github.com/huggingface/text-generation-inference)
-- [Hugging Face Model Deployment Documentation](https://huggingface.co/docs/inference-endpoints/index)
+- [Hugging Face 的 Pipeline 教程](https://huggingface.co/docs/transformers/en/pipeline_tutorial)
+- [Text Generation Inference 官方文档](https://huggingface.co/docs/text-generation-inference/en/index)
+- [基于 Pipeline 创建 WebServer 指南](https://huggingface.co/docs/transformers/en/pipeline_tutorial#using-pipelines-for-a-webserver)
+- [TGI GitHub 代码仓库](https://github.com/huggingface/text-generation-inference)
+- [Hugging Face 模型部署文档](https://huggingface.co/docs/inference-endpoints/index)
 - [vLLM: High-throughput LLM Serving](https://github.com/vllm-project/vllm)
-- [Optimizing Transformer Inference](https://huggingface.co/blog/optimize-transformer-inference)
+- [优化 Transformer 模型推理](https://huggingface.co/blog/optimize-transformer-inference)
diff --git a/7_inference/inference_pipeline.md b/7_inference/inference_pipeline.md
index e7f5c2c7..f9d72fd3 100644
--- a/7_inference/inference_pipeline.md
+++ b/7_inference/inference_pipeline.md
@@ -1,34 +1,34 @@
-# Basic Inference with Transformers Pipeline
+# 使用 transformers 的 pipeline 进行基本的模型推理
 
-The `pipeline` abstraction in 🤗 Transformers provides a simple way to run inference with any model from the Hugging Face Hub. It handles all the preprocessing and postprocessing steps, making it easy to use models without deep knowledge of their architecture or requirements.
+🤗 Transformers 中的 `pipeline` 抽象，为使用 Hugging Face 模型库中的任何模型进行推理提供了一种简单的方法。它处理了所有的预处理和后处理步骤，使得在无需深入了解模型架构或要求的情况下就能轻松使用这些模型。
 
-## How Pipelines Work
+## pipeline 的工作原理
 
-Hugging Face pipelines streamline the machine learning workflow by automating three critical stages between raw input and human-readable output:
+Hugging Face 的 pipeline 通过将原始输入和人类可读输出之间的三个关键阶段自动化，简化了机器学习工作流程：
 
-**Preprocessing Stage**
-The pipeline first prepares your raw inputs for the model. This varies by input type:
-- Text inputs undergo tokenization to convert words into model-friendly token IDs
-- Images are resized and normalized to match model requirements
-- Audio is processed through feature extraction to create spectrograms or other representations
+**预处理阶段**
+pipeline 首先将你的原始输入为模型做好准备。这会因输入类型而异：
+- 文本输入会经过分词处理，将单词转换为对模型友好的 token ID
+- 图像会被调整大小并进行归一化处理，以符合模型要求
+- 音频会通过特征提取进行处理，以创建频谱图或其他表示形式
 
-**Model Inference**
-During the forward pass, the pipeline:
-- Handles batching of inputs automatically for efficient processing
-- Places computation on the optimal device (CPU/GPU)
-- Applies performance optimizations like half-precision (FP16) inference where supported
+**模型推理**
+在正向传播过程中，pipeline 实现了这些事情：
+- 自动进行输入的 batch 处理，以实现高效处理
+- 选择最优计算设备（CPU/GPU）进行计算
+- 如果硬件可以支持，会使用诸如半精度（FP16）推理等技术进行性能优化
 
-**Postprocessing Stage**
-Finally, the pipeline converts raw model outputs into useful results:
-- Decodes token IDs back into readable text
-- Transforms logits into probability scores
-- Formats outputs according to the specific task (e.g., classification labels, generated text)
+**后处理阶段**
+最后，pipeline 将原始的模型输出转换为有用的结果：
+- 将 token ID 解码回可读文本
+- 将 logits 值转换为概率值
+- 根据具体任务（例如分类标签、生成文本），对输出进行格式化
 
-This abstraction lets you focus on your application logic while the pipeline handles the technical complexity of model inference.
+这种抽象让你可以专注于应用程序逻辑，而管道会处理模型推理的技术复杂性。
 
-## Basic Usage
+## 基本用法
 
-Here's how to use a pipeline for text generation:
+下面示例展示如何使用 pipeline 进行文本生成：
 
 ```python
 from transformers import pipeline
@@ -51,9 +51,9 @@ response = generator(
 print(response[0]['generated_text'])
 ```
 
-## Key Configuration Options
+## 关键配置
 
-### Model Loading
+### 载入模型
 ```python
 # CPU inference
 generator = pipeline("text-generation", model="HuggingFaceTB/SmolLM2-1.7B-Instruct", device="cpu")
@@ -70,23 +70,23 @@ generator = pipeline(
 )
 ```
 
-### Generation Parameters
+### 生成相关的参数
 
 ```python
 response = generator(
     "Translate this to French:",
-    max_new_tokens=100,     # Maximum length of generated text
-    do_sample=True,         # Use sampling instead of greedy decoding
-    temperature=0.7,        # Control randomness (higher = more random)
-    top_k=50,               # Limit to top k tokens
-    top_p=0.95,             # Nucleus sampling threshold
-    num_return_sequences=1  # Number of different generations
+    max_new_tokens=100,     # 生成文本的最大长度
+    do_sample=True,         # 解码时用采样的策略，而不是贪心策略
+    temperature=0.7,        # 这个参数可以控制随机性，值越大越随机
+    top_k=50,               # 采样时，只考虑最靠前的前 k 个 token
+    top_p=0.95,             # 采样时，概率值的阈值
+    num_return_sequences=1  # 针对一个输入输出几个输出
 )
 ```
 
-## Processing Multiple Inputs
+## 同时处理多个输入
 
-Pipelines can efficiently handle multiple inputs through batching:
+Pipeline 可以借助 batch 的技术，高效地同时处理多个输入：
 
 ```python
 # Prepare multiple prompts
@@ -111,9 +111,9 @@ for prompt, response in zip(prompts, responses):
     print(f"Response: {response[0]['generated_text']}\n")
 ```
 
-## Web Server Integration
+## 集成入网页端服务
 
-Here's how to integrate a pipeline into a FastAPI application:
+下面示例展示了如何将 pipeline 集成入 FastAPI 应用：
 
 ```python
 from fastapi import FastAPI, HTTPException
@@ -151,19 +151,20 @@ if __name__ == "__main__":
     uvicorn.run(app, host="0.0.0.0", port=5000)
 ```
 
-## Limitations
+## 局限性
 
-While pipelines are great for prototyping and small-scale deployments, they have some limitations:
+虽然 Pipeline 对于原型设计和小规模部署很有用，但它们存在一些局限性：
 
-- Limited optimization options compared to dedicated serving solutions
-- No built-in support for advanced features like dynamic batching
-- May not be suitable for high-throughput production workloads
+- 与专用服务解决方案相比，优化选项有限。
+- 不支持动态 batch 处理等高级特性。
+- 可能不适合高吞吐量的生产工作负载。
 
-For production deployments with high throughput requirements, consider using Text Generation Inference (TGI) or other specialized serving solutions.
+对于有高吞吐量要求的生产部署，可考虑使用 TGI 或其他专门的服务解决方案。
 
-## Resources
 
-- [Hugging Face Pipeline Tutorial](https://huggingface.co/docs/transformers/en/pipeline_tutorial)
-- [Pipeline API Reference](https://huggingface.co/docs/transformers/en/main_classes/pipelines)
-- [Text Generation Parameters](https://huggingface.co/docs/transformers/en/main_classes/text_generation)
-- [Model Quantization Guide](https://huggingface.co/docs/transformers/en/perf_infer_gpu_one)
\ No newline at end of file
+## 参考资料
+
+- [Hugging Face 的 Pipeline 教程](https://huggingface.co/docs/transformers/en/pipeline_tutorial)
+- [Pipeline API 参考资料](https://huggingface.co/docs/transformers/en/main_classes/pipelines)
+- [Text Generation 参数文档](https://huggingface.co/docs/transformers/en/main_classes/text_generation)
+- [模型量化指南](https://huggingface.co/docs/transformers/en/perf_infer_gpu_one)
\ No newline at end of file
diff --git a/7_inference/text_generation_inference.md b/7_inference/text_generation_inference.md
index 9cf20622..01f6cb41 100644
--- a/7_inference/text_generation_inference.md
+++ b/7_inference/text_generation_inference.md
@@ -1,24 +1,25 @@
 # Text Generation Inference (TGI)
 
-Text Generation Inference (TGI) is a toolkit developed by Hugging Face for deploying and serving Large Language Models (LLMs). It's designed to enable high-performance text generation for popular open-source LLMs. TGI is used in production by Hugging Chat - An open-source interface for open-access models.
+Text Generation Inference（简称 TGI）是一个由 Hugging Face 开发的工具包，主要用于对大语言模型进行部署和服务搭建。它旨在为常用的开源大语言模型（LLMs）实现高性能文本生成。TGI 被 Hugging Chat 用于实际生产，Hugging Chat 是一个面向开源模型的开源交互界面。
 
-## Why Use Text Generation Inference?
 
-Text Generation Inference addresses the key challenges of deploying large language models in production. While many frameworks excel at model development, TGI specifically optimizes for production deployment and scaling. Some key features include:
+## 为什么使用 TGI？
 
-- **Tensor Parallelism**: TGI's can split models across multiple GPUs through tensor parallelism, essential for serving larger models efficiently.
-- **Continuous Batching**: The continuous batching system maximizes GPU utilization by dynamically processing requests, while optimizations like Flash Attention and Paged Attention significantly reduce memory usage and increase speed.
-- **Token Streaming**: Real-time applications benefit from token streaming via Server-Sent Events, delivering responses with minimal latency.
+TGI 解决了在生产环境中部署大语言模型的关键难题。虽然许多框架在模型开发方面表现出色，但 TGI 专门针对生产部署和扩展进行了优化。它的一些关键特性包括：
 
-## How to Use Text Generation Inference
+- **张量并行**：TGI 能够通过张量并行技术将模型分割到多个 GPU 上，这对于高效部署更大的模型至关重要。
+- **连续 batch 处理**：连续 batch 处理系统通过动态处理请求，最大限度地提高 GPU 利用率，同时诸如 Flash Attention 和 Paged Attention 等优化方法显著降低了内存使用并提高了速度。
+- **token streaming**：实时应用程序受益于通过服务器发送事件（Server-Sent Events）实现的 token streaming 技术，以最低延迟提供响应。
 
-### Basic Python Usage
+## 怎样使用 TGI
 
-TGI uses a simple yet powerful REST API integration which makes it easy to integrate with your applications. 
+### 基本 Python 用法
 
-### Using the REST API
+TGI 采用了一种简洁而强大的 REST API 集成方式，这使得它能轻松与你的应用程序整合。
 
-TGI exposes a RESTful API that accepts JSON payloads. This makes it accessible from any programming language or tool that can make HTTP requests. Here's a basic example using curl:
+### 使用 REST API
+
+TGI 提供了一个接受 JSON 数据的 RESTful API 。这使得任何能够发出 HTTP 请求的编程语言或工具都可以访问它。以下是一个使用 curl 的基本示例：
 
 ```bash
 # Basic generation request
@@ -42,9 +43,10 @@ curl localhost:8080/v1/chat/completions \
     -H 'Content-Type: application/json'
 ```
 
-### Using the `huggingface_hub` Python Client
+### 使用 `huggingface_hub` 的 Python 客户端
+
+`huggingface_hub` 的 Python 客户端可以处理连接管理、请求格式化以及响应解析。示例如下：
 
-The `huggingface_hub` python client client handles connection management, request formatting, and response parsing. Here's how to get started.
 
 ```python
 from huggingface_hub import InferenceClient
@@ -68,9 +70,9 @@ for chunk in output:
 ```
 
 
-### Using OpenAI API
+### 使用 OpenAI 格式的 API
 
-Many libraries support the OpenAI API, so you can use the same client to interact with TGI.
+很多代码库支持 OpenAI API，你也可以用对应的客户端请求去和 TGI 进行互动。
 
 ```python
 from openai import OpenAI
@@ -95,13 +97,13 @@ for message in chat_completion:
     print(message)
 ```
 
-## Preparing Models for TGI
+## 为 TGI 部署准备模型
 
-To serve a model with TGI, ensure it meets these requirements:
+如果想借助 TGI 给模型构建服务端，你还需要执行以下步骤：
 
-1. **Supported Architecture**: Verify your model architecture is supported (Llama, BLOOM, T5, etc.)
+1. **支持的模型架构**：检查你的模型结构，看看是否受 TGI 支持（目前支持 Llama、BLOOM、T5 等)
 
-2. **Model Format**: Convert weights to safetensors format for faster loading:
+2. **模型格式**：将模型权重转换为 safetensors 格式，加速模型参数加载
 
 ```python
 from safetensors.torch import save_file
@@ -112,7 +114,7 @@ state_dict = model.state_dict()
 save_file(state_dict, "model.safetensors")
 ```
 
-3. **Quantization** (optional): Quantize your model to reduce memory usage:
+1. **量化**（可选步骤）：对模型进行量化，以减少内存使用：
 
 ```python
 from transformers import BitsAndBytesConfig
@@ -128,9 +130,9 @@ model = AutoModelForCausalLM.from_pretrained(
 )
 ```
 
-## References
+## 参考资料
 
-- [Text Generation Inference Documentation](https://huggingface.co/docs/text-generation-inference)
-- [TGI GitHub Repository](https://github.com/huggingface/text-generation-inference)
+- [Text Generation Inference 官方文档](https://huggingface.co/docs/text-generation-inference)
+- [TGI 在 GitHub 上的代码仓库](https://github.com/huggingface/text-generation-inference)
 - [Hugging Face Model Hub](https://huggingface.co/models)
-- [TGI API Reference](https://huggingface.co/docs/text-generation-inference/api_reference)
\ No newline at end of file
+- [TGI 的 API 参考文档](https://huggingface.co/docs/text-generation-inference/api_reference)
\ No newline at end of file
diff --git a/8_agents/README.md b/8_agents/README.md
index 1fd01c48..3c5b3ed8 100644
--- a/8_agents/README.md
+++ b/8_agents/README.md
@@ -1,36 +1,36 @@
-# Agents
+# 智能体
 
-AI Agents are autonomous systems that can understand user requests, break them down into steps, and execute actions to accomplish tasks. They combine language models with tools and external functions to interact with their environment. This module covers how to build effective agents using the [`smolagents`](https://github.com/huggingface/smolagents) library, which provides a lightweight framework for creating capable AI agents.
+AI 智能体（AI Agents）是一种自主系统，能够理解用户请求、将请求拆解为多个步骤，然后执行相应行动以完成任务。它们将语言模型与工具及外部功能相结合，从而与所处环境进行交互。本模章将讲解如何使用 [`smolagents`](https://github.com/huggingface/smolagents) 库构建高效的智能体，该库为创建强大的 AI 智能体提供了一个轻量级框架。
 
-## Module Overview
+## 章节概览
 
-Building effective agents requires understanding three key components. First, retrieval capabilities allow agents to access and use relevant information from various sources. Second, function calling enables agents to take concrete actions in their environment. Finally, domain-specific knowledge and tooling equip agents for specialized tasks like code manipulation.
+高效的智能体通常具备三种关键能力。一是检索能力，智能体需要能够从各种信息来源获取和使用相关信息。二是函数调用，这使得智能体能够在所处环境中采取具体行动。最后是特定领域的知识和工具，这能让智能体可以执行诸如代码操作这类专业任务。
 
-## Contents
+## 内容目录
 
-### 1️⃣ [Retrieval Agents](./retrieval_agents.md)
+### 1️⃣ [检索智能体](./retrieval_agents_cn.md)
 
-Retrieval agents combine models with knowledge bases. These agents can search and synthesize information from multiple sources, leveraging vector stores for efficient retrieval and implementing RAG (Retrieval Augmented Generation) patterns. They are great at combining web search with custom knowledge bases while maintaining conversation context through memory systems. The module covers implementation strategies including fallback mechanisms for robust information retrieval.
+检索型智能体将模型与知识库相结合。这些智能体能够从多个来源搜索并整合信息，借助向量数据库实现高效检索，并采用检索增强生成（Retrieval Augmented Generation 或 RAG）模式。它们擅长将网页搜索与自定义知识库相融合，同时通过记忆系统维持对话上下文。这部分教程主要讲解其实现策略，包括用于稳健信息检索的回退机制。
 
-### 2️⃣ [Code Agents](./code_agents.md)
+### 2️⃣ [代码智能体](./code_agents_cn.md)
 
-Code agents are specialized autonomous systems designed for software development tasks. These agents excel at analyzing and generating code, performing automated refactoring, and integrating with development tools. The module covers best practices for building code-focused agents that can understand programming languages, work with build systems, and interact with version control while maintaining high code quality standards.
+代码智能体是为软件开发任务而设计的专门化自主系统。这些智能体擅长分析和生成代码、执行自动重构，并与开发工具相集成。这部分教程主要讲解如何构建以代码为核心的智能体，以及最佳实践。这些智能体能够理解编程语言、与构建系统（build systems）协作，并与版本控制系统交互，同时保持较高的代码质量标准。
 
-### 3️⃣ [Custom Functions](./custom_functions.md)
+### 3️⃣ [自定义函数](./custom_functions_cn.md)
 
-Custom function agents extend basic AI capabilities through specialized function calls. This module explores how to design modular and extensible function interfaces that integrate directly with your application's logic. You'll learn to implement proper validation and error handling while creating reliable function-driven workflows. The focus is on building simple systems where agents can predictably interact with external tools and services.
+自定义函数智能体通过专门的函数调用扩展了基本的人工智能能力。这部分教程将探讨如何设计模块化和可扩展的函数接口，使其直接与应用程序的逻辑相集成。你将学习在创建可靠的函数驱动的工作流时，如何实施适当的验证和错误处理。重点在于构建简单的系统，在该系统中智能体可以以可预测的方式与外部工具和服务进行交互。
 
-### Exercise Notebooks
+### 练习
 
-| Title | Description | Exercise | Link | Colab |
+| 标题 | 概述 | 练习 | 链接 | Colab |
 |-------|-------------|----------|------|-------|
-| Building a Research Agent | Create an agent that can perform research tasks using retrieval and custom functions | 🐢 Build a simple RAG agent <br> 🐕 Add custom search functions <br> 🦁 Create a full research assistant | [Notebook](./notebooks/agents.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/8_agents/notebooks/agents.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+| 构建科研智能体 | 构建一个借助检索和自定义函数可以执行科研任务的智能体 | 🐢 构建简单的 RAG 智能体 <br> 🐕 加入自定义的搜索函数 <br> 🦁 创建完整的科研助手 | [Notebook](./notebooks/agents_cn.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/8_agents/notebooks/agents.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
 
 ## Resources
 
-- [smolagents Documentation](https://huggingface.co/docs/smolagents) - Official docs for the smolagents library
-- [Building Effective Agents](https://www.anthropic.com/research/building-effective-agents) - Research paper on agent architectures
-- [Agent Guidelines](https://huggingface.co/docs/smolagents/tutorials/building_good_agents) - Best practices for building reliable agents
-- [LangChain Agents](https://python.langchain.com/docs/how_to/#agents) - Additional examples of agent implementations
-- [Function Calling Guide](https://platform.openai.com/docs/guides/function-calling) - Understanding function calling in LLMs
-- [RAG Best Practices](https://www.pinecone.io/learn/retrieval-augmented-generation/) - Guide to implementing effective RAG
+- [smolagents Documentation](https://huggingface.co/docs/smolagents) - smolagents 代码库的官方文档
+- [Building Effective Agents](https://www.anthropic.com/research/building-effective-agents) - 智能体架构方面的研究性论文
+- [Agent Guidelines](https://huggingface.co/docs/smolagents/tutorials/building_good_agents) - 构建可靠智能体的最佳实践
+- [LangChain Agents](https://python.langchain.com/docs/how_to/#agents) - 智能体实现的一些示例
+- [Function Calling Guide](https://platform.openai.com/docs/guides/function-calling) - 理解 LLM 中的函数调用
+- [RAG Best Practices](https://www.pinecone.io/learn/retrieval-augmented-generation/) - 高效 RAG 实现指南
diff --git a/8_agents/code_agents.md b/8_agents/code_agents.md
index 62d89a22..ed79de98 100644
--- a/8_agents/code_agents.md
+++ b/8_agents/code_agents.md
@@ -1,16 +1,16 @@
-# Code Agents
+# 代码智能体
 
-Code agents are specialized autonomous systems that handle coding tasks like analysis, generation, refactoring, and testing. These agents leverage domain knowledge about programming languages, build systems, and version control to enhance software development workflows.
+代码智能体是专门的自主系统，可处理诸如分析、生成、重构和测试之类的代码任务。这些智能体利用关于编程语言、构建系统（build systems）和版本控制的领域知识来增强软件开发工作流程。
 
-## Why Code Agents?
+## 为什么我们需要代码智能体？
 
-Code agents accelerate development by automating repetitive tasks while maintaining code quality. They excel at generating boilerplate code, performing systematic refactoring, and identifying potential issues through static analysis. The agents combine retrieval capabilities to access external documentation and repositories with function calling to execute concrete actions like creating files or running tests.
+代码智能体通过将重复性任务自动化，同时保持代码质量，从而加速开发过程。它们擅长生成样板代码、进行系统性重构，并通过静态分析识别潜在问题。这些智能体将检索能力（访问外部文档和代码库）与函数调用相结合，以执行创建文件或运行测试等具体操作。
 
-## Building Blocks of a Code Agent
+## 代码智能体的组成
 
-Code agents are built on specialized language models fine-tuned for code understanding. These models are augmented with development tools like linters, formatters, and compilers to interact with real-world environments. Through retrieval techniques, agents maintain contextual awareness by accessing documentation and code histories to align with organizational patterns and standards. Action-oriented functions enable agents to perform concrete tasks such as committing changes or initiating merge requests.
+代码智能体建立在为代码理解而微调过的专门语言模型之上。这些模型辅以诸如代码检查工具、格式化工具和编译器等开发工具，以便与现实世界环境进行交互。通过检索技术，智能体通过访问文档和代码历史记录来保持上下文感知，以符合组织的模式和标准。以行动为导向的函数使智能体能够执行具体任务，例如提交变更或发起合并请求。
 
-In the following example, we create a code agent that can search the web using DuckDuckGo much like the retrieval agent we built earlier.
+在以下示例中，我们创建一个代码智能体，它可以像我们之前构建的检索智能体那样使用 DuckDuckGo 进行网络搜索。
 
 ```python
 from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
@@ -20,7 +20,7 @@ agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=HfApiModel())
 agent.run("How many seconds would it take for a leopard at full speed to run through Pont des Arts?")
 ```
 
-In the following example, we create a code agent that can get the travel time between two locations. Here, we use the `@tool` decorator to define a custom function that can be used as a tool.
+在以下示例中，我们创建了一个代码智能体，它能够获取两个位置之间的运动时间。这里，我们使用 `@tool` 装饰器来定义一个可用作工具的自定义函数。
 
 ```python
 from smolagents import CodeAgent, HfApiModel, tool
@@ -57,12 +57,12 @@ agent.run("Can you give me a nice one-day trip around Paris with a few locations
 
 ```
 
-These examples are just the beginning of what you can do with code agents. You can learn more about how to build code agents in the [smolagents documentation](https://huggingface.co/docs/smolagents).
+当然，这两个例子仅仅是你开发代码智能体的开始。你需要阅读 [smolagents 文档](https://huggingface.co/docs/smolagents)来了解更多构建代码智能体的方法。
 
-smolagents provides a lightweight framework for building code agents, with a core implementation of approximately 1,000 lines of code. The framework specializes in agents that write and execute Python code snippets, offering sandboxed execution for security. It supports both open-source and proprietary language models, making it adaptable to various development environments.
+`smolagents` 为构建代码智能体提供了一个轻量级的框架，其核心代码实现仅有 1000 行左右。该框架专注于构建能够编写和执行 Python 代码片段的智能体，并提供沙盒式执行环境以确保安全性。它同时支持开源和专有语言模型，使其能够适配各种开发环境。
 
-## Further Reading
+## 延伸阅读
 
-- [smolagents Blog](https://huggingface.co/blog/smolagents) - Introduction to smolagents and code interactions
-- [smolagents: Building Good Agents](https://huggingface.co/docs/smolagents/tutorials/building_good_agents) - Best practices for reliable agents
-- [Building Effective Agents - Anthropic](https://www.anthropic.com/research/building-effective-agents) - Agent design principles
+- [smolagents Blog](https://huggingface.co/blog/smolagents) - 介绍 smolagents 的博客
+- [smolagents: Building Good Agents](https://huggingface.co/docs/smolagents/tutorials/building_good_agents) - 构建可靠智能体的最佳实践
+- [Building Effective Agents - Anthropic](https://www.anthropic.com/research/building-effective-agents) - 智能体设计原则
diff --git a/8_agents/custom_functions.md b/8_agents/custom_functions.md
index ea590ca5..57b80298 100644
--- a/8_agents/custom_functions.md
+++ b/8_agents/custom_functions.md
@@ -1,30 +1,32 @@
-# Custom Function Agents
+# 自定义函数智能体
 
-Custom Function Agents are AI agents that leverage specialized function calls (or “tools”) to perform tasks. Unlike general-purpose agents, Custom Function Agents focus on powering advanced workflows by integrating directly with your application's logic. For example, you can expose database queries, system commands, or any custom utility as isolated functions for the agent to invoke.
+自定义函数智能体是一类借助专门的函数调用（或称为 tools）来执行任务的人工智能智能体。与通用智能体不同，自定义函数智能体专注于通过直接与应用程序的逻辑相集成，为高级工作流程提供支持。例如，你可以将数据库查询、系统命令或任何自定义实用程序作为独立函数开放，以供智能体调用。
 
-## Why Custom Function Agents?
+## 为什么需要自定义函数智能体？
 
-- **Modular and Extensible**: Instead of building one monolithic agent, you can design individual functions that represent discrete capabilities, making your architecture more extensible.
-- **Fine-Grained Control**: Developers can carefully control the agent’s actions by specifying exactly which functions are available and what parameters they accept.
-- **Improved Reliability**: By structuring each function with clear schemas and validations, you reduce errors and unexpected behaviors.
+- **模块化与可扩展性**：无需构建一个庞大的单体智能体，你可以设计代表不同独立功能的单个函数，使你的架构更具可扩展性。
 
-## Basic Workflow
+- **细粒度控制**：开发人员可以通过明确指定哪些函数可用以及它们接受哪些参数，来精细地控制智能体的行为。
 
-1. **Identify Functions**  
-   Determine which tasks can be transformed into custom functions (e.g., file I/O, database queries, streaming data processing).
+- **增强可靠性**：通过为每个函数构建清晰的模式和验证机制，可减少错误和意外行为。
 
-2. **Define the Interface**  
-   Use a function signature or schema that precisely outlines each function’s inputs, outputs, and expected behavior. This enforces strong contracts between your agent and its environment.
+## 基本工作流程
 
-3. **Register with the Agent**  
-   Your agent needs to “learn” which functions are available. Typically, you pass metadata describing each function’s interface to the language model or agent framework.
+1. **确定函数**
+   确定哪些任务可以转化为自定义函数（例如，文件输入输出、数据库查询、流数据处理）。
 
-4. **Invoke and Validate**  
-   Once the agent selects a function to call, run the function with the provided arguments and validate the results. If valid, feed the results back to the agent for context to drive subsequent decisions.
+2. **定义接口**
+   使用函数签名或模式精确概述每个函数的输入、输出以及预期行为。这在智能体与其运行环境之间建立了严格的契约。
 
-## Example
+3. **向智能体注册**
+   智能体需要 “了解” 哪些函数可用。通常，你将描述每个函数接口的元数据传递给语言模型或智能体框架。
 
-Below is a simplified example demonstrating how custom function calls might look in pseudocode. The objective is to perform a user-defined search and retrieve relevant content:
+4. **调用与验证**
+   一旦智能体选择要调用的函数，使用提供的参数运行该函数并验证结果。如果结果有效，将结果反馈给智能体作为上下文，以推动后续决策。
+
+## 示例
+
+以下是一个简化示例，以伪代码展示自定义函数调用可能的形式。目标是执行用户定义的搜索并检索相关内容：
 
 ```python
 # Define a custom function with clear input/output types
@@ -63,7 +65,7 @@ def process_search():
         logging.info("No results found for query")
 ```
 
-## Further Reading
+## 延伸阅读
 
-- [smolagents Blog](https://huggingface.co/blog/smolagents) - Learn about the latest advancements in AI agents and how they can be applied to custom function agents.
-- [Building Good Agents](https://huggingface.co/docs/smolagents/tutorials/building_good_agents) - A comprehensive guide on best practices for developing reliable and effective custom function agents.
\ No newline at end of file
+- [smolagents Blog](https://huggingface.co/blog/smolagents) - 介绍 smolagents 的博客
+- [Building Good Agents](https://huggingface.co/docs/smolagents/tutorials/building_good_agents) - 构建可靠智能体的最佳实践
\ No newline at end of file
diff --git a/8_agents/notebooks/agents.ipynb b/8_agents/notebooks/agents.ipynb
index 1f197346..54de1d6e 100644
--- a/8_agents/notebooks/agents.ipynb
+++ b/8_agents/notebooks/agents.ipynb
@@ -4,13 +4,14 @@
             "cell_type": "markdown",
             "metadata": {},
             "source": [
-                "# Building AI Agents\n",
+                "# 构建 AI 智能体\n",
                 "\n",
-                "This notebook contains exercises to help you learn how to build different types of agents using the `smolagents` library. We'll progress from basic to more complex implementations.\n",
                 "\n",
-                "## Setup\n",
+                "本文包含一些练习，旨在帮助你学习如何使用 `smolagents` 库构建不同类型的智能体。我们将从基础实现逐步过渡到更复杂的实现。\n",
                 "\n",
-                "First, let's install the required packages:"
+                "## 准备\n",
+                "\n",
+                "首先我们要安装需要用到的包："
             ]
         },
         {
@@ -34,9 +35,9 @@
             "cell_type": "markdown",
             "metadata": {},
             "source": [
-                "## 🐢 Exercise 1: Basic Code Agent\n",
+                "## 🐢 练习 1：基本的代码智能体\n",
                 "\n",
-                "Let's start by creating a simple code agent that can answer programming-related questions using web search."
+                "我们从创建一个简单的代码智能体开始，它可以通过网页搜索回答一些编程相关的问题。"
             ]
         },
         {
@@ -59,20 +60,20 @@
             "cell_type": "markdown",
             "metadata": {},
             "source": [
-                "### 🤔 Exercise 1 Challenge\n",
-                "Try asking the agent to explain different programming concepts and evaluate its responses. How well does it handle:\n",
-                "1. Basic syntax questions\n",
-                "2. Language-specific features\n",
-                "3. Code examples"
+                "### 🤔 练习 1 挑战项\n",
+                "试着让智能体解释不同的编程概念，并评估它的回复。它在以下方面的表现如何：\n",
+                "1. 基本的语法问题\n",
+                "2. 语言特定的特性\n",
+                "3. 代码示例"
             ]
         },
         {
             "cell_type": "markdown",
             "metadata": {},
             "source": [
-                "## 🐕 Exercise 2: Agent with Custom Functions\n",
+                "## 🐕 练习 2：带有自定义函数的智能体\n",
                 "\n",
-                "Now let's create an agent that can perform specific tasks using custom functions. We'll implement a simple calculator tool."
+                "现在我们创建一个可以通过自定义的函数去执行特定任务的智能体。这里我们创建一个简单的计算器，让它作为自定义的工具。"
             ]
         },
         {
@@ -125,19 +126,19 @@
             "cell_type": "markdown",
             "metadata": {},
             "source": [
-                "### 🤔 Exercise 2 Challenge\n",
-                "1. Add more mathematical operations to the calculator tool\n",
-                "2. Create a new custom tool (e.g., for string manipulation or date calculations)\n",
-                "3. Combine multiple custom tools in one agent"
+                "### 🤔 练习 2 挑战项\n",
+                "1. 往计算器这个工具多加入点数学运算功能\n",
+                "2. 创建一个新的自定义工具（例如，字符串处理或日期计算）\n",
+                "3. 把多个工具结合入一个智能体中"
             ]
         },
         {
             "cell_type": "markdown",
             "metadata": {},
             "source": [
-                "## 🦁 Exercise 3: Advanced Retrieval Agent\n",
+                "## 🦁 练习 3：改进检索智能体\n",
                 "\n",
-                "Finally, let's build a more sophisticated agent that combines web search with memory to maintain context across conversations."
+                "最后，让我们构建一个更复杂的智能体，它将网络搜索和记忆相结合，以便在对话中保持上下文连贯。"
             ]
         },
         {
@@ -166,9 +167,9 @@
             "metadata": {},
             "source": [
                 "### 🤔 Exercise 3 Challenge\n",
-                "1. Test how well the agent maintains context across different topics\n",
-                "2. Implement a custom knowledge base tool (as shown in the retrieval_agents.md example)\n",
-                "3. Create a hybrid agent that combines code understanding with research capabilities"
+                "1. 测试智能体在不同主题下保持上下文的能力如何。\n",
+                "2. 实现一个自定义知识库工具（如 [这里](retrieval_agents_cn.md) 所示）。\n",
+                "3. 创建一个混合智能体，将代码理解能力与研究能力相结合。"
             ]
         }
     ],
diff --git a/8_agents/retrieval_agents.md b/8_agents/retrieval_agents.md
index 97c6ed0b..523eb30d 100644
--- a/8_agents/retrieval_agents.md
+++ b/8_agents/retrieval_agents.md
@@ -1,12 +1,12 @@
-# Building Agentic RAG Systems
+# 构建自主性检索增强生成（RAG）系统
 
-Agentic RAG (Retrieval Augmented Generation) combines the power of autonomous agents with knowledge retrieval capabilities. While traditional RAG systems simply use an LLM to answer queries based on retrieved information, agentic RAG takes this further by allowing the system to intelligently control its own retrieval and response process.
+自主性检索增强生成（RAG 或 Retrieval Augmented Generation）将有自主性的智能体的能力与知识检索能力相结合。传统的 RAG 系统只是利用大语言模型（LLM），基于检索到的信息来回答询问。而自主性 RAG 更进一步，允许系统智能地控制自身的检索和回答过程。
 
-Traditional RAG has key limitations - it only performs a single retrieval step and relies on direct semantic similarity with the user query, which can miss relevant information. Agentic RAG addresses these challenges by empowering the agent to formulate its own search queries, critique results, and perform multiple retrieval steps as needed.
+传统 RAG 存在关键局限 —— 它仅执行单一的检索步骤，且依赖与用户询问的直接语义相似性，这可能会遗漏相关信息。自主性 RAG 通过让智能体能够自行制定搜索查询、评估结果，并根据需要执行多个检索步骤，来应对这些挑战。
 
-## Basic Retrieval with DuckDuckGo
+## 使用 DuckDuckGo 实现基本检索
 
-Let's start by building a simple agent that can search the web using DuckDuckGo. This agent will be able to answer questions by retrieving relevant information and synthesizing responses.
+我们首先构建一个简单的智能体，它可以用 DuckDuckGo 去搜索网页。这个智能体可以通过检索相关信息并整合，来回答问题。
 
 ```python
 from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
@@ -29,15 +29,15 @@ response = agent.run(
 print(response)
 ```
 
-The agent will:
-1. Analyze the query to determine what information is needed
-2. Use DuckDuckGo to search for relevant content
-3. Synthesize the retrieved information into a coherent response
-4. Store the interaction in its memory for future reference
+这个智能体背后所做的事情是：
+1. 分析这个问题，确定需要哪些参考信息
+2. 使用 DuckDuckGo 这个搜索引擎寻找相关内容
+3. 整合检索到的信息，形成条理清晰的回答
+4. 在内存中保存本次交互信息，以供将来参考
 
-## Custom Knowledge Base Tool
+## 自定义知识库
 
-For domain-specific applications, we often want to combine web search with our own knowledge base. Let's create a custom tool that can query a vector database of technical documentation.
+针对特定领域的应用，我们通常希望在网络搜索之外再加入特有的知识库作为参考。接下来我们就创建一个自定义的工具，用以从一个技术文档的向量数据库中进行查询。
 
 ```python
 from smolagents import Tool
@@ -75,22 +75,23 @@ class RetrieverTool(Tool):
 retriever_tool = RetrieverTool(docs_processed)
 ```
 
-This enhanced agent can:
-1. First check the documentation for relevant information
-2. Fall back to web search if needed
-3. Combine information from both sources
-4. Maintain conversation context through memory
+这个增强版的智能体背后所做的事情是：
+1. 首先检查文档，看有没有相关信息First check the documentation for relevant information
+2. 如有需要，也进行网页搜索
+3. 集成两个信息源的信息
+4. 在内存中保存对话的上下文
 
-## Enhanced Retrieval Capabilities
+## 增强检索能力
 
-When building agentic RAG systems, the agent can employ sophisticated strategies like:
+当构建自主性检索增强生成（RAG）系统时，智能体还可以借鉴以下复杂策略：
 
-1. Query Reformulation - Instead of using the raw user query, the agent can craft optimized search terms that better match the target documents
-2. Multi-Step Retrieval - The agent can perform multiple searches, using initial results to inform subsequent queries
-3. Source Integration - Information can be combined from multiple sources like web search and local documentation
-4. Result Validation - Retrieved content can be analyzed for relevance and accuracy before being included in responses
+1. 查询重构：智能体并非直接使用用户的原始查询，而是精心设计优化后的搜索词，使其与目标文档更匹配。
+2. 多步检索：智能体可以执行多次搜索，依据初始检索结果来调整后续的查询。
+3. 来源整合：整合来自多个渠道的信息，如网页搜索和本地文档。
+4. 结果验证：在将检索到的内容纳入回复之前，会对其相关性和准确性进行分析。
+
+要构建有效的自主性检索增强生成（RAG）系统，需要仔细考量几个关键方面。智能体应依据查询类型和上下文，在可用工具中做出选择。记忆系统有助于保存对话历史，避免重复检索。制定回退策略可确保即便首选的检索方法失效，系统仍能发挥作用。此外，实施验证步骤有助于保证检索信息的准确性和相关性。
 
-Effective agentic RAG systems require careful consideration of several key aspects. The agent should select between available tools based on the query type and context. Memory systems help maintain conversation history and avoid repetitive retrievals. Having fallback strategies ensures the system can still provide value even when primary retrieval methods fail. Additionally, implementing validation steps helps ensure the accuracy and relevance of retrieved information.
 
 ```python
 import datasets
@@ -116,6 +117,6 @@ text_splitter = RecursiveCharacterTextSplitter(
 docs_processed = text_splitter.split_documents(source_docs)
 ```
 
-## Next Steps
+## 接下来
 
-⏩ Check out the [Code Agents](./code_agents.md) module to learn how to build agents that can manipulate code.
+⏩ 学习[代码智能体](./code_agents_cn.md)部分，了解如何构建一个可以处理代码任务的智能体。
diff --git a/README.md b/README.md
index acb0cc20..864d772d 100644
--- a/README.md
+++ b/README.md
@@ -1,32 +1,34 @@
 ![smolcourse image](./banner.png)
 
-# a smol course
+# 关于 smol 的课程
 
-This is a practical course on aligning language models for your specific use case. It's a handy way to get started with aligning language models, because everything runs on most local machines. There are minimal GPU requirements and no paid services. The course is based on the [SmolLM2](https://github.com/huggingface/smollm/tree/main) series of models, but you can transfer the skills you learn here to larger models or other small language models.
+本课程是一个实践性课程，重点关注如何调整大语言模型，使其对齐到特定领域或任务中。对齐大语言模型到特定领域非常易于实操，因为其中的绝大部分工作都可以在本地机器上完成，需要的 GPU 成本非常低。本课程将使用 [SmolLM2](https://github.com/huggingface/smollm/tree/main) 系列的模型，但相关技能对于其它大语言模型同样适用。
 
 <a href="http://hf.co/join/discord">
 <img src="https://img.shields.io/badge/Discord-7289DA?&logo=discord&logoColor=white"/>
 </a>
 
 <div style="background: linear-gradient(to right, #e0f7fa, #e1bee7, orange); padding: 20px; border-radius: 5px; margin-bottom: 20px; color: purple;">
-    <h2>Participation is open, free, and now!</h2>
-    <p>This course is open and peer reviewed. To get involved with the course <strong>open a pull request</strong> and submit your work for review. Here are the steps:</p>
+    <h2>欢迎参与！</h2>
+    <p>本课程完全免费开放，并已经过同行评审。你可以通过 <strong> pull request </strong> 参与课程，并提交你的成果。具体步骤如下：</p>
     <ol>
-        <li>Fork the repo <a href="https://github.com/huggingface/smol-course/fork">here</a></li>
-        <li>Read the material, make changes, do the exercises, add your own examples.</li>
-        <li>Open a PR on the december_2024 branch</li>
-        <li>Get it reviewed and merged</li>
+        <li>在 <a href="https://github.com/huggingface/smol-course/fork">这里</a> fork 一下代码仓库 </li>
+        <li>阅读学习材料，完成练习，提交你的成果。</li>
+        <li>向 december_2024 分支上提出 PR</li>
+        <li>等待 PR 通过</li>
     </ol>
-    <p>This should help you learn and to build a community-driven course that is always improving.</p>
+    <p>希望这些学习步骤能够帮助到你，我们也期待看到你在社区的贡献</p>
 </div>
 
-We can discuss the process in this [discussion thread](https://github.com/huggingface/smol-course/discussions/2#discussion-7602932).
+在 GitHub 代码仓库的 [discussion](https://github.com/huggingface/smol-course/discussions/2#discussion-7602932) 部分有我们的英文讨论区。
 
-## Course Outline
+## 课程列表
 
-This course provides a practical, hands-on approach to working with small language models, from initial training through to production deployment.
+本课程旨在提供实操性指导，借助 smol 系列小模型作为示例，涵盖从最初的训练到生产部署的大部分技能。
 
-| Module | Description | Status | Release Date |
+
+
+| 模块 | 简介 | 状态 | 发布日期  |
 |--------|-------------|---------|--------------|
 | [Instruction Tuning](./1_instruction_tuning) | Learn supervised fine-tuning, chat templating, and basic instruction following | ✅ Ready | Dec 3, 2024 |
 | [Preference Alignment](./2_preference_alignment) | Explore DPO and ORPO techniques for aligning models with human preferences | ✅ Ready  | Dec 6, 2024 |
@@ -37,53 +39,61 @@ This course provides a practical, hands-on approach to working with small langua
 | [Inference](./7_inference) | Infer with models efficiently | ✅ Ready | Jan 8, 2025 |
 | [Agents](./8_agents) | Build your own agentic AI | ✅ Ready | Jan 13, 2025 ||
 
-## Why Small Language Models?
+## 为什么要使用小语言模型？
+
+虽然超大参数量的语言模型性能出众，但它们需要的计算资源过大，不利于实际生产环境的部署应用。相比而言，小模型则在针对特定场景的应用层面有着不少优势：
 
-While large language models have shown impressive capabilities, they often require significant computational resources and can be overkill for focused applications. Small language models offer several advantages for domain-specific applications:
+- **高效性**：训练和部署需要的计算资源少
+- **定制化**：方便微调，便于针对特定领域进行改进
+- **可控性**：有利于理解和控制模型的行为
+- **低成本**：训练和推理成本低
+- **隐私性**：可以本地运行，无需借助 API 传输个人数据
+- **更环保**：倡导充分利用计算资源、减少碳排放
+- **更易于科研发展**：不受人力物力层面的限制，为 LLM 的研究提供了一个更好出发点
 
-- **Efficiency**: Require significantly less computational resources to train and deploy
-- **Customization**: Easier to fine-tune and adapt to specific domains
-- **Control**: Better understanding and control of model behavior
-- **Cost**: Lower operational costs for training and inference
-- **Privacy**: Can be run locally without sending data to external APIs
-- **Green Technology**: Advocates efficient usage of resources with reduced carbon footprint
-- **Easier Academic Research Development**: Provides an easy starter for academic research with cutting-edge LLMs with less logistical constraints
+## 准备工作
 
-## Prerequisites
+开始课程前，请确保以下几点：
 
-Before starting, ensure you have the following:
-- Basic understanding of machine learning and natural language processing.
-- Familiarity with Python, PyTorch, and the `transformers` library.
-- Access to a pre-trained language model and a labeled dataset.
+- 你不是机器学习和自然语言处理领域的小白
+- 你能够使用 Python、PyTorch 以及 `transformers` 代码库
+- 你能够获得到一个预训练过的语言模型，以及有标注的数据集
 
-## Installation
+## 安装
 
-We maintain the course as a package so you can install dependencies easily via a package manager. We recommend [uv](https://github.com/astral-sh/uv) for this purpose, but you could use alternatives like `pip` or `pdm`.
+你可以通过一个软件包管理器安装必要的库。我们建议使用 [uv](https://github.com/astral-sh/uv)，当然 `pip` 或 `pdm` 也是可以的。
 
-### Using `uv`
+### 使用 `uv`
 
-With `uv` installed, you can install the course like this:
+目前所有教程内示例已经在 python 3.11 下跑通。如果你已经安装了 `uv`，你可以这样：
 
 ```bash
 uv venv --python 3.11.0
 uv sync
 ```
 
-### Using `pip`
+### 使用 `pip`
 
-All the examples run in the same **python 3.11** environment, so you should create an environment and install dependencies like this:
+先创建一个 **python 3.11** 的环境，然后这样安装：
 
 ```bash
 # python -m venv .venv
 # source .venv/bin/activate
 pip install -r requirements.txt
 ```
+所需要的偏底层的库也都列入了 `requirements.txt` 里，将会被自动安装。
 
 ### Google Colab
 
-**From Google Colab** you will need to install dependencies flexibly based on the hardware you're using. Like this:
+如果使用 **Google Colab**，其底层库需要根据机器硬件自行决定，所以直接这样就行:
 
 ```bash
-pip install transformers trl datasets huggingface_hub
+pip install -r transformers trl datasets huggingface_hub
 ```
 
+## 再次表示欢迎
+
+期待大家参与！让更多人在无需昂贵计算资源的情况下学习微调大语言模型。
+
+
+[![Star History Chart](https://api.star-history.com/svg?repos=huggingface/smol-course&type=Date)](https://star-history.com/#huggingface/smol-course&Date)
diff --git a/es/1_instruction_tuning/README.md b/es/1_instruction_tuning/README.md
deleted file mode 100644
index b1ee0a52..00000000
--- a/es/1_instruction_tuning/README.md
+++ /dev/null
@@ -1,30 +0,0 @@
-# Ajuste por Instrucciones (Instruction Tuning)
-
-Este módulo te guiará en el proceso de ajuste por instrucciones de modelos de lenguaje. El ajuste por instrucciones implica adaptar modelos preentrenados a tareas específicas mediante un entrenamiento adicional sobre conjuntos de datos específicos para esas tareas. Este proceso ayuda a los modelos a mejorar su rendimiento en tareas específicas.
-
-En este módulo, exploraremos dos temas: 1) Plantillas de Chat y 2) Fine-tuning Supervisado
-
-## 1️⃣ Plantillas de Chat
-
-Las plantillas de chat estructuran las interacciones entre los usuarios y los modelos de IA, asegurando respuestas coherentes y contextualmente apropiadas. Estas plantillas incluyen componentes como los mensajes de sistema y mensajes basados en roles. Para más información detallada, consulta la sección de [Plantillas de Chat](./chat_templates.md).
-
-## 2️⃣ Fine-tuning Supervisado
-
-El Fine-tuning supervisado (SFT) es un proceso crítico para adaptar modelos de lenguaje preentrenados a tareas específicas. Implica entrenar el modelo en un conjunto de datos específico para la tarea con ejemplos etiquetados. Para una guía detallada sobre el SFT, incluyendo pasos clave y mejores prácticas, consulta la página de [Fine-tuning Supervisado](./supervised_fine_tuning.md).
-
-## Cuadernos de Ejercicio
-
-| Título | Descripción | Ejercicio | Enlace | Colab |
-|--------|-------------|-----------|--------|-------|
-| Plantillas de Chat | Aprende a usar plantillas de chat con SmolLM2 y a procesar conjuntos de datos en formato chatml | 🐢 Convierte el conjunto de datos `HuggingFaceTB/smoltalk` al formato chatml <br> 🐕 Convierte el conjunto de datos `openai/gsm8k` al formato chatml | [Cuaderno](./notebooks/chat_templates_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/1_instruction_tuning/notebooks/chat_templates_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Abrir en Colab"/></a> |
-| Fine-tuning Supervisado | Aprende a afinar SmolLM2 utilizando el SFTTrainer | 🐢 Usa el conjunto de datos `HuggingFaceTB/smoltalk` <br> 🐕 Prueba el conjunto de datos `bigcode/the-stack-smol` <br> 🦁 Selecciona un conjunto de datos para un caso de uso real | [Cuaderno](./notebooks/sft_finetuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Abrir en Colab"/></a> |
-
-## Referencias
-
-- [Documentación de Transformers sobre plantillas de chat](https://huggingface.co/docs/transformers/main/en/chat_templating)
-- [Script para Fine-Tuning Supervisado en TRL](https://github.com/huggingface/trl/blob/main/examples/scripts/sft.py)
-- [`SFTTrainer` en TRL](https://huggingface.co/docs/trl/main/en/sft_trainer)
-- [Papel de Optimización Directa de Preferencias](https://arxiv.org/abs/2305.18290)
-- [Fine-Tuning Supervisado con TRL](https://huggingface.co/docs/trl/main/en/tutorials/supervised_finetuning)
-- [Cómo afinar Google Gemma con ChatML y Hugging Face TRL](https://www.philschmid.de/fine-tune-google-gemma)
-- [Fine-Tuning de LLM para generar catálogos de productos persas en formato JSON](https://huggingface.co/learn/cookbook/en/fine_tuning_llm_to_generate_persian_product_catalogs_in_json_format)
diff --git a/es/1_instruction_tuning/chat_templates.md b/es/1_instruction_tuning/chat_templates.md
deleted file mode 100644
index e7a27dd3..00000000
--- a/es/1_instruction_tuning/chat_templates.md
+++ /dev/null
@@ -1,116 +0,0 @@
-# Plantillas de Chat
-
-Las plantillas de chat son esenciales para estructurar las interacciones entre modelos de lenguaje y usuarios. Proporcionan un formato consistente para las conversaciones, asegurando que los modelos comprendan el contexto y el rol de cada mensaje, manteniendo patrones de respuesta apropiados.
-
-## Modelos Base vs Modelos Instructivos
-
-Un modelo base se entrena con datos de texto crudo para predecir el siguiente token, mientras que un modelo instructivo se ajusta específicamente para seguir instrucciones y participar en conversaciones. Por ejemplo, `SmolLM2-135M` es un modelo base, mientras que `SmolLM2-135M-Instruct` es su variante ajustada a instrucciones.
-
-Para hacer que un modelo base se comporte como un modelo instructivo, necesitamos formatear nuestros prompts de manera consistente para que el modelo los entienda. Aquí es donde entran las plantillas de chat. ChatML es un formato de plantilla que estructura las conversaciones con indicadores de rol claros (sistema, usuario, asistente).
-
-Es importante notar que un modelo base podría ser ajustado con diferentes plantillas de chat, por lo que, al usar un modelo instructivo, debemos asegurarnos de estar utilizando la plantilla de chat correcta.
-
-## Comprendiendo las Plantillas de Chat
-
-En su núcleo, las plantillas de chat definen cómo se deben formatear las conversaciones al comunicarse con un modelo de lenguaje. Incluyen instrucciones a nivel de sistema, mensajes del usuario y respuestas del asistente en un formato estructurado que el modelo puede entender. Esta estructura ayuda a mantener la coherencia en las interacciones y asegura que el modelo responda adecuadamente a diferentes tipos de entradas. A continuación, se muestra un ejemplo de una plantilla de chat:
-
-```sh
-<|im_start|>user
-¡Hola!<|im_end|>
-<|im_start|>assistant
-¡Mucho gusto!<|im_end|>
-<|im_start|>user
-¿Puedo hacer una pregunta?<|im_end|>
-<|im_start|>assistant
-``` 
-
-La librería `transformers` se encarga de las plantillas de chat por ti en relación con el tokenizador del modelo. Lee más sobre cómo `transformers` construye las plantillas de chat [aquí](https://huggingface.co/docs/transformers/en/chat_templating#how-do-i-use-chat-templates). Todo lo que tenemos que hacer es estructurar nuestros mensajes de la forma correcta y el tokenizador se encargará del resto. Aquí tienes un ejemplo básico de una conversación:
-
-```python
-messages = [
-    {"role": "system", "content": "Eres un asistente útil centrado en temas técnicos."},
-    {"role": "user", "content": "¿Puedes explicar qué es una plantilla de chat?"},
-    {"role": "assistant", "content": "Una plantilla de chat estructura las conversaciones entre los usuarios y los modelos de IA..."}
-]
-```
-
-Vamos a desglosar el ejemplo anterior y ver cómo se mapea al formato de plantilla de chat.
-
-## Mensajes del Sistema
-
-Los mensajes del sistema establecen la base para el comportamiento del modelo. Actúan como instrucciones persistentes que influyen en todas las interacciones posteriores. Por ejemplo:
-
-```python
-system_message = {
-    "role": "system",
-    "content": "Eres un agente de atención al cliente profesional. Siempre sé educado, claro y útil."
-}
-```
-
-## Conversaciones
-
-Las plantillas de chat mantienen el contexto a través del historial de la conversación, almacenando intercambios previos entre el usuario y el asistente. Esto permite conversaciones más coherentes en múltiples turnos:
-
-```python
-conversation = [
-    {"role": "user", "content": "Necesito ayuda con mi pedido"},
-    {"role": "assistant", "content": "Estaré encantado de ayudarte. ¿Podrías proporcionarme tu número de pedido?"},
-    {"role": "user", "content": "Es el PEDIDO-123"},
-]
-```
-
-## Implementación con Transformers
-
-La librería `transformers` proporciona soporte integrado para plantillas de chat. Aquí te mostramos cómo usarlas:
-
-```python
-from transformers import AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-135M-Instruct")
-
-messages = [
-    {"role": "system", "content": "Eres un asistente útil para programación."},
-    {"role": "user", "content": "Escribe una función en Python para ordenar una lista"},
-]
-
-# Aplica la plantilla de chat
-formatted_chat = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-```
-
-## Formato Personalizado
-
-Puedes personalizar cómo se formatean los diferentes tipos de mensajes. Por ejemplo, añadiendo tokens especiales o formato para diferentes roles:
-
-```python
-template = """
-<|system|>{system_message}
-<|user|>{user_message}
-<|assistant|>{assistant_message}
-""".lstrip()
-```
-
-## Soporte para Conversaciones de Varios Turnos
-
-Las plantillas pueden manejar conversaciones complejas de varios turnos mientras mantienen el contexto:
-
-```python
-messages = [
-    {"role": "system", "content": "Eres un tutor de matemáticas."},
-    {"role": "user", "content": "¿Qué es el cálculo?"},
-    {"role": "assistant", "content": "El cálculo es una rama de las matemáticas..."},
-    {"role": "user", "content": "¿Puedes darme un ejemplo?"},
-]
-```
-
-⏭️ [Siguiente: Fine-tuning Supervisado](./supervised_fine_tuning.md)
-
-## Recursos
-
-- [Guía de Plantillas de Chat de Hugging Face](https://huggingface.co/docs/transformers/main/en/chat_templating)
-- [Documentación de Transformers](https://huggingface.co/docs/transformers)
-- [Repositorio de Ejemplos de Plantillas de Chat](https://github.com/chujiezheng/chat_templates)
-```
\ No newline at end of file
diff --git a/es/1_instruction_tuning/notebooks/.env.example b/es/1_instruction_tuning/notebooks/.env.example
deleted file mode 100644
index ccef0469..00000000
--- a/es/1_instruction_tuning/notebooks/.env.example
+++ /dev/null
@@ -1,2 +0,0 @@
-# Duplica este archivo con el nombre .env y rellena con tus claves secretas.
-HF_TOKEN=
diff --git a/es/1_instruction_tuning/notebooks/chat_templates_example.ipynb b/es/1_instruction_tuning/notebooks/chat_templates_example.ipynb
deleted file mode 100644
index 1259e092..00000000
--- a/es/1_instruction_tuning/notebooks/chat_templates_example.ipynb
+++ /dev/null
@@ -1,5732 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "vZAvFVIAtFlq"
-   },
-   "source": [
-    "# Explorando Plantillas de Chat con SmolLM2\n",
-    "\n",
-    "Este notebook muestra cómo usar plantillas de chat con el modelo SmolLM2. Las plantillas de chat ayudan a estructurar las interacciones entre los usuarios y los modelos de IA, asegurando respuestas consistentes y contextualmente apropiadas."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "K-lZu8JvtwUN",
-    "outputId": "c3871418-15bc-4265-ae8d-6d6036036d0e"
-   },
-   "outputs": [],
-   "source": [
-    "# Instala los requisitos en Google Colab.\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "# Autenticación en Hugging Face\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()\n",
-    "\n",
-    "# Para mayor comodidad, puedes crear una variable de entorno con tu token de Hugging Face como HF_TOKEN en tu archivo .env\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "id": "tnHzBR7vtFlr"
-   },
-   "outputs": [],
-   "source": [
-    "# Importa las librerias necesarias.\n",
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "from trl import setup_chat_format\n",
-    "import torch"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "XTVOqbuetFlr"
-   },
-   "source": [
-    "## Plantilla de Chat de SmolLM2\n",
-    "\n",
-    "Exploremos cómo usar una plantilla de chat con el modelo SmolLM2. Definiremos una conversación simple y aplicaremos la plantilla de chat."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 397,
-     "referenced_widgets": [
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-     ]
-    },
-    "id": "Nrxh0oX6tFls",
-    "outputId": "953e1527-8168-4346-9338-6e188ca31a1a"
-   },
-   "outputs": [],
-   "source": [
-    "# Establece dinámicamente el dispositivo de procesamiento.\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=model_name\n",
-    ").to(device)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
-    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "id": "zkJwILrbtFls"
-   },
-   "outputs": [],
-   "source": [
-    "# Define los mensajes para SmolLM2\n",
-    "messages = [\n",
-    "    {   \n",
-    "        \"role\": \"user\", \n",
-    "        \"content\": \"Hello, how are you?\"},\n",
-    "    {\n",
-    "        \"role\": \"assistant\",\n",
-    "        \"content\": \"I'm doing well, thank you! How can I assist you today?\",\n",
-    "    },\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Ve4dgtjstFls"
-   },
-   "source": [
-    "# Aplica la plantilla de chat sin tokenización\n",
-    "\n",
-    "El tokenizador representa la conversación como una cadena de texto con tokens especiales para describir el rol del usuario y el asistente."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "pbAg-5x-tFls",
-    "outputId": "5f9482db-1fcf-4c13-ccaa-ef3f6eff7f76"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Conversation with template: <|im_start|>user\n",
-      "Hello, how are you?<|im_end|>\n",
-      "<|im_start|>assistant\n",
-      "I'm doing well, thank you! How can I assist you today?<|im_end|>\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "input_text = tokenizer.apply_chat_template(messages, tokenize=False)\n",
-    "\n",
-    "print(\"Conversation with template:\", input_text)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "sfvdglOqtFls"
-   },
-   "source": [
-    "# Decodifica la conversación\n",
-    "\n",
-    "Ten en cuenta que la conversación se representa como se mostró anteriormente, pero con un mensaje adicional del asistente."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "mXUVdPeytFls",
-    "outputId": "80870e53-7bc1-426e-ac33-ba6748e030fc"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Conversation decoded: <|im_start|>user\n",
-      "Hello, how are you?<|im_end|>\n",
-      "<|im_start|>assistant\n",
-      "I'm doing well, thank you! How can I assist you today?<|im_end|>\n",
-      "<|im_start|>assistant\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "input_text = tokenizer.apply_chat_template(\n",
-    "    messages, tokenize=True, add_generation_prompt=True\n",
-    ")\n",
-    "\n",
-    "print(\"Conversation decoded:\", tokenizer.decode(token_ids=input_text))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "UcZQpspEtFlt"
-   },
-   "source": [
-    "# Tokeniza la conversación\n",
-    "\n",
-    "Por supuesto, el tokenizador también convierte la conversación y los tokens especiales en identificadores que corresponden al vocabulario del modelo."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "jc2PLxAMtFlt",
-    "outputId": "d2098780-b3f4-41ec-a1f3-b6da2b593c62"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Conversation tokenized: [1, 4093, 198, 19556, 28, 638, 359, 346, 47, 2, 198, 1, 520, 9531, 198, 57, 5248, 2567, 876, 28, 9984, 346, 17, 1073, 416, 339, 4237, 346, 1834, 47, 2, 198, 1, 520, 9531, 198]\n"
-     ]
-    }
-   ],
-   "source": [
-    "input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)\n",
-    "\n",
-    "print(\"Conversación tokenizada:\", input_text)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "m3eNp9a0tFlt"
-   },
-   "source": [
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Ejercicio: Procesar un conjunto de datos para SFT (Fine-Tuning Supervisado)</h2>\n",
-    "    <p>Toma un conjunto de datos desde el repositorio de Hugging Face y procésalo para SFT.</p>\n",
-    "    <p><b>Niveles de dificultad</b></p>\n",
-    "    <p>🐢 Convierte el conjunto de datos 'HuggingFaceTB/smoltalk' al formato chatml.</p>\n",
-    "    <p>🐕 Convierte el conjunto de datos 'openai/gsm8k' al formato chatml.</p>\n",
-    "</div>\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 381
-    },
-    "id": "qbkXV2_ItFlt",
-    "outputId": "06deadc3-2c63-4660-d2bd-05096ef07c9f"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<iframe\n",
-       "  src=\"https://huggingface.co/datasets/HuggingFaceTB/smoltalk/embed/viewer/all/train?row=0\"\n",
-       "  frameborder=\"0\"\n",
-       "  width=\"100%\"\n",
-       "  height=\"360px\"\n",
-       "></iframe>\n"
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-      ]
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-     "metadata": {},
-     "output_type": "display_data"
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-   ],
-   "source": [
-    "from IPython.core.display import display, HTML\n",
-    "\n",
-    "display(\n",
-    "    HTML(\n",
-    "        \"\"\"<iframe\n",
-    "  src=\"https://huggingface.co/datasets/HuggingFaceTB/smoltalk/embed/viewer/all/train?row=0\"\n",
-    "  frameborder=\"0\"\n",
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-    "\"\"\"\n",
-    "    )\n",
-    ")"
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-    },
-    "id": "4p3atw4_tFlu",
-    "outputId": "62ee9812-3819-4a9c-9e24-5687368ffcd8"
-   },
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "ds = load_dataset(\"HuggingFaceTB/smoltalk\", \"everyday-conversations\")\n",
-    "\n",
-    "\n",
-    "def process_dataset(sample):\n",
-    "    # TODO: 🐢 Convertir la muestra al formato de chat\n",
-    "    # usa el método del tokenizador para aplicar la plantilla de chat\n",
-    "    return sample\n",
-    "\n",
-    "\n",
-    "ds = ds.map(process_dataset)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
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-    },
-    "id": "81fQeazltFlu",
-    "outputId": "36cf7148-9881-4f13-d0ce-76c82c4ab219"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<iframe\n",
-       "  src=\"https://huggingface.co/datasets/openai/gsm8k/embed/viewer/main/train\"\n",
-       "  frameborder=\"0\"\n",
-       "  width=\"100%\"\n",
-       "  height=\"360px\"\n",
-       "></iframe>\n"
-      ],
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-     "metadata": {},
-     "output_type": "display_data"
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-   "source": [
-    "display(\n",
-    "    HTML(\n",
-    "        \"\"\"<iframe\n",
-    "  src=\"https://huggingface.co/datasets/openai/gsm8k/embed/viewer/main/train\"\n",
-    "  frameborder=\"0\"\n",
-    "  width=\"100%\"\n",
-    "  height=\"360px\"\n",
-    "></iframe>\n",
-    "\"\"\"\n",
-    "    )\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true,
-    "id": "bWUSv7NMtFlu"
-   },
-   "outputs": [],
-   "source": [
-    "ds = load_dataset(\"openai/gsm8k\", \"main\")\n",
-    "\n",
-    "\n",
-    "def process_dataset(sample):\n",
-    "    # TODO: 🐕 Convertir la muestra al formato de chat\n",
-    "\n",
-    "    # 1. Crear un formato de mensaje con el rol y contenido\n",
-    "\n",
-    "    # 2. Aplicar la plantilla de chat a las muestras usando el método del tokenizador\n",
-    "\n",
-    "    return sample\n",
-    "\n",
-    "\n",
-    "ds = ds.map(process_dataset)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "qlXCuRKotFlu"
-   },
-   "source": [
-    "## Conclusión\n",
-    "\n",
-    "Este notebook demostró cómo aplicar plantillas de chat a diferentes modelos, como `SmolLM2`. Al estructurar las interacciones con plantillas de chat, podemos asegurar que los modelos de IA ofrezcan respuestas consistentes y contextualmente relevantes.\n",
-    "\n",
-    "En el ejercicio, intentaste convertir un conjunto de datos al formato chatml. Afortunadamente, TRL hará esto por ti, pero es útil entender lo que ocurre \"bajo el capó\".\n"
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diff --git a/es/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb b/es/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb
deleted file mode 100644
index 43ee7ac8..00000000
--- a/es/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb
+++ /dev/null
@@ -1,280 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Fine-Tuning Supervisado con SFTTrainer\n",
-    "\n",
-    "Este notebook demuestra cómo realizar fine-tuning del modelo `HuggingFaceTB/SmolLM2-135M` usando el `SFTTrainer` de la librería `trl`. Las celdas del notebook se ejecutan y harán el fine-tuning del modelo. Puedes seleccionar tu nivel de dificultad probando diferentes conjuntos de datos.\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Ejercicio: Fine-Tuning de SmolLM2 con SFTTrainer</h2>\n",
-    "    <p>Toma un conjunto de datos desde el repositorio de Hugging Face y realiza un fine-tuning de un modelo con él.</p>\n",
-    "    <p><b>Niveles de dificultad</b></p>\n",
-    "    <p>🐢 Usa el conjunto de datos `HuggingFaceTB/smoltalk`</p>\n",
-    "    <p>🐕 Prueba el conjunto de datos `bigcode/the-stack-smol` y realiza un fine-tuning de un modelo de generación de código en un subconjunto específico `data/python`.</p>\n",
-    "    <p>🦁 Selecciona un conjunto de datos que esté relacionado con un caso de uso del mundo real que te interese.</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instalar los requisitos en Google Colab\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "\n",
-    "# Autenticación en Hugging Face\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()\n",
-    "\n",
-    "# Para mayor comodidad, puedes crear una variable de entorno con tu token de Hugging Face como HF_TOKEN en tu archivo .env"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Importa las bibliotecas necesarias\n",
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "from datasets import load_dataset\n",
-    "from trl import SFTConfig, SFTTrainer, setup_chat_format\n",
-    "import torch\n",
-    "\n",
-    "# Establece dinámicamente el dispositivo de procesamiento.\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "# Carga el modelo y el tokenizador\n",
-    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=model_name\n",
-    ").to(device)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
-    "\n",
-    "# Configura el formato de chat\n",
-    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n",
-    "\n",
-    "# Establece nuestro nombre para guardar y/o subir el modelo finetuneado\n",
-    "finetune_name = \"SmolLM2-FT-MyDataset\"\n",
-    "finetune_tags = [\"smol-course\", \"module_1\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Generar con el modelo base\n",
-    "\n",
-    "Aquí probaremos el modelo base, que no tiene una plantilla de chat."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Probemos el modelo base antes de entrenar\n",
-    "prompt = \"Write a haiku about programming\"\n",
-    "\n",
-    "# Formatea con la plantilla\n",
-    "messages = [{\"role\": \"user\", \"content\": prompt}]\n",
-    "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n",
-    "\n",
-    "# Genera la respuesta\n",
-    "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
-    "outputs = model.generate(**inputs, max_new_tokens=100)\n",
-    "print(\"Before training:\")\n",
-    "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Preparación del Conjunto de Datos\n",
-    "\n",
-    "Cargaremos un conjunto de datos de ejemplo y lo formatearemos para el entrenamiento. El conjunto de datos debe estar estructurado con pares de entrada-salida, donde cada entrada es un prompt y la salida es la respuesta esperada del modelo.\n",
-    "\n",
-    "**TRL formateará los mensajes de entrada según las plantillas de chat del modelo.** Estos deben representarse como una lista de diccionarios con las claves: `role` y `content`.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Carga un conjunto de datos de ejemplo\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "# TODO: define tu conjunto de datos y configuración usando los parámetros de ruta y nombre\n",
-    "ds = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO: 🦁 Si tu conjunto de datos no está en un formato que TRL pueda convertir a la plantilla de chat, necesitarás procesarlo. Consulta el [módulo](../chat_templates.md)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Configuración del SFTTrainer\n",
-    "\n",
-    "El `SFTTrainer` se configura con varios parámetros que controlan el proceso de entrenamiento. Estos incluyen el número de pasos de entrenamiento, el tamaño del lote, la tasa de aprendizaje y la estrategia de evaluación. Ajusta estos parámetros según tus requisitos específicos y recursos computacionales."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configura el SFTTrainer\n",
-    "sft_config = SFTConfig(\n",
-    "    output_dir=\"./sft_output\",  # Directorio de salida para los resultados del entrenamiento\n",
-    "    max_steps=1000,  # Ajusta según el tamaño del conjunto de datos y la duración de entrenamiento deseada\n",
-    "    per_device_train_batch_size=4,  # Ajusta según la capacidad de la memoria de tu GPU\n",
-    "    learning_rate=5e-5,  # Punto de partida común para el fine-tuning\n",
-    "    logging_steps=10,  # Frecuencia de registro de métricas de entrenamiento\n",
-    "    save_steps=100,  # Frecuencia de guardado de puntos de control del modelo\n",
-    "    evaluation_strategy=\"steps\",  # Evalua el modelo en intervalos regulares\n",
-    "    eval_steps=50,  # Frecuencia de evaluación\n",
-    "    use_mps_device=(\n",
-    "        True if device == \"mps\" else False\n",
-    "    ),  # Usa MPS para entrenamiento con precisión mixta\n",
-    "    hub_model_id=finetune_name,  # Asigna un nombre único a tu modelo\n",
-    ")\n",
-    "\n",
-    "# Inicializa el SFTTrainer\n",
-    "trainer = SFTTrainer(\n",
-    "    model=model,\n",
-    "    args=sft_config,\n",
-    "    train_dataset=ds[\"train\"],\n",
-    "    tokenizer=tokenizer,\n",
-    "    eval_dataset=ds[\"test\"],\n",
-    ")\n",
-    "\n",
-    "# TODO: 🦁 🐕 ajusta los parámetros del SFTTrainer a tu conjunto de datos elegido. Por ejemplo, si usas el conjunto de datos `bigcode/the-stack-smol`, necesitarás elegir la columna `content`.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Entrenamiento del Modelo\n",
-    "\n",
-    "Con el entrenador configurado, ahora podemos proceder a entrenar el modelo. El proceso de entrenamiento implicará iterar sobre el conjunto de datos, calcular la pérdida y actualizar los parámetros del modelo para minimizar esta pérdida."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Entrena el modelo\n",
-    "trainer.train()\n",
-    "\n",
-    "# Guarda el modelo\n",
-    "trainer.save_model(f\"./{finetune_name}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.push_to_hub(tags=finetune_tags)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Ejercicio adicional: Generar con el modelo ajustado</h2>\n",
-    "    <p>🐕 Utiliza el modelo ajustado para generar una respuesta, de la misma forma que con el ejemplo base.</p>\n",
-    "</div>\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Prueba el modelo ajustado con el mismo mensaje\n",
-    "\n",
-    "# Probemos el modelo base antes del entrenamiento\n",
-    "prompt = \"Escribe un haiku sobre programación\"\n",
-    "\n",
-    "# Formatea con la plantilla\n",
-    "messages = [{\"role\": \"user\", \"content\": prompt}]\n",
-    "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n",
-    "\n",
-    "# Genera respuesta\n",
-    "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
-    "\n",
-    "# TODO: usa el modelo ajustado para generar una respuesta, igual que en el ejemplo base."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 💐 ¡Has terminado!\n",
-    "\n",
-    "Este notebook proporcionó una guía paso a paso para realizar fine-tuning del modelo `HuggingFaceTB/SmolLM2-135M` utilizando el `SFTTrainer`. Siguiendo estos pasos, puedes adaptar el modelo para realizar tareas específicas de manera más efectiva. Si deseas continuar trabajando en este curso, aquí tienes algunas sugerencias que podrías intentar:\n",
-    "\n",
-    "- Intenta este notebook en un nivel de dificultad más alto.\n",
-    "- Revisa el PR de un compañero.\n",
-    "- Mejora el material del curso a través de un Issue o PR.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py310",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.15"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/es/1_instruction_tuning/supervised_fine_tuning.md b/es/1_instruction_tuning/supervised_fine_tuning.md
deleted file mode 100644
index cc2092d0..00000000
--- a/es/1_instruction_tuning/supervised_fine_tuning.md
+++ /dev/null
@@ -1,41 +0,0 @@
-# Fine-Tuning Supervisado
-
-El Fine-Tuning Supervisado (SFT) es un proceso crítico para adaptar modelos de lenguaje preentrenados a tareas o dominios específicos. Aunque los modelos preentrenados tienen capacidades generales impresionantes, a menudo necesitan ser personalizados para sobresalir en casos de uso particulares. El SFT cubre esta brecha entrenando el modelo con conjuntos de datos cuidadosamente seleccionados que contienen ejemplos validados por humanos.
-
-## Comprendiendo el Fine-Tuning Supervisado
-
-En su núcleo, el Fine-Tuning supervisado se trata de enseñar a un modelo preentrenado a realizar tareas específicas mediante ejemplos de tokens etiquetados. El proceso consiste en mostrarle al modelo muchos ejemplos del comportamiento deseado de entrada-salida, permitiéndole aprender los patrones específicos de tu caso de uso.
-
-El SFT es eficaz porque utiliza el conocimiento fundamental adquirido durante el preentrenamiento, mientras adapta el comportamiento del modelo para que se ajuste a tus necesidades específicas.
-
-## Cuándo Utilizar Fine-Tuning Supervisado
-
-La decisión de usar SFT generalmente depende de la brecha entre las capacidades actuales de tu modelo y tus requisitos específicos. El SFT se vuelve particularmente valioso cuando necesitas un control preciso sobre las salidas del modelo o cuando trabajas en dominios especializados.
-
-Por ejemplo, si estás desarrollando una aplicación de atención al cliente, es posible que desees que tu modelo siga consistentemente las pautas de la empresa y maneje consultas técnicas de una manera estandarizada. De manera similar, en aplicaciones médicas o legales, la precisión y la adherencia a la terminología específica del dominio se vuelven cruciales. En estos casos, el SFT puede ayudar a alinear las respuestas del modelo con los estándares profesionales y la experiencia del dominio.
-
-## El Proceso de Fine-Tuning Supervisado (SFT)
-
-El proceso de Fine-Tuning Supervisado consiste en entrenar los pesos del modelo en un conjunto de datos específico para una tarea.
-
-Primero, necesitarás preparar o seleccionar un conjunto de datos que represente tu tarea objetivo. Este conjunto de datos debe incluir ejemplos diversos que cubran una amplia gama de escenarios que tu modelo encontrará. La calidad de estos datos es importante: cada ejemplo debe demostrar el tipo de salida que deseas que el modelo produzca. Luego, viene la fase de fine-tuning, donde usarás marcos como `transformers` y `trl` de Hugging Face para entrenar el modelo con tu conjunto de datos.
-
-Durante todo el proceso, la evaluación continua es esencial. Querrás monitorear el rendimiento del modelo en un conjunto de validación para asegurarte de que está aprendiendo los comportamientos deseados sin perder sus capacidades generales. En el [módulo 4](../4_evaluation), cubriremos cómo evaluar tu modelo.
-
-## El Rol del SFT en la Alineación con las Preferencias
-
-El SFT juega un papel fundamental en la alineación de los modelos de lenguaje con las preferencias humanas. Técnicas como el Aprendizaje por Refuerzo a partir de Retroalimentación Humana (RLHF) y la Optimización Directa de Preferencias (DPO) dependen del SFT para formar un nivel base de comprensión de la tarea antes de alinear aún más las respuestas del modelo con los resultados deseados. Los modelos preentrenados, a pesar de su competencia general en el lenguaje, no siempre generan salidas que coinciden con las preferencias humanas. El SFT cubre esta brecha al introducir datos específicos de dominio y orientación, lo que mejora la capacidad del modelo para generar respuestas que se alineen más estrechamente con las expectativas humanas.
-
-## Fine-Tuning Supervisado con Aprendizaje por Refuerzo de Transformers (TRL)
-
-Un paquete de software clave para el fine-tuning supervisado es el Aprendizaje por Refuerzo de Transformers (TRL). TRL es un conjunto de herramientas utilizado para entrenar modelos de lenguaje transformadores utilizando aprendizaje por refuerzo (RL).
-
-Basado en la librería Transformers de Hugging Face, TRL permite a los usuarios cargar directamente modelos de lenguaje preentrenados y es compatible con la mayoría de las arquitecturas de decodificadores y codificadores-decodificadores. La librería facilita los principales procesos del RL utilizado en la modelización del lenguaje, incluyendo el fine-tuning supervisado (SFT), la modelización de recompensas (RM), la optimización de políticas proximales (PPO) y la optimización directa de preferencias (DPO). Usaremos TRL en varios módulos a lo largo de este repositorio.
-
-# Próximos Pasos
-
-Prueba los siguientes tutoriales para obtener experiencia práctica con el SFT utilizando TRL:
-
-⏭️ [Tutorial de Plantillas de Chat](./notebooks/chat_templates_example.ipynb)
-
-⏭️ [Tutorial de Fine-Tuning Supervisado](./notebooks/supervised_fine_tuning_tutorial.ipynb)
diff --git a/es/2_preference_alignment/README.md b/es/2_preference_alignment/README.md
deleted file mode 100644
index 92384737..00000000
--- a/es/2_preference_alignment/README.md
+++ /dev/null
@@ -1,39 +0,0 @@
-# Alineación de Preferencias
-
-Este módulo cubre técnicas para alinear modelos de lenguaje con las preferencias humanas. Mientras que la afinación supervisada (SFT) ayuda a los modelos a aprender tareas, la alineación de preferencias fomenta que las salidas coincidan con las expectativas y valores humanos.
-
-## Descripción General
-
-Los métodos típicos de alineación incluyen múltiples etapas:
-1. Afinación Supervisada (SFT) para adaptar los modelos a dominios específicos.
-2. Alineación de preferencias (como RLHF o DPO) para mejorar la calidad de las respuestas.
-
-Enfoques alternativos como ORPO combinan la afinación por instrucciones y la alineación de preferencias en un solo proceso. Aquí, nos enfocaremos en los algoritmos DPO y ORPO.
-
-Si deseas aprender más sobre las diferentes técnicas de alineación, puedes leer más sobre ellas en el [Blog de Argilla](https://argilla.io/blog/mantisnlp-rlhf-part-8).
-
-### 1️⃣ Optimización Directa de Preferencias (DPO)
-
-La Optimización Directa de Preferencias (DPO) simplifica la alineación de preferencias optimizando directamente los modelos utilizando datos de preferencias. Este enfoque elimina la necesidad de modelos de recompensa separados y de un aprendizaje por refuerzo complejo, lo que lo hace más estable y eficiente que el Aprendizaje por Refuerzo de Retroalimentación Humana (RLHF) tradicional. Para más detalles, puedes consultar la [documentación de Optimización Directa de Preferencias (DPO)](./dpo.md).
-
-### 2️⃣ Optimización de Preferencias por Ratio de Probabilidades (ORPO)
-
-ORPO introduce un enfoque combinado para la afinación por instrucciones y la alineación de preferencias en un solo proceso. Modifica el objetivo estándar del modelado de lenguaje combinando la pérdida de verosimilitud logarítmica negativa con un término de ratio de probabilidades a nivel de token. El enfoque presenta un proceso de entrenamiento de una sola etapa, una arquitectura libre de modelo de referencia y una mayor eficiencia computacional. ORPO ha mostrado resultados impresionantes en varios puntos de referencia, demostrando un mejor rendimiento en AlpacaEval en comparación con los métodos tradicionales. Para más detalles, puedes consultar la [documentación de Optimización de Preferencias por Ratio de Probabilidades (ORPO)](./orpo.md).
-
-## Notebooks de Ejercicios
-
-| Titulo | Descripción | Ejercicio | Enlace | Colab |
-|-------|-------------|----------|------|-------|
-| Entrenamiento DPO | Aprende a entrenar modelos utilizando la Optimización Directa de Preferencias | 🐢 Entrenar un modelo utilizando el conjunto de datos HH-RLHF de Anthropic<br>🐕 Utiliza tu propio conjunto de datos de preferencias<br>🦁 Experimenta con diferentes conjuntos de datos de preferencias y tamaños de modelo | [Notebook](./notebooks/dpo_finetuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Abrir en Colab"/></a> |
-| Entrenamiento ORPO | Aprende a entrenar modelos utilizando la Optimización de Preferencias por Ratio de Probabilidades | 🐢 Entrenar un modelo utilizando datos de instrucciones y preferencias<br>🐕 Experimenta con diferentes ponderaciones de la pérdida<br>🦁 Compara los resultados de ORPO con DPO | [Notebook](./notebooks/orpo_finetuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Abrir en Colab"/></a> |
-
-## Recursos
-
-- [Documentación de TRL](https://huggingface.co/docs/trl/index) - Documentación para la librería Transformers Reinforcement Learning (TRL), que implementa diversas técnicas de alineación, incluyendo DPO.
-- [Papel de DPO](https://arxiv.org/abs/2305.18290) - Artículo original de investigación que introduce la Optimización Directa de Preferencias como una alternativa más simple al RLHF que optimiza directamente los modelos de lenguaje utilizando datos de preferencias.
-- [Papel de ORPO](https://arxiv.org/abs/2403.07691) - Introduce la Optimización de Preferencias por Ratio de Probabilidades, un enfoque novedoso que combina la afinación por instrucciones y la alineación de preferencias en una sola etapa de entrenamiento.
-- [Guía de RLHF de Argilla](https://argilla.io/blog/mantisnlp-rlhf-part-8/) - Una guía que explica diferentes técnicas de alineación, incluyendo RLHF, DPO y sus implementaciones prácticas.
-- [Entrada en el Blog sobre DPO](https://huggingface.co/blog/dpo-trl) - Guía práctica sobre cómo implementar DPO utilizando la librería TRL con ejemplos de código y mejores prácticas.
-- [Script de ejemplo de TRL sobre DPO](https://github.com/huggingface/trl/blob/main/examples/scripts/dpo.py) - Script completo de ejemplo que demuestra cómo implementar el entrenamiento DPO utilizando la librería TRL.
-- [Script de ejemplo de TRL sobre ORPO](https://github.com/huggingface/trl/blob/main/examples/scripts/orpo.py) - Implementación de referencia del entrenamiento ORPO utilizando la librería TRL con opciones detalladas de configuración.
-- [Manual de Alineación de Hugging Face](https://github.com/huggingface/alignment-handbook) - Guías y código para alinear modelos de lenguaje utilizando diversas técnicas, incluyendo SFT, DPO y RLHF.
diff --git a/es/2_preference_alignment/dpo.md b/es/2_preference_alignment/dpo.md
deleted file mode 100644
index 93d795d8..00000000
--- a/es/2_preference_alignment/dpo.md
+++ /dev/null
@@ -1,73 +0,0 @@
-**Optimización Directa de Preferencias (DPO)**
-
-La Optimización Directa de Preferencias (DPO) ofrece un enfoque simplificado para alinear modelos de lenguaje con las preferencias humanas. A diferencia de los métodos tradicionales de RLHF (Reinforcement Learning with Human Feedback), que requieren modelos de recompensas separados y algoritmos complejos de aprendizaje por refuerzo, DPO optimiza directamente el modelo utilizando datos de preferencia.
-
-## Entendiendo DPO
-
-DPO redefine la alineación de preferencias como un problema de clasificación basado en datos de preferencias humanas. Los enfoques tradicionales de RLHF requieren entrenar un modelo de recompensa separado y usar algoritmos complejos como PPO (Proximal Policy Optimization) para alinear las salidas del modelo. DPO simplifica este proceso al definir una función de pérdida que optimiza directamente la política del modelo en función de las salidas preferidas versus las no preferidas.
-
-Este enfoque ha demostrado ser altamente efectivo en la práctica, utilizándose para entrenar modelos como Llama. Al eliminar la necesidad de un modelo de recompensa separado y una etapa de aprendizaje por refuerzo, DPO hace que la alineación de preferencias sea más accesible y estable.
-
-## Cómo Funciona DPO
-
-El proceso de DPO requiere un fine-tuning supervisado (SFT) para adaptar el modelo al dominio objetivo. Esto crea una base para el aprendizaje de preferencias mediante el entrenamiento en conjuntos de datos estándar de seguimiento de instrucciones. El modelo aprende a completar tareas básicas mientras mantiene sus capacidades generales.
-
-Luego viene el aprendizaje de preferencias, donde el modelo se entrena con pares de salidas: una preferida y una no preferida. Los pares de preferencias ayudan al modelo a entender qué respuestas se alinean mejor con los valores y expectativas humanas.
-
-La innovación central de DPO radica en su enfoque de optimización directa. En lugar de entrenar un modelo de recompensa separado, DPO utiliza una pérdida de entropía cruzada binaria para actualizar directamente los pesos del modelo en función de los datos de preferencia. Este proceso simplificado hace que el entrenamiento sea más estable y eficiente, logrando resultados comparables o mejores que los métodos tradicionales de RLHF.
-
-## Conjuntos de Datos para DPO
-
-Los conjuntos de datos para DPO se crean típicamente anotando pares de respuestas como preferidas o no preferidas. Esto se puede hacer manualmente o mediante técnicas de filtrado automatizado. A continuación, se muestra un ejemplo de la estructura de un conjunto de datos de preferencias para DPO en un turno único:
-
-| Prompt | Elegido | Rechazado |
-|--------|---------|-----------|
-| ...    | ...     | ...       |
-| ...    | ...     | ...       |
-| ...    | ...     | ...       |
-
-La columna `Prompt` contiene el prompt utilizado para generar las respuestas `Elegido` y `Rechazado`. Las columnas `Elegido` y `Rechazado` contienen las respuestas preferidas y no preferidas, respectivamente. Existen variaciones en esta estructura, por ejemplo, incluyendo una columna de `system_prompt` o `Input` con material de referencia. Los valores de `elegido` y `rechazado` pueden representarse como cadenas para conversaciones de un solo turno o como listas de conversación.
-
-Puedes encontrar una colección de conjuntos de datos DPO en Hugging Face [aquí](https://huggingface.co/collections/argilla/preference-datasets-for-dpo-656f0ce6a00ad2dc33069478).
-
-## Implementación con TRL
-
-La biblioteca Transformers Reinforcement Learning (TRL) facilita la implementación de DPO. Las clases `DPOConfig` y `DPOTrainer` siguen la misma API de estilo `transformers`.
-
-Aquí tienes un ejemplo básico de cómo configurar el entrenamiento de DPO:
-
-```python
-from trl import DPOConfig, DPOTrainer
-
-# Definir los argumentos
-training_args = DPOConfig(
-    ...
-)
-
-# Inicializar el entrenador
-trainer = DPOTrainer(
-    model,
-    train_dataset=dataset,
-    tokenizer=tokenizer,
-    ...
-)
-
-# Entrenar el modelo
-trainer.train()
-```
-
-En el [tutorial de DPO](./notebooks/dpo_finetuning_example.ipynb) se cubrirán más detalles sobre cómo utilizar las clases `DPOConfig` y `DPOTrainer`.
-
-## Mejores Prácticas
-
-La calidad de los datos es crucial para una implementación exitosa de DPO. El conjunto de datos de preferencias debe incluir ejemplos diversos que cubran diferentes aspectos del comportamiento deseado. Las pautas claras de anotación aseguran un etiquetado consistente de respuestas preferidas y no preferidas. Puedes mejorar el rendimiento del modelo mejorando la calidad de tu conjunto de datos de preferencias. Por ejemplo, filtrando conjuntos de datos más grandes para incluir solo ejemplos de alta calidad o que estén más relacionados con tu caso de uso.
-
-Durante el entrenamiento, monitorea cuidadosamente la convergencia de la pérdida y valida el rendimiento con datos retenidos. Es posible que sea necesario ajustar el parámetro beta para equilibrar el aprendizaje de preferencias con el mantenimiento de las capacidades generales del modelo. La evaluación regular sobre ejemplos diversos ayuda a garantizar que el modelo está aprendiendo las preferencias deseadas sin sobreajustarse.
-
-Compara las salidas del modelo con el modelo de referencia para verificar la mejora en la alineación de preferencias. Probar el modelo con una variedad de prompts, incluidos los casos extremos, ayuda a garantizar un aprendizaje robusto de preferencias en diferentes escenarios.
-
-## Próximos Pasos
-
-⏩ Para obtener experiencia práctica con DPO, prueba el [Tutorial de DPO](./notebooks/dpo_finetuning_example.ipynb). Esta guía práctica te guiará a través de la implementación de la alineación de preferencias con tu propio modelo, desde la preparación de datos hasta el entrenamiento y la evaluación.
-
-⏭️ Después de completar el tutorial, puedes explorar la página de [ORPO](./orpo.md) para aprender sobre otra técnica de alineación de preferencias.
\ No newline at end of file
diff --git a/es/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb b/es/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
deleted file mode 100644
index 8bab8b3c..00000000
--- a/es/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
+++ /dev/null
@@ -1,5501 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Alineación de Preferencias con Optimización de Preferencia Directa (Direct Preference Optimization (DPO))\n",
-    "\n",
-    "Este notebook te guiará a través del proceso de fine-tuning de un modelo de lenguaje utilizando Optimización de Preferencia Directa (Direct Preference Optimization (DPO)). Usaremos el modelo SmolLM2-135M-Instruct, que ya ha pasado por un entrenamiento SFT, por lo que es compatible con DPO. También puedes usar el modelo que entrenaste en [1_instruction_tuning](../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb).\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "     <h2 style='margin: 0;color:blue'>Ejercicio: Alineando SmolLM2 con DPOTrainer</h2>\n",
-    "     <p>Toma un dataset del Hub de Hugging Face y alinea un modelo sobre él.</p> \n",
-    "     <p><b>Niveles de dificultad</b></p>\n",
-    "     <p>🐢 Usa el dataset `trl-lib/ultrafeedback_binarized`</p>\n",
-    "     <p>🐕 Prueba el dataset `argilla/ultrafeedback-binarized-preferences`</p>\n",
-    "     <p>🦁 Selecciona un dataset relacionado con un caso de uso real que te interese, o usa el modelo que entrenaste en \n",
-    "        <a href=\"../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb\">1_instruction_tuning</a></p>\n",
-    "</div>\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instala los requisitos en Google Colab\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "# Autenticación en Hugging Face\n",
-    "\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()\n",
-    "\n",
-    "# Para conveniencia, puedes crear una variable de entorno que contenga tu token de Hugging Face como HF_TOKEN"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Importa las libraries\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "_zIBL8IssExG",
-    "outputId": "90d70bd2-30e7-49a4-d5bf-20d74deb6cec"
-   },
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "import os\n",
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "from datasets import load_dataset\n",
-    "from trl import DPOTrainer, DPOConfig"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "d8CvUgROUDw-"
-   },
-   "source": [
-    "## Formatea el conjunto de datos"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "MCD77GZ60DOT"
-   },
-   "outputs": [],
-   "source": [
-    "# Carga el conjunto de datos\n",
-    "\n",
-    "# TODO: 🦁🐕 cambia el conjunto de datos a uno de tu elección\n",
-    "dataset = load_dataset(path=\"trl-lib/ultrafeedback_binarized\", split=\"train\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO: 🐕 Si tu dataset no está representado como listas de conversaciones, puedes usar la función process_dataset para convertirlo."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Selecciona el modelo**\n",
-    "\n",
-    "Usaremos el modelo SmolLM2-135M-Instruct, que ya ha pasado por un entrenamiento SFT, por lo que es compatible con DPO. También puedes usar el modelo que entrenaste en [1_instruction_tuning](../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb).\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; width:80%; color:black'>\n",
-    "     <p>🦁 cambia la ruta del modelo o el ID del repositorio del modelo que entrenaste en <a href=\"../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb\">1_instruction_tuning</a></p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO: 🦁 cambia la ruta del modelo o el ID del repositorio del modelo que entrenaste en [1_instruction_tuning](../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb)\n",
-    "\n",
-    "model_name = \"HuggingFaceTB/SmolLM2-135M-Instruct\"\n",
-    "\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "# Modelo para afinar\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=model_name,\n",
-    "    torch_dtype=torch.float16,\n",
-    ").to(device)\n",
-    "model.config.use_cache = False\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
-    "tokenizer.pad_token = tokenizer.eos_token\n",
-    "\n",
-    "# Establece el nombre para el modelo afinado que se guardará y/o subirá\n",
-    "finetune_name = \"SmolLM2-FT-DPO\"\n",
-    "finetune_tags = [\"smol-course\", \"module_2\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "DeT5eUK_UJgK"
-   },
-   "source": [
-    "## Train model with DPO"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000,
-     "referenced_widgets": [
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-     ]
-    },
-    "id": "rKPILNOLR-aK",
-    "outputId": "1e9c1a6e-910d-40f9-e0bb-2a83a322d015"
-   },
-   "outputs": [],
-   "source": [
-    "# Argumentos de entrenamiento\n",
-    "training_args = DPOConfig(\n",
-    "    # Tamaño del lote de entrenamiento por GPU\n",
-    "    per_device_train_batch_size=4,\n",
-    "    # Número de pasos de actualización para acumular antes de realizar una pasada hacia atrás/actualización\n",
-    "    # Tamaño efectivo del lote = per_device_train_batch_size * gradient_accumulation_steps\n",
-    "    gradient_accumulation_steps=4,\n",
-    "    # Ahorra memoria no almacenando activaciones durante la pasada hacia adelante\n",
-    "    # En su lugar, las vuelve a calcular durante la pasada hacia atrás\n",
-    "    gradient_checkpointing=True,\n",
-    "    # Tasa base de aprendizaje para el entrenamiento\n",
-    "    learning_rate=5e-5,\n",
-    "    # Tipo de programación de la tasa de aprendizaje - 'coseno' disminuye gradualmente la LR siguiendo una curva coseno\n",
-    "    lr_scheduler_type=\"cosine\",\n",
-    "    # Número total de pasos de entrenamiento\n",
-    "    max_steps=200,\n",
-    "    # Desactiva el guardado de puntos de control del modelo durante el entrenamiento\n",
-    "    save_strategy=\"no\",\n",
-    "    # Con qué frecuencia registrar métricas de entrenamiento\n",
-    "    logging_steps=1,\n",
-    "    # Directorio para guardar las salidas del modelo\n",
-    "    output_dir=\"smol_dpo_output\",\n",
-    "    # Número de pasos para el calentamiento de la tasa de aprendizaje\n",
-    "    warmup_steps=100,\n",
-    "    # Usar precisión bfloat16 para un entrenamiento más rápido\n",
-    "    bf16=True,\n",
-    "    # Desactivar los registros de wandb/tensorboard\n",
-    "    report_to=\"none\",\n",
-    "    # Mantener todas las columnas en el dataset incluso si no se utilizan\n",
-    "    remove_unused_columns=False,\n",
-    "    # Habilitar MPS (Metal Performance Shaders) para dispositivos Mac\n",
-    "    use_mps_device=device == \"mps\",\n",
-    "    # ID del modelo para cargas al HuggingFace Hub\n",
-    "    hub_model_id=finetune_name,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer = DPOTrainer(\n",
-    "    # El modelo a ser entrenado\n",
-    "    model=model,\n",
-    "    # Configuración de entrenamiento de los argumentos anteriores\n",
-    "    args=training_args,\n",
-    "    # Dataset que contiene los pares de respuestas preferidas/rechazadas\n",
-    "    train_dataset=dataset,\n",
-    "    # Tokenizer para procesar las entradas\n",
-    "    processing_class=tokenizer,\n",
-    "    # Parámetro específico de DPO que controla la intensidad del modelo de preferencias\n",
-    "    # Valores más bajos (como 0.1) hacen que el modelo sea más conservador al seguir las preferencias\n",
-    "    beta=0.1,\n",
-    "    # Longitud máxima del prompt de entrada en tokens\n",
-    "    max_prompt_length=1024,\n",
-    "    # Longitud máxima combinada de prompt + respuesta en tokens\n",
-    "    max_length=1536,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Entrena el modelo\n",
-    "trainer.train()\n",
-    "\n",
-    "# Guarda el modelo\n",
-    "trainer.save_model(f\"./{finetune_name}\")\n",
-    "\n",
-    "# Sube el modelo a Hugging Face si el inicio de sesión o HF_TOKEN está configurado\n",
-    "if os.getenv(\"HF_TOKEN\"):\n",
-    "    trainer.push_to_hub(tags=finetune_tags)"
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-    "## 💐 ¡Has terminado!\n",
-    "\n",
-    "Este notebook proporcionó una guía paso a paso para realizar fine-tuning al modelo `HuggingFaceTB/SmolLM2-135M` utilizando el `DPOTrainer`. Siguiendo estos pasos, puedes adaptar el modelo para realizar tareas específicas de manera más eficaz. Si deseas continuar trabajando en este curso, aquí tienes algunos pasos que podrías intentar:\n",
-    "\n",
-    "- Prueba este notebook en una dificultad más alta\n",
-    "- Revisa el PR de un compañero\n",
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diff --git a/es/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb b/es/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
deleted file mode 100644
index 7872f25d..00000000
--- a/es/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
+++ /dev/null
@@ -1,8644 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Alineación de Preferencias con Optimización de Preferencias por Ratio de Probabilidades (ORPO)\n",
-    "\n",
-    "Este notebook te guiará en el proceso de fine-tuning de un modelo de lenguaje utilizando la Optimización de Preferencias por Ratio de Probabilidades (Odds Ratio Preference Optimization (ORPO)). Usaremos el modelo **SmolLM2-135M**, el cual **no** ha sido sometido al entrenamiento SFT, haciéndolo incompatible con DPO. Por tanto, no podrás usar el modelo que entrenaste en [1_instruction_tuning](../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb).\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "     <h2 style='margin: 0;color:blue'>Ejercicio: Alinea SmolLM2 con DPOTrainer</h2>\n",
-    "     <p>Elige un dataset del hub de Hugging Face y alinea un modelo con él.</p> \n",
-    "     <p><b>Niveles de Dificultad</b></p>\n",
-    "     <ul>\n",
-    "         <li>🐢 Usa el dataset `trl-lib/ultrafeedback_binarized`</li>\n",
-    "         <li>🐕 Prueba el dataset `argilla/ultrafeedback-binarized-preferences`</li>\n",
-    "         <li>🦁 Trabaja con un subconjunto del dataset `mlabonne/orpo-dpo-mix-40k`</li>\n",
-    "     </ul>\n",
-    "</div>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Importa las librerías\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "EwNxh0pCQhfI",
-    "outputId": "e4c288e1-2fa3-4717-d9dd-ae3ec0b91e3b"
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/ben/code/smol-course/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "import os\n",
-    "from datasets import load_dataset\n",
-    "from transformers import (\n",
-    "    AutoModelForCausalLM,\n",
-    "    AutoTokenizer,\n",
-    ")\n",
-    "from trl import ORPOConfig, ORPOTrainer, setup_chat_format\n",
-    "\n",
-    "# Autenticación en Hugging Face\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Formatea el conjunto de datos"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Generating train split: 100%|██████████| 62135/62135 [00:00<00:00, 450465.27 examples/s]\n",
-      "Generating test split: 100%|██████████| 1000/1000 [00:00<00:00, 382866.64 examples/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Carga el conjunto de datos\n",
-    "\n",
-    "# TODO: 🦁🐕 cambia el conjunto de datos a uno de tu elección.\n",
-    "dataset = load_dataset(path=\"trl-lib/ultrafeedback_binarized\", split=\"train\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO: 🐕 Si tu conjunto de datos no está estructurado como listas de conversaciones, puedes usar la función `process_dataset` para convertirlo."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Define el modelo"
-   ]
-  },
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-    "outputId": "62246d3e-5e5d-4910-a9d9-dd1d24fd3c01"
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-   "outputs": [],
-   "source": [
-    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
-    "\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "# Modelo para afinar\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=model_name,\n",
-    "    torch_dtype=torch.float16,\n",
-    ").to(device)\n",
-    "model.config.use_cache = False\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
-    "model, tokenizer = setup_chat_format(model, tokenizer)\n",
-    "\n",
-    "# Establece el nombre para guardar o subir el modelo afinado\n",
-    "finetune_name = \"SmolLM2-FT-DPO\"\n",
-    "finetune_tags = [\"smol-course\", \"module_2\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Entrena el modelo con ORPO"
-   ]
-  },
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-    "orpo_args = ORPOConfig(\n",
-    "    # Tasa de aprendizaje baja para evitar el olvido catastrófico\n",
-    "    learning_rate=8e-6,\n",
-    "    # Decaimiento lineal de la tasa de aprendizaje durante el entrenamiento\n",
-    "    lr_scheduler_type=\"linear\",\n",
-    "    # Longitud máxima combinada de prompt + respuesta\n",
-    "    max_length=1024,\n",
-    "    # Longitud máxima para los prompts de entrada\n",
-    "    max_prompt_length=512,\n",
-    "    # Controla el peso de la pérdida del ratio de probabilidades (λ en el artículo)\n",
-    "    beta=0.1,\n",
-    "    # Tamaño del lote para el entrenamiento\n",
-    "    per_device_train_batch_size=2,\n",
-    "    per_device_eval_batch_size=2,\n",
-    "    # Ayuda con la estabilidad del entrenamiento acumulando gradientes antes de actualizar\n",
-    "    gradient_accumulation_steps=4,\n",
-    "    # Optimizador eficiente en memoria para CUDA, vuelve a adamw_torch en CPU/MPS\n",
-    "    optim=\"paged_adamw_8bit\" if device == \"cuda\" else \"adamw_torch\",\n",
-    "    # Número de épocas de entrenamiento\n",
-    "    num_train_epochs=1,\n",
-    "    # Cuándo ejecutar la evaluación\n",
-    "    evaluation_strategy=\"steps\",\n",
-    "    # Evalua cada 20% del entrenamiento\n",
-    "    eval_steps=0.2,\n",
-    "    # Registra las métricas en cada paso\n",
-    "    logging_steps=1,\n",
-    "    # Calentamiento gradual de la tasa de aprendizaje\n",
-    "    warmup_steps=10,\n",
-    "    # Desactivar registro externo\n",
-    "    report_to=\"none\",\n",
-    "    # Dónde guardar el modelo/puntos de control\n",
-    "    output_dir=\"./results/\",\n",
-    "    # Habilitar MPS (Metal Performance Shaders) si está disponible\n",
-    "    use_mps_device=device == \"mps\",\n",
-    "    hub_model_id=finetune_name,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer = ORPOTrainer(\n",
-    "    model=model,\n",
-    "    args=orpo_args,\n",
-    "    train_dataset=dataset[\"train\"],\n",
-    "    eval_dataset=dataset[\"test\"],\n",
-    "    processing_class=tokenizer,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.train()  # Entrena el modelo\n",
-    "\n",
-    "# Guarda el modelo\n",
-    "trainer.save_model(f\"./{finetune_name}\")\n",
-    "\n",
-    "# Sube el modelo a Hugging Face si el inicio de sesión o HF_TOKEN está configurado\n",
-    "if os.getenv(\"HF_TOKEN\"):\n",
-    "    trainer.push_to_hub(tags=finetune_tags)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 💐 ¡Has terminado!\n",
-    "\n",
-    "Este notebook ofrece una guía paso a paso para realizar fine-tuning al modelo `HuggingFaceTB/SmolLM2-135M` utilizando el `ORPOTrainer`. Siguiendo estos pasos, puedes adaptar el modelo para realizar tareas específicas de manera más efectiva. Si deseas continuar trabajando en este curso, aquí tienes algunos pasos que podrías intentar:\n",
-    "\n",
-    "- Prueba este notebook en un nivel de dificultad más alto.  \n",
-    "- Revisa el PR de un colega.  \n",
-    "- Mejora el material del curso a través de un Issue o PR.  "
-   ]
-  }
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diff --git a/es/2_preference_alignment/orpo.md b/es/2_preference_alignment/orpo.md
deleted file mode 100644
index 7f1a4029..00000000
--- a/es/2_preference_alignment/orpo.md
+++ /dev/null
@@ -1,84 +0,0 @@
-**Optimización por Ratios de Probabilidad de Preferencias (ORPO)**
-
-ORPO (Odds Ratio Preference Optimization) es una técnica novedosa de fine-tuning que combina el fine-tuning y la alineación de preferencias en un único proceso unificado. Este enfoque combinado ofrece ventajas en términos de eficiencia y rendimiento en comparación con métodos tradicionales como RLHF o DPO.
-
-## Entendiendo ORPO
-
-Los métodos de alineación como DPO suelen involucrar dos pasos separados: fine-tuning supervisado para adaptar el modelo a un dominio y formato, seguido de la alineación de preferencias para alinearse con las preferencias humanas. Mientras que el fine-tuning fino supervisado (SFT) adapta eficazmente los modelos a los dominios objetivo, puede aumentar inadvertidamente la probabilidad de generar tanto respuestas deseables como indeseables. ORPO aborda esta limitación integrando ambos pasos en un solo proceso, como se ilustra en la siguiente comparación:
-
-![Comparación de Técnicas de Alineación](https://argilla.io/images/blog/mantisnlp-rlhf/part-8-alignments.png)
-*Comparación de diferentes técnicas de alineación de modelos*
-
-## Cómo Funciona ORPO
-
-El proceso de entrenamiento de ORPO utiliza un conjunto de datos de preferencias similar al que usamos para DPO, donde cada ejemplo de entrenamiento contiene un prompt de entrada junto con dos respuestas: una preferida y otra rechazada. A diferencia de otros métodos de alineación que requieren etapas separadas y modelos de referencia, ORPO integra la alineación de preferencias directamente en el proceso de fine-tuning supervisado. Este enfoque monolítico lo hace libre de modelos de referencia, computacionalmente más eficiente y eficiente en memoria, con menos FLOPs.
-
-ORPO crea un nuevo objetivo combinando dos componentes principales:
-
-1. **Pérdida de SFT**: La pérdida estándar de log-verosimilitud negativa utilizada en la modelización de lenguaje, que maximiza la probabilidad de generar tokens de referencia. Esto ayuda a mantener las capacidades generales del modelo en lenguaje.
-
-2. **Pérdida de Odds Ratio**: Un componente novedoso que penaliza respuestas no deseadas mientras recompensa las preferidas. Esta función de pérdida utiliza odds ratios para contrastar eficazmente entre respuestas favorecidas y desfavorecidas a nivel de token.
-
-Juntos, estos componentes guían al modelo para adaptarse a las generaciones deseadas para el dominio específico mientras desalienta activamente las generaciones del conjunto de respuestas rechazadas. El mecanismo de odds ratio proporciona una manera natural de medir y optimizar las preferencias del modelo entre respuestas elegidas y rechazadas. Si deseas profundizar en las matemáticas, puedes leer el [artículo de ORPO](https://arxiv.org/abs/2403.07691). Si deseas aprender sobre ORPO desde la perspectiva de implementación, puedes revisar cómo se calcula la pérdida de ORPO en la [biblioteca TRL](https://github.com/huggingface/trl/blob/b02189aaa538f3a95f6abb0ab46c0a971bfde57e/trl/trainer/orpo_trainer.py#L660).
-
-## Rendimiento y Resultados
-
-ORPO ha demostrado resultados impresionantes en varios puntos de referencia. En MT-Bench, alcanza puntuaciones competitivas en diferentes categorías:
-
-![Resultados MT-Bench](https://argilla.io/images/blog/mantisnlp-rlhf/part-8-mtbench.png)
-*Resultados de MT-Bench por categoría para modelos Mistral-ORPO*
-
-Cuando se compara con otros métodos de alineación, ORPO muestra un rendimiento superior en AlpacaEval 2.0:
-
-![Resultados AlpacaEval](https://argilla.io/images/blog/mantisnlp-rlhf/part-8-winrate.png)
-*Puntuaciones de AlpacaEval 2.0 en diferentes métodos de alineación*
-
-En comparación con SFT+DPO, ORPO reduce los requisitos computacionales al eliminar la necesidad de un modelo de referencia y reducir a la mitad el número de pasadas hacia adelante por lote. Además, el proceso de entrenamiento es más estable en diferentes tamaños de modelos y conjuntos de datos, lo que requiere menos hiperparámetros para ajustar. En cuanto al rendimiento, ORPO iguala a modelos más grandes mientras muestra una mejor alineación con las preferencias humanas.
-
-## Implementación
-
-La implementación exitosa de ORPO depende en gran medida de datos de preferencias de alta calidad. Los datos de entrenamiento deben seguir pautas claras de anotación y proporcionar una representación equilibrada de respuestas preferidas y rechazadas en diversos escenarios.
-
-### Implementación con TRL
-
-ORPO se puede implementar utilizando la biblioteca Transformers Reinforcement Learning (TRL). Aquí tienes un ejemplo básico:
-
-```python
-from trl import ORPOConfig, ORPOTrainer
-
-# Configurar el entrenamiento de ORPO
-orpo_config = ORPOConfig(
-    learning_rate=1e-5,
-    per_device_train_batch_size=4,
-    gradient_accumulation_steps=4,
-    max_steps=1000,
-    orpo_alpha=1.0,  # Controla la fuerza de la optimización de preferencias
-    orpo_beta=0.1,   # Parámetro de temperatura para el cálculo de odds ratio
-)
-
-# Inicializar el entrenador
-trainer = ORPOTrainer(
-    model=model,
-    args=orpo_config,
-    train_dataset=dataset,
-    tokenizer=tokenizer,
-)
-
-# Iniciar el entrenamiento
-trainer.train()
-```
-
-Parámetros clave a considerar:
-- `orpo_alpha`: Controla la fuerza de la optimización de preferencias
-- `orpo_beta`: Parámetro de temperatura para el cálculo de odds ratio
-- `learning_rate`: Debe ser relativamente pequeño para evitar el olvido catastrófico
-- `gradient_accumulation_steps`: Ayuda con la estabilidad del entrenamiento
-
-## Próximos Pasos
-
-⏩ Prueba el [Tutorial de ORPO](./notebooks/orpo_tutorial.ipynb) para implementar este enfoque unificado de alineación de preferencias.
-
-## Recursos
-- [Artículo de ORPO](https://arxiv.org/abs/2403.07691)
-- [Documentación de TRL](https://huggingface.co/docs/trl/index)
-- [Guía de RLHF de Argilla](https://argilla.io/blog/mantisnlp-rlhf-part-8/)
\ No newline at end of file
diff --git a/es/3_parameter_efficient_finetuning/README.md b/es/3_parameter_efficient_finetuning/README.md
deleted file mode 100644
index 2dacad4a..00000000
--- a/es/3_parameter_efficient_finetuning/README.md
+++ /dev/null
@@ -1,40 +0,0 @@
-# Fine-Tuning Eficiente en Parámetros (PEFT)
-
-A medida que los modelos de lenguaje se hacen más grandes, el *fine-tuning* tradicional se vuelve cada vez más complicado. Afinar completamente un modelo con 1.7 mil millones de parámetros, por ejemplo, requiere una memoria de GPU significativa, hace costoso almacenar copias separadas del modelo y puede ocasionar un olvido catastrófico de las capacidades originales del modelo. Los métodos de *fine-tuning* eficiente en parámetros (*Parameter-Efficient Fine-Tuning* o PEFT) abordan estos problemas modificando solo un subconjunto pequeño de los parámetros del modelo, mientras que la mayor parte del modelo permanece congelada.
-
-El *fine-tuning* tradicional actualiza todos los parámetros del modelo durante el entrenamiento, lo cual resulta poco práctico para modelos grandes. Los métodos PEFT introducen enfoques para adaptar modelos utilizando una fracción mínima de parámetros entrenables, generalmente menos del 1% del tamaño original del modelo. Esta reducción dramática permite:
-
-- Realizar *fine-tuning* en hardware de consumo con memoria de GPU limitada.
-- Almacenar eficientemente múltiples adaptaciones de tareas específicas.
-- Mejorar la generalización en escenarios con pocos datos.
-- Entrenamientos y ciclos de iteración más rápidos.
-
-## Métodos Disponibles
-
-En este módulo, se cubrirán dos métodos populares de PEFT:
-
-### 1️⃣ LoRA (Adaptación de Bajo Rango)
-
-LoRA se ha convertido en el método PEFT más adoptado, ofreciendo una solución sofisticada para la adaptación eficiente de modelos. En lugar de modificar el modelo completo, **LoRA inyecta matrices entrenables en las capas de atención del modelo.** Este enfoque, por lo general, reduce los parámetros entrenables en aproximadamente un 90%, manteniendo un rendimiento comparable al *fine-tuning* completo. Exploraremos LoRA en la sección [LoRA (Adaptación de Bajo Rango)](./lora_adapters.md).
-
-### 2️⃣ *Prompt Tuning*
-
-El *prompt tuning* ofrece un enfoque **aún más ligero** al **añadir tokens entrenables a la entrada** en lugar de modificar los pesos del modelo. Aunque es menos popular que LoRA, puede ser útil para adaptar rápidamente un modelo a nuevas tareas o dominios. Exploraremos el *prompt tuning* en la sección [Prompt Tuning](./prompt_tuning.md).
-
-## Notebooks de Ejercicios
-
-| Título | Descripción | Ejercicio | Enlace | Colab |
-|-------|-------------|-----------|--------|-------|
-| *Fine-tuning* con LoRA | Aprende a realizar *fine-tuning* con adaptadores LoRA | 🐢 Entrena un modelo con LoRA<br>🐕 Experimenta con diferentes valores de rango<br>🦁 Compara el rendimiento con el *fine-tuning* completo | [Notebook](./notebooks/finetune_sft_peft.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Abrir en Colab"/></a> |
-| Carga Adaptadores LoRA | Aprende a cargar y usar adaptadores LoRA entrenados | 🐢 Carga adaptadores preentrenados<br>🐕 Combina adaptadores con el modelo base<br>🦁 Alterna entre múltiples adaptadores | [Notebook](./notebooks/load_lora_adapter_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/load_lora_adapter_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Abrir en Colab"/></a> |
-
-<!-- | Prompt Tuning | Aprende a implementar *prompt tuning* | 🐢 Entrenar *soft prompts*<br>🐕 Comparar diferentes estrategias de inicialización<br>🦁 Evaluar en múltiples tareas | [Notebook](./notebooks/prompt_tuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/prompt_tuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Abrir en Colab"/></a> | -->
-
-## Recursos
-- [Documentación de PEFT](https://huggingface.co/docs/peft)
-- [Artículo de LoRA](https://arxiv.org/abs/2106.09685)
-- [Artículo de QLoRA](https://arxiv.org/abs/2305.14314)
-- [Artículo de *Prompt Tuning*](https://arxiv.org/abs/2104.08691)
-- [Guía de PEFT en Hugging Face](https://huggingface.co/blog/peft)
-- [Cómo hacer *Fine-Tuning* de LLMs en 2024 con Hugging Face](https://www.philschmid.de/fine-tune-llms-in-2024-with-trl)
-- [TRL](https://huggingface.co/docs/trl/index)
\ No newline at end of file
diff --git a/es/3_parameter_efficient_finetuning/lora_adapters.md b/es/3_parameter_efficient_finetuning/lora_adapters.md
deleted file mode 100644
index d6ed38e0..00000000
--- a/es/3_parameter_efficient_finetuning/lora_adapters.md
+++ /dev/null
@@ -1,124 +0,0 @@
-# LoRA (Adaptación de Bajo Rango) 
-
-LoRA (Low-Rank Adaptation) se ha convertido en el método PEFT más utilizado. Su funcionamiento se basa en añadir matrices de descomposición de rango reducido a los pesos de atención, lo que suele reducir los parámetros entrenables en un 90%.
-
-## Entendiendo LoRA
-
-LoRA es una técnica de fine-tuning eficiente en parámetros que congela los pesos del modelo preentrenado e inyecta matrices entrenables de descomposición de rango reducido en las capas del modelo. En lugar de entrenar todos los parámetros del modelo durante el fine-tuning, LoRA descompone las actualizaciones de los pesos en matrices más pequeñas mediante descomposición de rango, reduciendo significativamente la cantidad de parámetros entrenables mientras mantiene el rendimiento del modelo. Por ejemplo, se ha aplicado a GPT-3 175B. LoRA redujo los parámetros entrenables 10 000 veces y los requerimientos de memoria de GPU 3 veces en comparación con el fine-tuning completo. Puedes leer más sobre LoRA en el [paper de LoRA](https://arxiv.org/pdf/2106.09685).
-
-LoRA funciona añadiendo pares de matrices de descomposición de rango reducido a las capas del transformador, normalmente enfocándose en los pesos de atención. Durante la inferencia, estos pesos adaptadores pueden fusionarse con el modelo base, lo que elimina la sobrecarga de latencia adicional. LoRA es especialmente útil para adaptar modelos de lenguaje grandes a tareas o dominios específicos mientras se mantienen requisitos de recursos manejables.
-
-## Cargando adaptadores LoRA
-
-Se pueden cargar adaptadores en un modelo preentrenado utilizando `load_adapter()`, lo cual es útil para probar diferentes adaptadores cuyos pesos no están fusionados. Se pueden establecer los pesos de los adaptadores activos con la función `set_adapter()`. Para devolver el modelo base, se puede usar `unload()` para descargar todos los módulos de LoRA. Esto permite cambiar fácilmente entre diferentes pesos específicos de tareas.
-
-```python
-from transformers import AutoModelForCausalLM
-from peft import PeftModel
-
-base_model = AutoModelForCausalLM.from_pretrained("<base_model_name>")
-peft_model_id = "<peft_adapter_id>"
-model = PeftModel.from_pretrained(base_model, peft_model_id)
-```
-
-![lora_load_adapter](./images/lora_adapter.png)
-
-## Fusionando adaptadores LoRA
-
-Después de entrenar con LoRA, podrías querer fusionar los pesos del adaptador de vuelta en el modelo base para facilitar su implementación. Esto crea un único modelo con los pesos combinados, eliminando la necesidad de cargar adaptadores por separado durante la inferencia.
-
-El proceso de fusión requiere atención al manejo de memoria y precisión. Como necesitarás cargar tanto el modelo base como los pesos del adaptador simultáneamente, asegúrate de tener suficiente memoria GPU/CPU disponible. Usar `device_map="auto"` en `transformers` ayudará con el manejo automático de memoria. Mantén una precisión consistente (por ejemplo, `float16`) durante todo el proceso, y guarda el modelo fusionado en el mismo formato para su implementación. Antes de implementar, siempre valida el modelo fusionado comparando sus salidas y métricas de rendimiento con la versión basada en adaptadores.
-
-Los adaptadores también son convenientes para cambiar entre diferentes tareas o dominios. Puedes cargar el modelo base y los pesos del adaptador por separado, permitiendo un cambio rápido entre diferentes pesos específicos de tareas.
-
-## Guía de implementación
-
-El directorio `notebooks/` contiene tutoriales prácticos y ejercicios para implementar diferentes métodos PEFT. Comienza con `load_lora_adapter_example.ipynb` para una introducción básica, y luego explora `lora_finetuning.ipynb` para un análisis más detallado de cómo realizar fine-tuning en un modelo con LoRA y SFT.
-
-Cuando implementes métodos PEFT, comienza con valores de rango pequeños (4-8) para LoRA y monitoriza la pérdida durante el entrenamiento. Utiliza conjuntos de validación para evitar el sobreajuste y compara los resultados con los baselines del fine-tuning completo cuando sea posible. La efectividad de los diferentes métodos puede variar según la tarea, por lo que la experimentación es clave.
-
-## OLoRA
-
-[OLoRA](https://arxiv.org/abs/2406.01775) utiliza descomposición QR para inicializar los adaptadores LoRA. OLoRA traduce los pesos base del modelo por un factor de sus descomposiciones QR, es decir, muta los pesos antes de realizar cualquier entrenamiento sobre ellos. Este enfoque mejora significativamente la estabilidad, acelera la velocidad de convergencia y logra un rendimiento superior.
-
-## Usando TRL con PEFT
-
-Los métodos PEFT pueden combinarse con TRL (Transformers Reinforcement Learning) para un fine-tuning eficiente. Esta integración es particularmente útil para RLHF (Reinforcement Learning from Human Feedback), ya que reduce los requisitos de memoria.
-
-```python
-from peft import LoraConfig
-from transformers import AutoModelForCausalLM
-
-# Carga el modelo con configuración PEFT
-lora_config = LoraConfig(
-    r=16,
-    lora_alpha=32,
-    lora_dropout=0.05,
-    bias="none",
-    task_type="CAUSAL_LM"
-)
-
-# Carga el modelo en un dispositivo específico
-model = AutoModelForCausalLM.from_pretrained(
-    "your-model-name",
-    load_in_8bit=True,  # Opcional: usa la precisión de 8 bits
-    device_map="auto",
-    peft_config=lora_config
-)
-```
-
-Aquí, usamos `device_map="auto"` para asignar automáticamente el modelo al dispositivo correcto. También puedes asignar manualmente el modelo a un dispositivo específico usando `device_map={"": device_index}`. Además, podrías escalar el entrenamiento en múltiples GPUs mientras mantienes un uso eficiente de memoria.
-
-## Implementación básica de fusión
-
-Después de entrenar un adaptador LoRA, puedes fusionar los pesos del adaptador de vuelta en el modelo base. Aquí tienes cómo hacerlo:
-
-```python
-import torch
-from transformers import AutoModelForCausalLM
-from peft import PeftModel
-
-# 1. Carga el modelo base
-base_model = AutoModelForCausalLM.from_pretrained(
-    "base_model_name",
-    torch_dtype=torch.float16,
-    device_map="auto"
-)
-
-# 2. Carga el modelo PEFT con adaptador
-peft_model = PeftModel.from_pretrained(
-    base_model,
-    "path/to/adapter",
-    torch_dtype=torch.float16
-)
-
-# 3. Fusiona los pesos del adaptador con el modelo base
-try:
-    merged_model = peft_model.merge_and_unload()
-except RuntimeError as e:
-    print(f"Fusión fallida: {e}")
-    # Implementa estrategia de respaldo u optimización de memoria
-
-# 4. Guarda el modelo fusionado
-merged_model.save_pretrained("path/to/save/merged_model")
-```
-
-Si encuentras discrepancias de tamaño en el modelo guardado, asegúrate de guardar también el tokenizer:
-
-```python
-# Guarda tanto el modelo como el tokenizer
-tokenizer = AutoTokenizer.from_pretrained("base_model_name")
-merged_model.save_pretrained("path/to/save/merged_model")
-tokenizer.save_pretrained("path/to/save/merged_model")
-```
-
-## Próximos pasos
-
-⏩ Pasa al [Prompt Tuning](prompt_tuning.md) para aprender a realizar fine-tuning en un modelo con prompt tuning.  
-⏩ Revisa el [Tutorial de Carga de Adaptadores LoRA](./notebooks/load_lora_adapter.ipynb) para aprender a cargar adaptadores LoRA.
-
-# Recursos
-
-- [LORA: ADAPTACIÓN DE BAJO RANGO PARA MODELOS DE LENGUAJE DE GRAN TAMAÑO](https://arxiv.org/pdf/2106.09685)  
-- [Documentación de PEFT](https://huggingface.co/docs/peft)  
-- [Publicación en el blog de Hugging Face sobre PEFT](https://huggingface.co/blog/peft)  
\ No newline at end of file
diff --git a/es/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb b/es/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
deleted file mode 100644
index 60408c89..00000000
--- a/es/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
+++ /dev/null
@@ -1,514 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "z-6LLOPZouLg"
-   },
-   "source": [
-    "# Cómo afinar los modelos de lenguaje grandes con adaptadores LoRA usando Hugging Face TRL\n",
-    "\n",
-    "Este notebook demuestra cómo afinar con precisión de manera eficiente modelos de lenguaje grandes usando adaptadores LoRA (adaptación de bajo rango). LoRA es una técnica de fine-tuning con eficiencia de parámetros que:\n",
-    "- Congela los pesos del modelo pre-entrenado.\n",
-    "- Agrega pequeñas matrices de descomposición de rangos entrenables a las capas de atención\n",
-    "- Generalmente reduce los parámetros entrenables en aproximadamente un 90 %\n",
-    "- Mantiene el rendimiento del modelo mientras es eficiente en el uso de la memoria\n",
-    "\n",
-    "Cubriremos:\n",
-    "1. Configuración del entorno de desarrollo y configuración de LoRA\n",
-    "2. Creación y preparación del conjunto de datos para el entrenamiento del adaptador\n",
-    "3. _Fine-tuning_ utilizando `trl` y `SFTTrainer` con adaptadores LoRA\n",
-    "4. Prueba del modelo y fusión de adaptadores (opcional)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "fXqd9BXgouLi"
-   },
-   "source": [
-    "## 1. Configura el entorno de desarrollo\n",
-    "\n",
-    "Nuestro primer paso es instalar las librerías de Hugging Face y PyTorch, incluidas `trl`, `transformers` y `datasets`. Si todavía no sabes qué es `trl`, no te preocupes. Se trata de una librería nueva integrada con `transformers`y `datasets`, que facilita el fine-tuning, aprendizaje por refuerzo o el alineamiento de modelos de lenguaje.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "tKvGVxImouLi"
-   },
-   "outputs": [],
-   "source": [
-    "# Instala los requisitos en Google Colab\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "# Autenticación en Hugging Face\n",
-    "\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()\n",
-    "\n",
-    "# Por comodidad, puedes crear una variable de entorno que contenga tu token de Hugging Face como HF_TOKEN"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "XHUzfwpKouLk"
-   },
-   "source": [
-    "## 2. Carga el conjunto de datos"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "id": "z4p6Bvo7ouLk"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['full_topic', 'messages'],\n",
-       "        num_rows: 2260\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['full_topic', 'messages'],\n",
-       "        num_rows: 119\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Carga el conjunto de datos\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "# TODO: define tu conjunto de datos y configuración utilizando los parámetros de ruta y nombre.\n",
-    "dataset = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")\n",
-    "dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "9TOhJdtsouLk"
-   },
-   "source": [
-    "## 3. Fine-tuning del LLM usando `trl` y el `SFTTrainer` con LoRA\n",
-    "\n",
-    "El [SFTTrainer](https://huggingface.co/docs/trl/sft_trainer) de `trl` está integrado con los adaptadores LoRA mediante la biblioteca [PEFT](https://huggingface.co/docs/peft/en/index). Las ventajas clave de esta configuración incluyen:\n",
-    "\n",
-    "1. **Eficiencia de memoria**: \n",
-    "   - Solo los parámetros del adaptador se almacenan en la memoria de la GPU\n",
-    "   - Los pesos del modelo base permanecen congelados y pueden cargarse en menor precisión\n",
-    "   - Permite el fine-tuning de modelos grandes en GPUs de consumo\n",
-    "\n",
-    "2. **Características de entrenamiento**:\n",
-    "   - Integración nativa de PEFT/LoRA con configuración mínima\n",
-    "   - Soporte para QLoRA (LoRA Cuantizado) para una eficiencia de memoria aún mejor\n",
-    "\n",
-    "3. **Gestión de los adaptadores**:\n",
-    "   - Guardado de pesos del adaptador durante puntos de control\n",
-    "   - Funciones para fusionar adaptadores de vuelta al modelo base\n",
-    "\n",
-    "Usaremos LoRA en nuestro ejemplo, que combina LoRA con cuantización de 4 bits para reducir aún más el uso de memoria sin sacrificar el rendimiento. La configuración requiere solo unos pocos pasos:\n",
-    "1. Definir la configuración de LoRA (rank, alpha, dropout)\n",
-    "2. Crear el SFTTrainer con la configuración PEFT\n",
-    "3. Entrenar y guardar los pesos del adaptador"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Importa las librerias necesarias\n",
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "from datasets import load_dataset\n",
-    "from trl import SFTConfig, SFTTrainer, setup_chat_format\n",
-    "import torch\n",
-    "\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "# Carga el modelo y el tokenizador\n",
-    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
-    "\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=model_name\n",
-    ").to(device)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
-    "\n",
-    "# Establece el formato de chat\n",
-    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n",
-    "\n",
-    "# Establece el nombre para nuestro fine-tuning que se guardará y/o subirá\n",
-    "finetune_name = \"SmolLM2-FT-MyDataset\"\n",
-    "finetune_tags = [\"smol-course\", \"module_1\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ZbuVArTHouLk"
-   },
-   "source": [
-    "El SFTTrainer cuenta con una integración nativa con peft, lo que hace que sea muy sencillo ajustar eficientemente los LLMs utilizando, por ejemplo, LoRA. Solo necesitamos crear nuestra LoraConfig y proporcionarla al entrenador. \n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'> \n",
-    "    <h2 style='margin: 0;color:blue'>Ejercicio: Define los parámetros de LoRA para fine-tuning</h2> \n",
-    "    <p>Escoge un conjunto de datos de Hugging Face y realiza fine-tuning a un modelo con él.</p> \n",
-    "    <p><b>Niveles de dificultad</b></p> \n",
-    "    <p>🐢 Usa los parámetros generales para un fine-tuning arbitrario</p> \n",
-    "    <p>🐕 Ajusta los parámetros y revisalos en weights & biases.</p> \n",
-    "    <p>🦁 Ajusta los parámetros y muestra el cambio en los resultados de inferencia.\n",
-    "    </p> \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "blDSs9swouLk"
-   },
-   "outputs": [],
-   "source": [
-    "from peft import LoraConfig\n",
-    "\n",
-    "# TODO: Configura los parámetros de LoRA\n",
-    "# r: dimensión de rango para las matrices de actualización de LoRA (más pequeño = más compresión)\n",
-    "rank_dimension = 6\n",
-    "# lora_alpha: factor de escala para las capas LoRA (más alto = adaptación más fuerte)\n",
-    "lora_alpha = 8\n",
-    "# lora_dropout: probabilidad de dropout para las capas LoRA (ayuda a prevenir el sobreajuste)\n",
-    "lora_dropout = 0.05\n",
-    "\n",
-    "peft_config = LoraConfig(\n",
-    "    r=rank_dimension,  # Dimensión de rango - normalmente entre 4-32\n",
-    "    lora_alpha=lora_alpha,  # Factor de escala LoRA - normalmente 2x el rango\n",
-    "    lora_dropout=lora_dropout,  # Probabilidad de dropout para las capas LoRA\n",
-    "    bias=\"none\",  # Tipo de sesgo para LoRA. los sesgos correspondientes se actualizarán durante el entrenamiento.\n",
-    "    target_modules=\"all-linear\",  # A qué módulos aplicar LoRA\n",
-    "    task_type=\"CAUSAL_LM\",  # Tipo de tarea para la arquitectura del modelo\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "l5NUDPcaouLl"
-   },
-   "source": [
-    "Antes de comenzar nuestro entrenamiento, necesitamos definir los hiperparámetros (TrainingArguments) que queremos utilizar."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "NqT28VZlouLl"
-   },
-   "outputs": [],
-   "source": [
-    "# Configuración del entrenamiento\n",
-    "# Hiperparámetros basados en las recomendaciones del artículo sobre QLoRA\n",
-    "args = SFTConfig(\n",
-    "    # Configuración de salida\n",
-    "    output_dir=finetune_name,  # Directorio para guardar los checkpoints del modelo\n",
-    "    # Duración del entrenamiento\n",
-    "    num_train_epochs=1,  # Número de épocas de entrenamiento\n",
-    "    # Configuración del tamaño de lote\n",
-    "    per_device_train_batch_size=2,  # Tamaño de lote por GPU\n",
-    "    gradient_accumulation_steps=2,  # Acumular gradientes para un lote efectivo más grande\n",
-    "    # Optimización de memoria\n",
-    "    gradient_checkpointing=True,  # Intercambiar cómputo por ahorro de memoria\n",
-    "    # Configuración del optimizador\n",
-    "    optim=\"adamw_torch_fused\",  # Usar AdamW fusionado para mayor eficiencia\n",
-    "    learning_rate=2e-4,  # Tasa de aprendizaje (artículo sobre QLoRA)\n",
-    "    max_grad_norm=0.3,  # Umbral de recorte de gradiente\n",
-    "    # Programación de la tasa de aprendizaje\n",
-    "    warmup_ratio=0.03,  # Porción de pasos para el calentamiento\n",
-    "    lr_scheduler_type=\"constant\",  # Mantener la tasa de aprendizaje constante después del calentamiento\n",
-    "    # Registro y guardado\n",
-    "    logging_steps=10,  # Registrar métricas cada N pasos\n",
-    "    save_strategy=\"epoch\",  # Guardar checkpoint cada época\n",
-    "    # Configuración de precisión\n",
-    "    bf16=True,  # Usar precisión bfloat16\n",
-    "    # Configuración de integración\n",
-    "    push_to_hub=False,  # No subir a HuggingFace\n",
-    "    report_to=\"none\",  # Desactivar registro externo\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "cGhR7uFBouLl"
-   },
-   "source": [
-    "Ahora tenemos todos los componentes básicos que necesitamos para crear nuestro `SFTTrainer` y comenzar a entrenar nuestro modelo."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "M00Har2douLl"
-   },
-   "outputs": [],
-   "source": [
-    "max_seq_length = 1512  # longitud máxima de secuencia para el modelo y el empaquetado del conjunto de datos\n",
-    "\n",
-    "# Crea SFTTrainer con configuración LoRA\n",
-    "trainer = SFTTrainer(\n",
-    "    model=model,\n",
-    "    args=args,\n",
-    "    train_dataset=dataset[\"train\"],\n",
-    "    peft_config=peft_config,  # Configuración LoRA\n",
-    "    max_seq_length=max_seq_length,  # Longitud máxima de la secuencia\n",
-    "    tokenizer=tokenizer,\n",
-    "    packing=True,  # Habilita el empaquetado de entrada para mayor eficiencia\n",
-    "    dataset_kwargs={\n",
-    "        \"add_special_tokens\": False,  # Los tokens especiales son manejados por la plantilla\n",
-    "        \"append_concat_token\": False,  # No se necesita un separador adicional\n",
-    "    },\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "zQ_kRN24ouLl"
-   },
-   "source": [
-    "Comienza a entrenar nuestro modelo llamando al método `train()` en nuestra instancia de `Trainer`. Esto iniciará el bucle de entrenamiento y entrenará nuestro modelo durante 3 épocas. Dado que estamos utilizando un método PEFT, solo guardaremos los pesos del modelo adaptado y no el modelo completo."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Tq4nIYqKouLl"
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "300e5dfbb4b54750b77324345c7591f9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/72 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "TrainOutput(global_step=72, training_loss=1.6402628521124523, metrics={'train_runtime': 195.2398, 'train_samples_per_second': 1.485, 'train_steps_per_second': 0.369, 'total_flos': 282267289092096.0, 'train_loss': 1.6402628521124523, 'epoch': 0.993103448275862})"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Inicia el entrenamiento, el modelo se guardará automáticamente en Hugging Face y en el directorio de salida\n",
-    "trainer.train()\n",
-    "\n",
-    "# Guarda el modelo\n",
-    "trainer.save_model()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "y4HHSYYzouLl"
-   },
-   "source": [
-    "El entrenamiento con Flash Attention durante 3 épocas con un conjunto de datos de 15k muestras tardó 4:14:36 en un `g5.2xlarge`. La instancia cuesta `1.21$/h`, lo que nos lleva a un coste total de solo ~`5.3$`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "C309KsXjouLl"
-   },
-   "source": [
-    "### Fusiona el Adaptador LoRA con el Modelo Original\n",
-    "\n",
-    "Cuando se usa LoRA, solo entrenamos los pesos del adaptador mientras mantenemos el modelo base congelado. Durante el entrenamiento, guardamos solo estos pesos de adaptador ligeros (~2-10MB) en lugar de una copia completa del modelo. Sin embargo, para la implementación, es posible que desee volver a fusionar los adaptadores con el modelo base para:\n",
-    "\n",
-    "1.  **Implementación Simplificada**: Un solo archivo de modelo en lugar de modelo base + adaptadores.\n",
-    "2.  **Velocidad de Inferencia**: Sin sobrecarga de computación del adaptador.\n",
-    "3.  **Compatibilidad con Frameworks**: Mejor compatibilidad con frameworks de servicio.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from peft import AutoPeftModelForCausalLM\n",
-    "\n",
-    "\n",
-    "# Carga el modelo PEFT en CPU\n",
-    "model = AutoPeftModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=args.output_dir,\n",
-    "    torch_dtype=torch.float16,\n",
-    "    low_cpu_mem_usage=True,\n",
-    ")\n",
-    "\n",
-    "# Fusiona LoRA y el modelo base, y guarda\n",
-    "merged_model = model.merge_and_unload()\n",
-    "merged_model.save_pretrained(\n",
-    "    args.output_dir, safe_serialization=True, max_shard_size=\"2GB\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "-yO6E9quouLl"
-   },
-   "source": [
-    "## 3. Prueba el Modelo y Ejecuta la Inferencia\n",
-    "\n",
-    "Una vez que finaliza el entrenamiento, queremos probar nuestro modelo. Cargaremos diferentes muestras del conjunto de datos original y evaluaremos el modelo en esas muestras, utilizando un bucle simple y la precisión como nuestra métrica.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Ejercicio Adicional: Carga el Adaptador LoRA</h2>\n",
-    "    <p>Utiliza lo que aprendiste del notebook de ejemplo para cargar tu adaptador LoRA entrenado para la inferencia.</p> \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "id": "I5B494OdouLl"
-   },
-   "outputs": [],
-   "source": [
-    "# Libera memoria otra vez\n",
-    "del model\n",
-    "del trainer\n",
-    "torch.cuda.empty_cache()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "P1UhohVdouLl"
-   },
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from peft import AutoPeftModelForCausalLM\n",
-    "from transformers import AutoTokenizer, pipeline\n",
-    "\n",
-    "# Carga el modelo con el adaptador PEFT\n",
-    "tokenizer = AutoTokenizer.from_pretrained(finetune_name)\n",
-    "model = AutoPeftModelForCausalLM.from_pretrained(\n",
-    "    finetune_name, device_map=\"auto\", torch_dtype=torch.float16\n",
-    ")\n",
-    "pipe = pipeline(\n",
-    "    \"text-generation\", model=merged_model, tokenizer=tokenizer, device=device\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "99uFDAuuouLl"
-   },
-   "source": [
-    "Probemos algunas muestras de *prompts* y veamos cómo se desempeña el modelo."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "id": "-shSmUbvouLl",
-    "outputId": "16d97c61-3b31-4040-c780-3c4de75c3824"
-   },
-   "outputs": [],
-   "source": [
-    "prompts = [\n",
-    "    \"What is the capital of Germany? Explain why thats the case and if it was different in the past?\",\n",
-    "    \"Write a Python function to calculate the factorial of a number.\",\n",
-    "    \"A rectangular garden has a length of 25 feet and a width of 15 feet. If you want to build a fence around the entire garden, how many feet of fencing will you need?\",\n",
-    "    \"What is the difference between a fruit and a vegetable? Give examples of each.\",\n",
-    "]\n",
-    "\n",
-    "\n",
-    "def test_inference(prompt):\n",
-    "    prompt = pipe.tokenizer.apply_chat_template(\n",
-    "        [{\"role\": \"user\", \"content\": prompt}],\n",
-    "        tokenize=False,\n",
-    "        add_generation_prompt=True,\n",
-    "    )\n",
-    "    outputs = pipe(\n",
-    "        prompt,\n",
-    "    )\n",
-    "    return outputs[0][\"generated_text\"][len(prompt) :].strip()\n",
-    "\n",
-    "\n",
-    "for prompt in prompts:\n",
-    "    print(f\"    prompt:\\n{prompt}\")\n",
-    "    print(f\"    response:\\n{test_inference(prompt)}\")\n",
-    "    print(\"-\" * 50)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.10"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/es/3_parameter_efficient_finetuning/notebooks/load_lora_adapter_example.ipynb b/es/3_parameter_efficient_finetuning/notebooks/load_lora_adapter_example.ipynb
deleted file mode 100644
index bdc5516d..00000000
--- a/es/3_parameter_efficient_finetuning/notebooks/load_lora_adapter_example.ipynb
+++ /dev/null
@@ -1,5060 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "otj46qRbtpnd",
-        "outputId": "0fa81296-44b7-479d-e03b-7ef196cc89e4"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install -q bitsandbytes datasets accelerate loralib\n",
-        "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 446,
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-          ]
-        },
-        "id": "cg3fiQOvmI3Q",
-        "outputId": "92fcd520-ad2c-4488-8f66-664ffc566b89"
-      },
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
-        "import torch\n",
-        "import torch.nn as nn\n",
-        "import bitsandbytes as bnb\n",
-        "from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "S65GcxNGA9kz"
-      },
-      "source": [
-        "## Carga los adaptadores desde Hugging Face"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 517,
-          "referenced_widgets": [
-            "ff2454cf69b346fea70070522cf93689",
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-            "75913676a5df43fbbfe744b8882188df",
-            "1e94c570880449d0a2763bc1eba09057",
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-            "39f1a52884e3440c8b521e493d6e1a53",
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-            "ce8d4bac782949579a2e52864455d9de",
-            "523deba849044669bfe8c959750708fc"
-          ]
-        },
-        "id": "hsD1VKqeA62Z",
-        "outputId": "83ecbf31-3f84-4b8e-d667-10317f7adcec"
-      },
-      "outputs": [],
-      "source": [
-        "import torch\n",
-        "import transformers\n",
-        "from datasets import load_dataset\n",
-        "from peft import LoraConfig, get_peft_model\n",
-        "from peft import PeftModel, PeftConfig\n",
-        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-        "\n",
-        "peft_model_id = \"ybelkada/opt-6.7b-lora\"\n",
-        "config = PeftConfig.from_pretrained(peft_model_id)\n",
-        "model = AutoModelForCausalLM.from_pretrained(\n",
-        "    config.base_model_name_or_path,\n",
-        "    return_dict=True,\n",
-        "    load_in_8bit=True,\n",
-        "    device_map=\"auto\",\n",
-        ")\n",
-        "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
-        "\n",
-        "# Carga el modelo Lora\n",
-        "model = PeftModel.from_pretrained(model, peft_model_id)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "MDqJWba-tpnv",
-        "outputId": "dd65644f-6921-46e1-920c-400bd7d888cf"
-      },
-      "outputs": [],
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diff --git a/es/3_parameter_efficient_finetuning/prompt_tuning.md b/es/3_parameter_efficient_finetuning/prompt_tuning.md
deleted file mode 100644
index 00882616..00000000
--- a/es/3_parameter_efficient_finetuning/prompt_tuning.md
+++ /dev/null
@@ -1,74 +0,0 @@
-# Ajuste de Prompts
-
-El ajuste de prompts es un enfoque eficiente en términos de parámetros que modifica las representaciones de entrada en lugar de los pesos del modelo. A diferencia del fine-tuning tradicional que actualiza todos los parámetros del modelo, el ajuste de prompts agrega y optimiza un pequeño conjunto de tokens entrenables mientras mantiene el modelo base congelado.
-
-## Entendiendo el Ajuste de Prompts
-
-El ajuste de prompts es una alternativa eficiente en términos de parámetros al fine-tuning del modelo que antepone vectores continuos entrenables (prompts suaves) al texto de entrada. A diferencia de los prompts discretos de texto, estos prompts suaves se aprenden mediante retropropagación mientras el modelo de lenguaje se mantiene congelado. El método fue introducido en ["El poder de la escala para el ajuste de prompts eficiente en parámetros"](https://arxiv.org/abs/2104.08691) (Lester et al., 2021), que demostró que el ajuste de prompts se vuelve más competitivo con el fine-tuning del modelo a medida que aumenta el tamaño del modelo. En el artículo, alrededor de 10 mil millones de parámetros, el ajuste de prompts iguala el rendimiento del fine-tuning del modelo mientras solo modifica unos pocos cientos de parámetros por tarea.
-
-Estos prompts suaves son vectores continuos en el espacio de incrustación del modelo que se optimizan durante el entrenamiento. A diferencia de los prompts discretos tradicionales que usan tokens de lenguaje natural, los prompts suaves no tienen un significado inherente, pero aprenden a provocar el comportamiento deseado del modelo congelado mediante el descenso de gradiente. La técnica es particularmente efectiva para escenarios de múltiples tareas, ya que cada tarea requiere almacenar solo un pequeño vector de prompt (normalmente unos pocos cientos de parámetros) en lugar de una copia completa del modelo. Este enfoque no solo mantiene una huella de memoria mínima, sino que también permite una adaptación rápida de tareas simplemente intercambiando los vectores de prompt sin necesidad de recargar el modelo.
-
-## Proceso de Entrenamiento
-
-Los prompts suaves suelen ser entre 8 y 32 tokens y se pueden inicializar de forma aleatoria o a partir de texto existente. El método de inicialización juega un papel crucial en el proceso de entrenamiento, con la inicialización basada en texto a menudo obteniendo mejores resultados que la aleatoria.
-
-Durante el entrenamiento, solo se actualizan los parámetros del prompt mientras que el modelo base permanece congelado. Este enfoque centrado utiliza objetivos de entrenamiento estándar, pero requiere una atención cuidadosa a la tasa de aprendizaje y el comportamiento de los gradientes de los tokens del prompt.
-
-## Implementación con PEFT
-
-La libreria PEFT facilita la implementación del ajuste de prompts. Aquí tienes un ejemplo básico:
-
-```python
-from peft import PromptTuningConfig, TaskType, get_peft_model
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-# Carga el modelo base
-model = AutoModelForCausalLM.from_pretrained("your-base-model")
-tokenizer = AutoTokenizer.from_pretrained("your-base-model")
-
-# Configura el ajuste de prompt
-peft_config = PromptTuningConfig(
-    task_type=TaskType.CAUSAL_LM,
-    num_virtual_tokens=8,  # Número de tokens entrenables
-    prompt_tuning_init="TEXT",  # Inicializa a partir de texto
-    prompt_tuning_init_text="Clasifique si este texto es positivo o negativo:",
-    tokenizer_name_or_path="your-base-model",
-)
-
-# Crea el modelo ajustable por prompt
-model = get_peft_model(model, peft_config)
-```
-
-## Comparación con Otros Métodos
-
-Cuando se compara con otros enfoques de PEFT, el ajuste de prompts destaca por su eficiencia. Mientras que LoRA ofrece bajos recuentos de parámetros y uso de memoria pero requiere cargar adaptadores para el cambio de tareas, el ajuste de prompts logra un uso de recursos aún menor y permite un cambio de tareas inmediato. El fine-tuning completo, en contraste, exige recursos significativos y requiere copias separadas del modelo para diferentes tareas.
-
-| Método            | Parámetros  | Memoria  | Cambio de Tarea |
-|-------------------|-------------|----------|-----------------|
-| Ajuste de Prompts | Muy Bajo    | Mínimo   | Fácil           |
-| LoRA              | Bajo        | Bajo     | Requiere Carga  |
-| Fine-tuning Completo   | Alto        | Alto     | Nueva Copia     |
-
-Al implementar el ajuste de prompts, comienza con un pequeño número de tokens virtuales (8-16) y aumenta solo si la complejidad de la tarea lo requiere. La inicialización con texto generalmente da mejores resultados que la aleatoria, especialmente cuando se utiliza texto relevante para la tarea. La estrategia de inicialización debe reflejar la complejidad de la tarea objetivo.
-
-El entrenamiento requiere consideraciones ligeramente diferentes al fine-tuning completo. Las tasas de aprendizaje más altas suelen funcionar bien, pero es esencial monitorear cuidadosamente los gradientes de los tokens de prompt. La validación regular con ejemplos diversos ayuda a garantizar un rendimiento robusto en diferentes escenarios.
-
-## Aplicaciones
-
-El ajuste de prompts sobresale en varios escenarios:
-
-1. Despliegue de múltiples tareas
-2. Entornos con limitaciones de recursos
-3. Adaptación rápida de tareas
-4. Aplicaciones sensibles a la privacidad
-
-A medida que los modelos se hacen más pequeños, el ajuste de prompts se vuelve menos competitivo en comparación con el fine-tuning completo. Por ejemplo, en modelos como SmolLM2, el ajuste de prompts es menos relevante que el fine-tuning completo.
-
-## Próximos Pasos
-
-⏭️ Pasa al [Tutorial de Adaptadores LoRA](./notebooks/finetune_sft_peft.ipynb) para aprender cómo ajustar un modelo con adaptadores LoRA.
-
-## Recursos
-- [Documentación de PEFT](https://huggingface.co/docs/peft)
-- [Papel sobre Ajuste de Prompts](https://arxiv.org/abs/2104.08691)
-- [Recetario de Hugging Face](https://huggingface.co/learn/cookbook/prompt_tuning_peft)
\ No newline at end of file
diff --git a/es/4_evaluation/README.md b/es/4_evaluation/README.md
deleted file mode 100644
index 37e2c58c..00000000
--- a/es/4_evaluation/README.md
+++ /dev/null
@@ -1,36 +0,0 @@
-# Evaluación
-
-La evaluación es un paso crítico en el desarrollo y despliegue de modelos de lenguaje. Nos permite entender qué tan bien funcionan nuestros modelos en diferentes capacidades e identificar áreas de mejora. Este módulo cubre tanto los "becnhmarks" estándares como los enfoques de evaluación específicos para evaluar de manera integral tu modelo **smol**.
-
-Usaremos [`lighteval`](https://github.com/huggingface/lighteval), una poderosa biblioteca de evaluación desarrollada por Hugging Face que se integra perfectamente con el ecosistema de Hugging Face. Para una explicación más detallada sobre los conceptos y mejores prácticas de evaluación, consulta la [guía de evaluación](https://github.com/huggingface/evaluation-guidebook).
-
-## Descripción del Módulo
-
-Una estrategia de evaluación completa examina múltiples aspectos del rendimiento del modelo. Evaluamos capacidades específicas en tareas como responder preguntas y resumir textos, para entender cómo el modelo maneja diferentes tipos de problemas. Medimos la calidad del "output" mediante factores como coherencia y precisión. A su vez, la evaluación de seguridad ayuda a identificar posibles "outputs" dañinas o sesgos. Finalmente, las pruebas de experticia en áreas especfícios verifican el conocimiento especializado del modelo en tu campo objetivo.
-
-## Contenidos
-
-### 1️⃣ [Evaluaciones Automáticas](./automatic_benchmarks.md)
-Aprende a evaluar tu modelo utilizando "benchmarks" y métricas estandarizadas. Exploraremos "benchmarks" comunes como MMLU y TruthfulQA, entenderemos las métricas clave de evaluación y configuraciones, y cubriremos mejores prácticas para una evaluación reproducible.
-
-### 2️⃣ [Evaluación Personalizada en un Dominio](./custom_evaluation.md)
-Descubre cómo crear flujos de evaluación adaptados a tus casos de uso específicos. Te guiaremos en el diseño de tareas de evaluación personalizadas, la implementación de métricas especializadas y la construcción de conjuntos de datos de evaluación que se ajusten a tus necesidades.
-
-### 3️⃣ [Proyecto de Evaluación en un Dominio](./project/README.md)
-Sigue un ejemplo completo de cómo construir un flujo de evaluación específico para un dominio. Aprenderás a generar conjuntos de datos de evaluación, usar Argilla para la anotación de datos, crear conjuntos de datos estandarizados y evaluar modelos utilizando LightEval.
-
-### Cuadernos de Ejercicios
-
-| Título | Descripción | Ejercicio | Enlace | Colab |
-|--------|-------------|----------|-------|-------|
-| Evalúa y analiza tu LLM | Aprende a usar LightEval para evaluar y comparar modelos en dominios específicos | 🐢 Usa tareas del dominio médico para evaluar un modelo <br> 🐕 Crea una evaluación de dominio con diferentes tareas MMLU <br> 🦁 Diseña una tarea de evaluación personalizada para tu dominio | [Cuaderno](./notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-
-## Recursos
-
-- [Guía de Evaluación](https://github.com/huggingface/evaluation-guidebook) - Guía completa para la evaluación de modelos de lenguaje
-- [Documentación de LightEval](https://github.com/huggingface/lighteval) - Documentación oficial de la biblioteca LightEval
-- [Documentación de Argilla](https://docs.argilla.io) - Aprende sobre la plataforma de anotación Argilla
-- [Paper de MMLU](https://arxiv.org/abs/2009.03300) - Artículo sobre el benchmark MMLU
-- [Crear una Tarea Personalizada](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
-- [Crear una Métrica Personalizada](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
-- [Usar métricas existentes](https://github.com/huggingface/lighteval/wiki/Metric-List)
diff --git a/es/4_evaluation/automatic_benchmarks.md b/es/4_evaluation/automatic_benchmarks.md
deleted file mode 100644
index b85e460a..00000000
--- a/es/4_evaluation/automatic_benchmarks.md
+++ /dev/null
@@ -1,136 +0,0 @@
-# Benchmarks Automáticos
-
-Los "benchmarks" automáticos sirven como herramientas estandarizadas para evaluar modelos de lenguaje en diferentes tareas y capacidades. Aunque proporcionan un punto de partida útil para entender el rendimiento de un modelo, es importante reconocer que representan solo una parte de una estrategia de evaluación integral.
-
-## Entendiendo los Benchmarks Automáticos
-
-Los "benchmarks" automáticos generalmente consisten en conjuntos de datos curados con tareas y métricas de evaluación predefinidas. Estos "benchmarks" buscan evaluar diversos aspectos de la capacidad del modelo, desde la comprensión básica del lenguaje hasta el razonamiento complejo. La principal ventaja de usar "benchmarks" automáticos es su estandarización: permiten comparaciones consistentes entre diferentes modelos y proporcionan resultados reproducibles.
-
-Sin embargo, es crucial entender que el rendimiento en un "benchmark" no siempre se traduce directamente en efectividad en el mundo real. Un modelo que sobresale en "benchmarks" académicos puede tener dificultades en aplicaciones específicas de dominio o casos de uso prácticos.
-
-## Benchmarks y Sus Limitaciones
-
-### Benchmarks de Conocimientos Generales
-
-MMLU (Massive Multitask Language Understanding) evalúa conocimientos en 57 materias, desde ciencias hasta humanidades. Aunque es extenso, puede no reflejar la profundidad de experiencia necesaria para dominios específicos. TruthfulQA evalúa la tendencia de un modelo a reproducir conceptos erróneos comunes, aunque no puede capturar todas las formas de desinformación.
-
-### Benchmarks de Razonamiento
-
-BBH (Big Bench Hard) y GSM8K se enfocan en tareas de razonamiento complejo. BBH evalúa el pensamiento lógico y la planificación, mientras que GSM8K se centra específicamente en la resolución de problemas matemáticos. Estos "benchmarks" ayudan a evaluar capacidades analíticas pero pueden no capturar el razonamiento matizado requerido en escenarios del mundo real.
-
-### Comprensión del Lenguaje
-
-HELM proporciona un marco de evaluación holístico, mientras que WinoGrande prueba el sentido común mediante la desambiguación de pronombres. Estos "benchmarks" ofrecen información sobre las capacidades de procesamiento del lenguaje, pero pueden no representar completamente la complejidad de las conversaciones naturales o la terminología específica de un dominio.
-
-## Enfoques Alternativos de Evaluación
-
-Muchas organizaciones han desarrollado métodos de evaluación alternativos para abordar las limitaciones de los "benchmarks" estándar:
-
-### Model de lenguaje como Juez (LLM-as-Judge)
-
-Usar un modelo de lenguaje para evaluar los "outputs" de otro modelo se ha vuelto cada vez más popular. Este enfoque puede proporcionar retroalimentación más matizada que las métricas tradicionales, aunque viene con sus propios sesgos y limitaciones.
-
-### Arenas de Evaluación (Evaluation Arenas)
-
-Plataformas como Chatbot Arena permiten que los modelos interactúen y se evalúen entre sí en entornos controlados. Esto puede revelar fortalezas y debilidades que podrían no ser evidentes en "benchmarks" tradicionales.
-
-### Grupos de Benchmarks Personalizados
-
-Las organizaciones a menudo desarrollan conjuntos de "benchmarks" internos adaptados a sus necesidades específicas y casos de uso. Estos pueden incluir pruebas de conocimientos específicos de dominio o escenarios de evaluación que reflejen las condiciones reales de despliegue.
-
-## Creando Tu Propia Estrategia de Evaluación
-
-Recuerda que aunque LightEval facilita ejecutar "benchmarks" estándar, también deberías invertir tiempo en desarrollar métodos de evaluación específicos para tu caso de uso.
-
-Aunque los "benchmarks" estándares proporcionan una línea base útil, no deberían ser tu único método de evaluación. Así es como puedes desarrollar un enfoque más integral:
-
-1. Comienza con "benchmarks" estándares relevantes para establecer una línea base y permitir comparaciones con otros modelos.
-
-2. Identifica los requisitos y desafíos específicos de tu caso de uso. ¿Qué tareas realizará tu modelo realmente? ¿Qué tipos de errores serían más problemáticos?
-
-3. Desarrolla conjuntos de datos de evaluación personalizados que reflejen tu caso de uso real. Esto podría incluir:
-   - Consultas reales de usuarios en tu dominio
-   - Casos límite comunes que hayas encontrado
-   - Ejemplos de escenarios particularmente desafiantes
-
-4. Considera implementar una estrategia de evaluación por capas:
-   - Métricas automatizadas para retroalimentación rápida
-   - Evaluación humana para una comprensión más matizada
-   - Revisión por expertos en el dominio para aplicaciones especializadas
-   - Pruebas A/B en entornos controlados
-
-## Usando LightEval para "Benchmarking"
-
-Las tareas de LightEval se definen usando un formato específico:
-``` 
-{suite}|{task}|{num_few_shot}|{auto_reduce} 
-```
-
-- **suite**: El conjunto de "benchmarks" (por ejemplo, 'mmlu', 'truthfulqa')
-- **task**: Tarea específica dentro del conjunto (por ejemplo, 'abstract_algebra')
-- **num_few_shot**: Número de ejemplos a incluir en el "prompt" (0 para zero-shot)
-- **auto_reduce**: Si se debe reducir automáticamente los ejemplos "few-shot" si el "prompt" es demasiado largo (0 o 1)
-
-Ejemplo: `"mmlu|abstract_algebra|0|0"` evalúa la tarea de álgebra abstracta de MMLU sin ejempls en el "prompt" (i.e. "zero-shot").
-
-### Ejemplo de Pipeline de Evaluación
-
-Aquí tienes un ejemplo completo de configuración y ejecución de una evaluación utilizando "benchmarks" automáticos relevantes para un dominio específico:
-
-```python
-from lighteval.tasks import Task, Pipeline
-from transformers import AutoModelForCausalLM
-
-# Definir tareas para evaluar
-domain_tasks = [
-    "mmlu|anatomy|0|0",
-    "mmlu|high_school_biology|0|0", 
-    "mmlu|high_school_chemistry|0|0",
-    "mmlu|professional_medicine|0|0"
-]
-
-# Configurar parámetros del pipeline
-pipeline_params = {
-    "max_samples": 40,  # Número de muestras para evaluar
-    "batch_size": 1,    # Tamaño del "batch" para la inferencia
-    "num_workers": 4    # Número de procesos paralelos
-}
-
-# Crear tracker de evaluación
-evaluation_tracker = EvaluationTracker(
-    output_path="./results",
-    save_generations=True
-)
-
-# Cargar modelo y crear pipeline
-model = AutoModelForCausalLM.from_pretrained("your-model-name")
-pipeline = Pipeline(
-    tasks=domain_tasks,
-    pipeline_parameters=pipeline_params,
-    evaluation_tracker=evaluation_tracker,
-    model=model
-)
-
-# Ejecutar evaluación
-pipeline.evaluate()
-
-# Obtener y mostrar resultados
-results = pipeline.get_results()
-pipeline.show_results()
-```
-
-Los resultados se muestran en formato tabular:
-``` 
-|                  Task                  |Version|Metric|Value |   |Stderr|
-|----------------------------------------|------:|------|-----:|---|-----:|
-|all                                     |       |acc   |0.3333|±  |0.1169|
-|leaderboard:mmlu:_average:5             |       |acc   |0.3400|±  |0.1121|
-|leaderboard:mmlu:anatomy:5              |      0|acc   |0.4500|±  |0.1141|
-|leaderboard:mmlu:high_school_biology:5  |      0|acc   |0.1500|±  |0.0819|
-```
-
-También puedes manejar los resultados en un DataFrame de pandas y visualizarlos o representarlos como prefieras.
-
-# Próximos Pasos
-
-⏩ Explora [Evaluación Personalizada en un Dominio](./custom_evaluation.md) para aprender a crear flujos de evaluación adaptados a tus necesidades específicas.
diff --git a/es/4_evaluation/custom_evaluation.md b/es/4_evaluation/custom_evaluation.md
deleted file mode 100644
index b8d765ce..00000000
--- a/es/4_evaluation/custom_evaluation.md
+++ /dev/null
@@ -1,132 +0,0 @@
-# Evaluación Personalizada en un Dominio
-
-Aunque los "benchmarks" estándares proporcionan conocimiento relevante, muchas aplicaciones requieren enfoques de evaluación especializados que se adapten a dominios específicos o a casos de uso particulares. Esta guía te ayudará a crear flujos de evaluación personalizados que evalúen con precisión el rendimiento de tu modelo en tu dominio objetivo.
-
-## Diseñando Tu Estrategia de Evaluación
-
-Una estrategia de evaluación personalizada exitosa comienza con objetivos claros. Es fundamental considerar qué capacidades específicas necesita demostrar tu modelo en tu dominio. Esto podría incluir conocimientos técnicos, patrones de razonamiento o formatos específicos del dominio. Documenta estos requisitos cuidadosamente; ellos guiarán tanto el diseño de tus tareas como la selección de métricas.
-
-Tu evaluación debe probar tanto casos de uso estándar como casos límite. Por ejemplo, en un dominio médico, podrías evaluar tanto escenarios comunes de diagnóstico como condiciones raras. En aplicaciones financieras, podrías probar tanto transacciones rutinarias como casos complejos que involucren múltiples monedas o condiciones especiales.
-
-## Implementación con LightEval
-
-LightEval proporciona un marco flexible para implementar evaluaciones personalizadas. Así es como puedes crear una tarea personalizada:
-
-```python
-from lighteval.tasks import Task, Doc
-from lighteval.metrics import SampleLevelMetric, MetricCategory, MetricUseCase
-
-class CustomEvalTask(Task):
-    def __init__(self):
-        super().__init__(
-            name="custom_task",
-            version="0.0.1",
-            metrics=["accuracy", "f1"],  # Tus métricas elegidas
-            description="Descripción de tu tarea de evaluación personalizada"
-        )
-    
-    def get_prompt(self, sample):
-        # Formatea tu entrada en un "prompt"
-        return f"Question: {sample['question']}\nAnswer:"
-    
-    def process_response(self, response, ref):
-        # Procesa el "output" del modelo y compáralo con la referencia
-        return response.strip() == ref.strip()
-```
-
-## Métricas Personalizadas
-
-Las tareas específicas de dominio a menudo requieren métricas especializadas. LightEval proporciona un marco flexible para crear métricas personalizadas que capturen aspectos relevantes del rendimiento del dominio:
-
-```python
-from aenum import extend_enum
-from lighteval.metrics import Metrics, SampleLevelMetric, SampleLevelMetricGrouping
-import numpy as np
-
-# Definir una función de métrica a nivel de muestra
-def custom_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> dict:
-    """Ejemplo de métrica que genera múltiples puntuaciones por muestra"""
-    response = predictions[0]
-    return {
-        "accuracy": response == formatted_doc.choices[formatted_doc.gold_index],
-        "length_match": len(response) == len(formatted_doc.reference)
-    }
-
-# Crear una métrica que genere múltiples valores por muestra
-custom_metric_group = SampleLevelMetricGrouping(
-    metric_name=["accuracy", "length_match"],  # Nombres de submétricas
-    higher_is_better={  # define si valores más altos son mejores para cada métrica
-        "accuracy": True,
-        "length_match": True
-    },
-    category=MetricCategory.CUSTOM,
-    use_case=MetricUseCase.SCORING,
-    sample_level_fn=custom_metric,
-    corpus_level_fn={  # define cómo agregar cada métrica
-        "accuracy": np.mean,
-        "length_match": np.mean
-    }
-)
-
-# Registrar la métrica en LightEval
-extend_enum(Metrics, "custom_metric_name", custom_metric_group)
-```
-
-Para casos más simples donde solo necesitas un valor por muestra:
-
-```python
-def simple_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> bool:
-    """Ejemplo de métrica que genera una única puntuación por muestra"""
-    response = predictions[0]
-    return response == formatted_doc.choices[formatted_doc.gold_index]
-
-simple_metric_obj = SampleLevelMetric(
-    metric_name="simple_accuracy",
-    higher_is_better=True,
-    category=MetricCategory.CUSTOM,
-    use_case=MetricUseCase.SCORING,
-    sample_level_fn=simple_metric,
-    corpus_level_fn=np.mean  # define cómo agregar resultados entre muestras
-)
-
-extend_enum(Metrics, "simple_metric", simple_metric_obj)
-```
-
-Una vez definas tus métricas personalizadas, puedes usarlas luego en tus tareas de evaluación haciendo referencia a ellas en la configuración de la tarea. Las métricas se calcularán automáticamente en todas las muestras y se agregarán según las funciones que especifiques.
-
-Para métricas más complejas, considera:
-- Usar metadatos en tus documentos formateados para ponderar o ajustar puntuaciones
-- Implementar funciones de agregación personalizadas para estadísticas a nivel de corpus
-- Agregar verificaciones de validación para las entradas de tus métricas
-- Documentar casos límite y comportamientos esperados
-
-Para un ejemplo completo de métricas personalizadas en acción, consulta nuestro [proyecto de evaluación de dominio](./project/README.md).
-
-## Creación de Conjuntos de Datos
-
-La evaluación de alta calidad requiere conjuntos de datos cuidadosamente curados. Considera estos enfoques para la creación de conjuntos de datos:
-
-1. **Anotación por Expertos**: Trabaja con expertos del dominio para crear y validar ejemplos de evaluación. Herramientas como [Argilla](https://github.com/argilla-io/argilla) hacen este proceso más eficiente.
-
-2. **Datos del Mundo Real**: Recopila y anonimiza datos de uso real, asegurándote de que representen escenarios reales de despliegue del modelo.
-
-3. **Generación Sintética**: Usa LLMs para generar ejemplos iniciales y luego permite que expertos los validen y refinen.
-
-## Mejores Prácticas
-
-- Documenta tu metodología de evaluación a fondo, incluidas los supuestos o limitaciones
-- Incluye casos de prueba diversos que cubran diferentes aspectos de tu dominio
-- Considera tanto métricas automatizadas como evaluaciones humanas donde sea apropiado
-- Controla las versiones de tus conjuntos de datos y código de evaluación
-- Actualiza regularmente tu conjunto de evaluaciones a medida que descubras nuevos casos límite o requisitos
-
-## Referencias
-
-- [Guía de Tareas Personalizadas en LightEval](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
-- [Métricas Personalizadas en LightEval](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
-- [Documentación de Argilla](https://docs.argilla.io) para anotación de conjuntos de datos
-- [Guía de Evaluación](https://github.com/huggingface/evaluation-guidebook) para principios generales de evaluación
-
-# Próximos Pasos
-
-⏩ Para un ejemplo completo de cómo implementar estos conceptos, consulta nuestro [proyecto de evaluación de dominio](./project/README.md).
diff --git a/es/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb b/es/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
deleted file mode 100644
index 060c3262..00000000
--- a/es/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
+++ /dev/null
@@ -1,19409 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# lighteval es tu biblioteca de evaluación de IA\n",
-        "\n",
-        "Este cuaderno explora cómo puedes usar lighteval para evaluar y comparar modelos de lenguaje grandes (LLMs).\n",
-        "\n",
-        "`lighteval` ha estado disponible por un tiempo y es una gran herramienta para obtener puntuaciones de evaluación en los principales \"benchmarks\". Recientemente ha sido refactorizado para poder usarse como una biblioteca en Python, lo que la hace excelente para comparar modelos en diferentes \"benchmarks\".\n",
-        "\n",
-        "Así que exploremos algunas puntuaciones de evaluación.\n",
-        "\n",
-        "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-        "    <h2 style='margin: 0;color:blue'>Ejercicio: Evalúa tu propio modelo</h2>\n",
-        "    <p>Ahora que has visto cómo evaluar modelos en dominios específicos, intenta evaluar un modelo en un dominio que te interese.</p>\n",
-        "    <p><b>Niveles de dificultad</b></p>\n",
-        "    <p>🐢 Usa las tareas existentes del dominio médico pero evalúa un modelo diferente del repositorio de Hugging Face</p>\n",
-        "    <p>🐕 Crea una nueva evaluación de dominio seleccionando diferentes tareas de MMLU (por ejemplo, ciencias de la computación, matemáticas, física)</p>\n",
-        "    <p>🦁 Crea una tarea de evaluación personalizada utilizando el marco de tareas de LightEval y evalúa modelos en tu dominio específico</p>\n",
-        "</div>\n",
-        "\n",
-        "## Instalar dependencias"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "afW_CLJoPCnF",
-        "outputId": "91611cd5-51d6-4f73-9b3d-ac79c918d074"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install -qqq -U \"torch<2.5\" \"torchvision<2.5\" \"torchaudio<2.5\" --index-url https://download.pytorch.org/whl/cu121\n",
-        "!pip list | grep torch"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "8xMnn_cQ1EEi",
-        "outputId": "bcaf8498-19c3-46af-9ae1-0306326f006b"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install -qqq git+https://github.com/huggingface/lighteval.git tiktoken"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n",
-        "## Configurar la evaluación con `lighteval`\n",
-        "\n",
-        "Necesitamos configurar el entorno de evaluación y el pipeline. Muchas de estas configuraciones estarán desactivadas porque trabajaremos directamente en el notebook, pero también podríamos usar `push_to_hub` o `push_to_tensorboard`.\n",
-        "\n",
-        "### `push_to_hub`\n",
-        "\n",
-        "Esto es útil si estamos evaluando un modelo y queremos guardar su evaluación junto con los pesos y la configuración en el repositorio de Hugging Face.\n",
-        "\n",
-        "### `push_to_tensorboard`\n",
-        "\n",
-        "Esto sería útil si estamos construyendo una herramienta o script de evaluación y queremos visualizar los resultados en tensorboard.\n"
-      ]
-    },
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-        },
-        "id": "3cUebd-z6IWs",
-        "outputId": "4b5ef2ce-04a0-4b2a-f3d7-5ecb7afb662e"
-      },
-      "outputs": [],
-      "source": [
-        "import lighteval\n",
-        "import os\n",
-        "from datetime import timedelta\n",
-        "from transformers import AutoModelForCausalLM\n",
-        "\n",
-        "from lighteval.logging.evaluation_tracker import EvaluationTracker\n",
-        "from lighteval.logging.hierarchical_logger import hlog_warn, htrack\n",
-        "from lighteval.models.model_config import create_model_config\n",
-        "from lighteval.pipeline import EnvConfig, ParallelismManager, Pipeline, PipelineParameters\n",
-        "\n",
-        "TOKEN = os.getenv(\"HF_TOKEN\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "muikmXNQXgFv"
-      },
-      "outputs": [],
-      "source": [
-        "env_config = EnvConfig(token=TOKEN, cache_dir=\"~/tmp\")\n",
-        "\n",
-        "evaluation_tracker = EvaluationTracker(\n",
-        "    output_dir=\"~/tmp\",\n",
-        "    save_details=False,\n",
-        "    push_to_hub=False,\n",
-        "    push_to_tensorboard=False,\n",
-        "    public=False,\n",
-        "    hub_results_org=False,\n",
-        ")\n",
-        "\n",
-        "pipeline_params = PipelineParameters(\n",
-        "    launcher_type=ParallelismManager.ACCELERATE,\n",
-        "    env_config=env_config,\n",
-        "    job_id=1,\n",
-        "    override_batch_size=1,\n",
-        "    num_fewshot_seeds=0,\n",
-        "    max_samples=10,\n",
-        "    use_chat_template=False,\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Comparar modelos con `lighteval`\n",
-        "\n",
-        "Vamos a comparar dos modelos pequeños en un dominio específico. Usaremos `Qwen2.5-0.5B` y `SmolLM2-360M-Instruct`, y los evaluaremos en un dominio médico.\n",
-        "\n",
-        "Podemos crear una evaluación de dominio a partir de un subconjunto de evaluaciones de MMLU definiendo las tareas de evaluación. En lighteval, las tareas se describen como cadenas.\n",
-        "\n",
-        "`{suite}|{task}:{subtask}|{num_few_shot}|{0 o 1 para reducir num_few_shot si el \"prompt\" es demasiado largo}`\n",
-        "\n",
-        "Por lo tanto, pasaremos nuestra lista de tareas relacionadas con medicina de esta forma:\n",
-        "\n",
-        "```\n",
-        "\"leaderboard|mmlu:anatomy|5|0,leaderboard|mmlu:professional_medicine|5|0,leaderboard|mmlu:high_school_biology|5|0,leaderboard|mmlu:high_school_chemistry|5|0\"\n",
-        "```\n",
-        "\n",
-        "Que se traduce a:\n",
-        "\n",
-        "| Suite | Tarea | Ejemplos Fewshot | Limitar Fewshots |\n",
-        "|---|---|---|---|\n",
-        "| leaderboard | mmlu:anatomy | 5 | Falso |\n",
-        "| leaderboard | mmlu:professional_medicine | 5 | Falso |\n",
-        "| leaderboard | mmlu:high_school_biology | 5 | Falso |\n",
-        "| leaderboard | mmlu:high_school_chemistry | 5 | Falso |\n",
-        "\n",
-        "Para obtener una lista completa de las tareas soportadas por lighteval, consulta esta página en [la documentación](https://github.com/huggingface/lighteval/wiki/Available-Tasks).\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "qTqsizv9mVbO"
-      },
-      "outputs": [],
-      "source": [
-        "domain_tasks = \"leaderboard|mmlu:anatomy|5|0,leaderboard|mmlu:professional_medicine|5|0,leaderboard|mmlu:high_school_biology|5|0,leaderboard|mmlu:high_school_chemistry|5|0\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "XwcJklSFX4H6"
-      },
-      "source": [
-        "# Evaluación Qwen2.5 0.5B"
-      ]
-    },
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-          ]
-        },
-        "id": "PXJuaXVxUNBO",
-        "outputId": "87aca361-5bd5-4b12-9097-3e97f1165d7b"
-      },
-      "outputs": [],
-      "source": [
-        "qwen_model = AutoModelForCausalLM.from_pretrained(\"Qwen/Qwen2.5-0.5B\")\n",
-        "\n",
-        "pipeline = Pipeline(\n",
-        "    tasks=domain_tasks,\n",
-        "    pipeline_parameters=pipeline_params,\n",
-        "    evaluation_tracker=evaluation_tracker,\n",
-        "    model=qwen_model\n",
-        ")\n",
-        "\n",
-        "pipeline.evaluate()\n",
-        "\n",
-        "qwen_results = pipeline.get_results()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "CIwCaCxJX_hA"
-      },
-      "source": [
-        "# Evaluación SmolLM 360M"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000,
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-        "id": "Dxg0RtlNVT4y",
-        "outputId": "ee40393c-bf62-499d-d443-e96da72411e8"
-      },
-      "outputs": [],
-      "source": [
-        "smol_model = AutoModelForCausalLM.from_pretrained(\"HuggingFaceTB/SmolLM2-360M-Instruct\")\n",
-        "\n",
-        "pipeline = Pipeline(\n",
-        "    tasks=domain_tasks,\n",
-        "    pipeline_parameters=pipeline_params,\n",
-        "    evaluation_tracker=evaluation_tracker,\n",
-        "    model=smol_model\n",
-        ")\n",
-        "\n",
-        "pipeline.evaluate()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "pdjyzfKHVt52"
-      },
-      "outputs": [],
-      "source": [
-        "smol_results = pipeline.get_results()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "eugvMFfgV1VD",
-        "outputId": "62956e3f-29c8-4493-f868-4ba63b7d8e96"
-      },
-      "outputs": [],
-      "source": [
-        "pipeline.show_results()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0HD8aFwSYGHu"
-      },
-      "source": [
-        "# Visualiza los Resultados\n",
-        "\n",
-        "Ahora que tenemos los resultados de los dos modelos, podemos visualizarlos lado a lado. Mantendremos la visualización simple, pero con esta estructura de datos podrías representar las puntuaciones de muchas maneras."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 447
-        },
-        "id": "1sReqrgQUO9r",
-        "outputId": "9a0779e1-c278-48ab-9ecc-5055028ea3fd"
-      },
-      "outputs": [],
-      "source": [
-        "import pandas as pd\n",
-        "\n",
-        "df = pd.DataFrame.from_records(smol_results[\"results\"]).T[\"acc\"].rename(\"SmolLM2-360M-Instruct\")\n",
-        "_df = pd.DataFrame.from_records(qwen_results[\"results\"]).T[\"acc\"].rename(\"Qwen2-0.5B-DPO\")\n",
-        "df = pd.concat([df, _df], axis=1)\n",
-        "df.plot(kind=\"barh\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 💐 ¡Eso es todo!\n",
-        "\n",
-        "Tienes un cuaderno práctico para visualizar las evaluaciones de modelos. Puedes usarlo para:\n",
-        "\n",
-        "- seleccionar el modelo adecuado para tu caso de uso\n",
-        "- evaluar \"checkpoints\" durante el entrenamiento\n",
-        "- compartir puntuaciones de modelos\n",
-        "\n",
-        "🏃 Próximos pasos\n",
-        "\n",
-        "- Si quieres profundizar en tus resultados de evaluación, consulta este [cuaderno](https://github.com/huggingface/evaluation-guidebook/blob/main/contents/examples/comparing_task_formulations.ipynb)."
-      ]
-    }
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diff --git a/es/4_evaluation/project/README.md b/es/4_evaluation/project/README.md
deleted file mode 100644
index 70f9a9b2..00000000
--- a/es/4_evaluation/project/README.md
+++ /dev/null
@@ -1,83 +0,0 @@
-# Evaluación Específica en un Dominio con Argilla, Distilabel y LightEval
-
-La mayoría de los "benchmarks" populares evalúan capacidades muy generales (razonamiento, matemáticas, código), pero ¿alguna vez has necesitado estudiar capacidades más específicas?
-
-¿Qué deberías hacer si necesitas evaluar un modelo en un **dominio específico** relevante para tus casos de uso? (Por ejemplo, aplicaciones financieras, legales o médicas).
-
-Este tutorial muestra todo el flujo de trabajo que puedes seguir, desde la creación de datos relevantes y la anotación de tus muestras hasta la evaluación de tu modelo, utilizando las herramientas de [Argilla](https://github.com/argilla-io/argilla), [distilabel](https://github.com/argilla-io/distilabel) y [lighteval](https://github.com/huggingface/lighteval). Para nuestro ejemplo, nos centraremos en generar preguntas de exámenes a partir de múltiples documentos.
-
-## Estructura del Proyecto
-
-Seguiremos 4 pasos, con un script para cada uno: generar un conjunto de datos, anotarlo, extraer muestras relevantes para la evaluación y, finalmente, evaluar los modelos.
-
-| Nombre del Script       | Descripción                                                                 |
-|-------------------------|-----------------------------------------------------------------------------|
-| generate_dataset.py     | Genera preguntas de exámenes a partir de múltiples documentos de texto usando un modelo de lenguaje especificado. |
-| annotate_dataset.py     | Crea un conjunto de datos en Argilla para la anotación manual de las preguntas generadas. |
-| create_dataset.py       | Procesa los datos anotados desde Argilla y crea una base de datos de Hugging Face. |
-| evaluation_task.py      | Define una tarea personalizada en LightEval para evaluar modelos de lenguaje en el conjunto de preguntas de examen. |
-
-## Pasos
-
-### 1. Generar el Conjunto de Datos
-
-El script `generate_dataset.py` utiliza la biblioteca distilabel para generar preguntas de examen basadas en múltiples documentos de texto. Usa el modelo especificado (por defecto: Meta-Llama-3.1-8B-Instruct) para crear preguntas, respuestas correctas y respuestas incorrectas (llamadas "distractores"). Puedes agregar tus propios datos y también usar un modelo diferente.
-
-Para ejecutar la generación:
-
-```sh
-python generate_dataset.py --input_dir ubicacion/de/los/documentos --model_id id_del_modelo --output_path output_directory
-```
-
-Esto creará un [Distiset](https://distilabel.argilla.io/dev/sections/how_to_guides/advanced/distiset/) que contiene las preguntas de examen generadas para todos los documentos en el directorio de entrada.
-
-### 2. Anotar el Conjunto de Datos
-
-El script `annotate_dataset.py` toma las preguntas generadas y crea una base de datos en Argilla para su anotación. Este script configura la estructura de la base de datos y la completa con las preguntas y respuestas generadas, aleatorizando el orden de las respuestas para evitar sesgos. Una vez en Argilla, tú o un experto en el dominio pueden validar el conjunto de datos con las respuestas correctas.
-
-Verás respuestas sugeridas por el LLM en orden aleatorio, y podrás aprobar la respuesta correcta o seleccionar una diferente. La duración de este proceso dependerá de la escala de tu base de datos de evaluación, la complejidad de tus datos y la calidad de tu LLM. Por ejemplo, logramos crear 150 muestras en 1 hora en el dominio de aprendizaje por transferencia, utilizando Llama-3.1-70B-Instruct, principalmente aprobando respuestas correctas y descartando las incorrectas.
-
-Para ejecutar el proceso de anotación:
-
-```sh
-python annotate_dataset.py --dataset_path ubicacion/del/distiset --output_dataset_name nombre_de_los_datos_argilla
-```
-
-Esto creará un conjunto de datos en Argilla que puede ser utilizado para revisión y anotación manual.
-
-![argilla_dataset](./images/domain_eval_argilla_view.png)
-
-Si no estás usando Argilla, desplégalo localmente o en Spaces siguiendo esta [guía rápida](https://docs.argilla.io/latest/getting_started/quickstart/).
-
-### 3. Crear la Base de Datos
-
-El script `create_dataset.py` procesa los datos anotados desde Argilla y crea una base de datos en Hugging Face. El script incorpora tanto respuestas sugeridas como respuestas anotadas manualmente. El script creará una base de datos con la pregunta, posibles respuestas y el nombre de la columna para la respuesta correcta. Para crear la base de datos final:
-
-```sh
-huggingface_hub login
-python create_dataset.py --dataset_path nombre_de_los_datos_argilla --dataset_repo_id id_repo_hf
-```
-
-Esto enviará el conjunto de datos al Hugging Face Hub bajo el repositorio especificado. Puedes ver una base de datos de ejemplo en el hub [aquí](https://huggingface.co/datasets/burtenshaw/exam_questions/viewer/default/train). La vista previa de la base de datos se ve así:
-
-![hf_dataset](./images/domain_eval_dataset_viewer.png)
-
-### 4. Tarea de Evaluación
-
-El script `evaluation_task.py` define una tarea personalizada en LightEval para evaluar modelos de lenguaje en el conjunto de preguntas de examen. Incluye una función de "prompt", una métrica de "accuracy" personalizada y la configuración de la tarea.
-
-Para evaluar un modelo utilizando lighteval con la tarea personalizada de preguntas de examen:
-
-```sh
-lighteval accelerate \
-    --model_args "pretrained=HuggingFaceH4/zephyr-7b-beta" \
-    --tasks "community|exam_questions|0|0" \
-    --custom_tasks domain-eval/evaluation_task.py \
-    --output_dir "./evals"
-```
-
-Puedes encontrar guías detalladas en el wiki de lighteval sobre cada uno de estos pasos:
-
-- [Crear una Tarea Personalizada](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
-- [Crear una Métrica Personalizada](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
-- [Usar Métricas Existentes](https://github.com/huggingface/lighteval/wiki/Metric-List)
diff --git a/es/4_evaluation/project/annotate_dataset.py b/es/4_evaluation/project/annotate_dataset.py
deleted file mode 100644
index dadffc10..00000000
--- a/es/4_evaluation/project/annotate_dataset.py
+++ /dev/null
@@ -1,129 +0,0 @@
-import argparse
-import json
-from random import choices, sample
-
-import argilla as rg
-from distilabel.distiset import Distiset
-
-################################################################################
-# Script Parameters
-################################################################################
-
-parser = argparse.ArgumentParser(
-    description="Annotate exam questions dataset using Argilla."
-)
-parser.add_argument(
-    "--argilla_api_key",
-    type=str,
-    default="argilla.apikey",
-    help="API key for Argilla",
-)
-parser.add_argument(
-    "--argilla_api_url",
-    type=str,
-    default="http://localhost:6900",
-    help="API URL for Argilla",
-)
-parser.add_argument(
-    "--dataset_path",
-    type=str,
-    default="exam_questions",
-    help="Path to the exam questions dataset",
-)
-parser.add_argument(
-    "--dataset_config",
-    type=str,
-    default="default",
-    help="Configuration of the dataset",
-)
-parser.add_argument(
-    "--dataset_split",
-    type=str,
-    default="train",
-    help="Split of the dataset to use",
-)
-parser.add_argument(
-    "--output_dataset_name",
-    type=str,
-    default="exam_questions",
-    help="Name of the output Argilla dataset",
-)
-
-args = parser.parse_args()
-
-################################################################################
-# Create Argilla dataset with the feedback task for validation
-################################################################################
-
-client = rg.Argilla(api_key=args.argilla_api_key, api_url=args.argilla_api_url)
-
-if client.datasets(args.output_dataset_name):
-    print(f"Deleting existing dataset '{args.output_dataset_name}'")
-    client.datasets(args.output_dataset_name).delete()
-
-settings = rg.Settings(
-    fields=[
-        rg.TextField("question"),
-        rg.TextField("answer_a"),
-        rg.TextField("answer_b"),
-        rg.TextField("answer_c"),
-        rg.TextField("answer_d"),
-    ],
-    questions=[
-        rg.LabelQuestion(
-            name="correct_answer",
-            labels=["answer_a", "answer_b", "answer_c", "answer_d"],
-        ),
-        rg.TextQuestion(
-            name="improved_question",
-            description="Could you improve the question?",
-        ),
-        rg.TextQuestion(
-            name="improved_answer",
-            description="Could you improve the best answer?",
-        ),
-    ],
-)
-
-dataset = rg.Dataset(settings=settings, name=args.output_dataset_name)
-dataset.create()
-
-################################################################################
-# Load the Distiset and process and add records to Argilla dataset
-# We will validate that questions appear in random order to avoid bias
-# but we will show correct answers in the Argilla UI as suggestions.
-################################################################################
-
-distiset = Distiset.load_from_disk(args.dataset_path)
-answer_names = ["answer_a", "answer_b", "answer_c", "answer_d"]
-dataset_records = []
-
-for exam in distiset[args.dataset_config][args.dataset_split]:
-    exam_json = json.loads(exam["generation"])["exam"]
-
-    for question in exam_json:
-        answer = question["answer"]
-        distractors = question["distractors"]
-        distractors = choices(distractors, k=3)
-        answers = distractors + [answer]
-        answers = sample(answers, len(answers))
-        suggestion_idx = answers.index(answer)
-        fields = dict(zip(answer_names, answers))
-        fields["question"] = question["question"]
-
-        record = rg.Record(
-            fields=fields,
-            suggestions=[
-                rg.Suggestion(
-                    question_name="correct_answer",
-                    value=answer_names[suggestion_idx],
-                )
-            ],
-        )
-        dataset_records.append(record)
-
-dataset.records.log(dataset_records)
-
-print(
-    f"Dataset '{args.output_dataset_name}' has been created and populated in Argilla."
-)
diff --git a/es/4_evaluation/project/create_dataset.py b/es/4_evaluation/project/create_dataset.py
deleted file mode 100644
index 822e268e..00000000
--- a/es/4_evaluation/project/create_dataset.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import argparse
-
-import argilla as rg
-from datasets import Dataset
-
-################################################################################
-# Script Parameters
-################################################################################
-
-parser = argparse.ArgumentParser(
-    description="Create a Hugging Face dataset from annotated Argilla data."
-)
-parser.add_argument(
-    "--argilla_api_key",
-    type=str,
-    default="argilla.apikey",
-    help="API key for Argilla",
-)
-parser.add_argument(
-    "--argilla_api_url",
-    type=str,
-    default="http://localhost:6900",
-    help="API URL for Argilla",
-)
-parser.add_argument(
-    "--dataset_path",
-    type=str,
-    default="exam_questions",
-    help="Path to the Argilla dataset",
-)
-parser.add_argument(
-    "--dataset_repo_id",
-    type=str,
-    default="burtenshaw/exam_questions",
-    help="Hugging Face dataset repository ID",
-)
-
-args = parser.parse_args()
-
-################################################################################
-# Initialize Argilla client and load dataset
-################################################################################
-
-client = rg.Argilla(api_key=args.argilla_api_key, api_url=args.argilla_api_url)
-dataset = client.datasets(args.dataset_path)
-
-################################################################################
-# Process Argilla records
-################################################################################
-
-dataset_rows = []
-
-for record in dataset.records(with_suggestions=True, with_responses=True):
-    row = record.fields
-
-    if len(record.responses) == 0:
-        answer = record.suggestions["correct_answer"].value
-        row["correct_answer"] = answer
-    else:
-        for response in record.responses:
-            if response.question_name == "correct_answer":
-                row["correct_answer"] = response.value
-    dataset_rows.append(row)
-
-################################################################################
-# Create Hugging Face dataset and push to Hub
-################################################################################
-
-hf_dataset = Dataset.from_list(dataset_rows)
-hf_dataset.push_to_hub(repo_id=args.dataset_repo_id)
-
-print(f"Dataset has been successfully pushed to {args.dataset_repo_id}")
diff --git a/es/4_evaluation/project/evaluation_task.py b/es/4_evaluation/project/evaluation_task.py
deleted file mode 100644
index 397c75ad..00000000
--- a/es/4_evaluation/project/evaluation_task.py
+++ /dev/null
@@ -1,86 +0,0 @@
-import numpy as np
-
-from lighteval.tasks.lighteval_task import LightevalTaskConfig
-from lighteval.tasks.requests import Doc
-from lighteval.metrics.utils.metric_utils import (
-    SampleLevelMetric,
-    MetricCategory,
-    MetricUseCase,
-)
-
-################################################################################
-# Define the prompt function based on the structure of the dataset
-################################################################################
-
-
-def prompt_fn(line, task_name: str = None):
-    """Converts a dataset line to a Doc object for evaluation."""
-    instruction = "Choose the correct answer for the following exam question:"
-    return Doc(
-        task_name=task_name,
-        query=f"{instruction} {line['question']}",
-        choices=[
-            f" {line['answer_a']}",
-            f" {line['answer_b']}",
-            f" {line['answer_c']}",
-            f" {line['answer_d']}",
-        ],
-        gold_index=["answer_a", "answer_b", "answer_c", "answer_d"].index(
-            line["correct_answer"]
-        ),
-        instruction=instruction,
-    )
-
-
-################################################################################
-# Define the custom metric based on guide here https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric
-# Or use an existing metric based on the guide here: https://github.com/huggingface/lighteval/wiki/Metric-List
-# Existing metrics can be imported from lighteval.metrics.metrics
-################################################################################
-
-
-def sample_level_fn(formatted_doc: Doc, **kwargs) -> bool:
-    response = np.argmin(kwargs["choices_logprob"])
-    return response == formatted_doc.gold_index
-
-
-custom_metric = SampleLevelMetric(
-    metric_name="exam_question_accuracy",
-    higher_is_better=True,
-    category=MetricCategory.MULTICHOICE,
-    use_case=MetricUseCase.NONE,
-    sample_level_fn=sample_level_fn,
-    corpus_level_fn=np.mean,
-)
-
-################################################################################
-# Define the task based on the prompt function and the custom metric
-# Based on the guide here: https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task
-################################################################################
-
-task = LightevalTaskConfig(
-    name="example",
-    prompt_function=prompt_fn,
-    suite=["community"],
-    hf_repo="burtenshaw/exam_questions",
-    hf_subset="default",
-    hf_avail_splits=["train"],
-    evaluation_splits=["train"],
-    few_shots_split=None,
-    few_shots_select=None,
-    metric=[custom_metric],
-)
-
-# Add the task to TASKS_TABLE
-TASKS_TABLE = [task]
-
-# MODULE LOGIC
-if __name__ == "__main__":
-    print([t.name for t in TASKS_TABLE])
-    print(len(TASKS_TABLE))
-
-# lighteval accelerate \
-# "pretrained=HuggingFaceTB/SmolLM2-135M-Instruct" \
-# "community|example|0|0" \
-# --custom-tasks "submitted_tasks/example.py" \
-# --output-dir "results"
diff --git a/es/4_evaluation/project/generate_dataset.py b/es/4_evaluation/project/generate_dataset.py
deleted file mode 100644
index cb531d3f..00000000
--- a/es/4_evaluation/project/generate_dataset.py
+++ /dev/null
@@ -1,167 +0,0 @@
-import argparse
-import os
-from pydantic import BaseModel, Field
-from datasets import Dataset
-
-from distilabel.llms import InferenceEndpointsLLM
-from distilabel.pipeline import Pipeline
-from distilabel.steps.tasks import TextGeneration
-
-
-################################################################################
-# Script Parameters
-################################################################################
-
-parser = argparse.ArgumentParser(
-    description="Generate exam questions from text files in a directory."
-)
-parser.add_argument(
-    "--model_id",
-    type=str,
-    default="Qwen/Qwen2.5-7B-Instruct",
-    help="Model ID for text generation",
-)
-parser.add_argument(
-    "--tokenizer_id",
-    type=str,
-    default="Qwen/Qwen2.5-7B-Instruct",
-    help="Tokenizer ID for text generation",
-)
-parser.add_argument(
-    "--input_dir",
-    type=str,
-    help="Directory containing input text files",
-    default="data",
-)
-parser.add_argument(
-    "--max_new_tokens",
-    type=int,
-    default=2048,
-    help="Maximum number of new tokens to generate",
-)
-parser.add_argument(
-    "--output_path",
-    type=str,
-    default="exam_questions_output",
-    help="Directory to save the generated datasets",
-)
-
-args = parser.parse_args()
-
-################################################################################
-# Load the documents
-# We assume that the documents are in the input directory, and that each file
-# is a separate document about the same topic.
-################################################################################
-
-# Process all text files in the input directory
-documents = []
-for filename in os.listdir(args.input_dir):
-    if filename.endswith(".txt"):
-        file_path = os.path.join(args.input_dir, filename)
-        with open(file=file_path, mode="r", encoding="utf-8") as file:
-            document_content = file.read()
-            documents.append(document_content)
-
-# Create a single dataset from all document contents
-dataset = Dataset.from_dict({"document": documents})
-
-################################################################################
-# Define the prompts
-# We use a system prompt to guide the model to generate the correct output format.
-# A template is used to insert the document into the prompt.
-################################################################################
-
-SYSTEM_PROMPT = """\
-You are an exam writer specialized in writing exams for students.
-Your goal is to create questions and answers based on the document provided, 
-and a list of distractors, that are incorrect but viable answers to the question.
-Your answer must adhere to the following format:
-```
-[
-    {
-        "question": "Your question",
-        "answer": "The correct answer to the question",
-        "distractors": ["wrong answer 1", "wrong answer 2", "wrong answer 3"]
-    },
-    ... (more questions and answers as required)
-]
-```
-""".strip()
-
-INSTRUCTION_TEMPLATE = """\
-    Generate a list of answers and questions about the document. 
-    Document:\n\n{{ instruction }}"""
-
-################################################################################
-# Define the output structure
-# We define a data model for the output of the pipeline, this is used to ensure
-# that the output is in the correct format for the evaluation task.
-################################################################################
-
-
-class ExamQuestion(BaseModel):
-    question: str = Field(..., description="The question to be answered")
-    answer: str = Field(..., description="The correct answer to the question")
-    distractors: List[str] = Field(
-        ..., description="A list of incorrect but viable answers to the question"
-    )
-
-
-class ExamQuestions(BaseModel):
-    exam: List[ExamQuestion]
-
-
-################################################################################
-# Create the pipeline
-# We create a pipeline with a single task that generates the exam questions
-# based on the document and in the correct format. We will Hugging Face
-# InferenceEndpoints and the model specified in the arguments.
-################################################################################
-
-with Pipeline(
-    name="Domain-Eval-Questions",
-    description="Generate exam questions based on given documents.",
-) as pipeline:
-    # Set up the text generation task
-    text_generation = TextGeneration(
-        name="exam_generation",
-        llm=InferenceEndpointsLLM(
-            model_id=args.model_id,
-            tokenizer_id=args.model_id,
-            api_key=os.environ["HF_TOKEN"],
-            structured_output={
-                "schema": ExamQuestions.model_json_schema(),
-                "format": "json",
-            },
-        ),
-        input_batch_size=8,
-        output_mappings={"model_name": "generation_model"},
-        input_mappings={"instruction": "document"},
-        system_prompt=SYSTEM_PROMPT,
-        template=INSTRUCTION_TEMPLATE,
-    )
-
-
-################################################################################
-# Run the pipeline
-# We run the pipeline for all documents and save the results to the output path.
-################################################################################
-
-if __name__ == "__main__":
-    # Run the pipeline for all documents
-    distiset = pipeline.run(
-        parameters={
-            "exam_generation": {
-                "llm": {
-                    "generation_kwargs": {
-                        "max_new_tokens": args.max_new_tokens,
-                    }
-                }
-            }
-        },
-        use_cache=False,
-        dataset=dataset,
-    )
-
-    distiset.save_to_disk(args.output_path)
diff --git a/es/4_evaluation/project/images/domain_eval_argilla_view.png b/es/4_evaluation/project/images/domain_eval_argilla_view.png
deleted file mode 100644
index 7a07649ba1a40d826913a25f062f94daacdda625..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/es/README.md b/es/README.md
deleted file mode 100644
index 914d0956..00000000
--- a/es/README.md
+++ /dev/null
@@ -1,94 +0,0 @@
-![smolcourse image](./banner.png)
-
-# Un Pequeño (Smol) Curso
-
-Este curso práctico está enfocado en alinear modelos de lenguaje para casos de uso específicos. Es una forma accesible de empezar a trabajar con modelos de lenguaje, ya que puede ejecutarse en la mayoría de las máquinas locales con requisitos mínimos de GPU y sin necesidad de servicios pagos. El curso se basa en la serie de modelos [SmolLM2](https://github.com/huggingface/smollm/tree/main), pero las habilidades que adquieras aquí son transferibles a modelos más grandes o otros modelos pequeños de lenguaje.
-
-<a href="http://hf.co/join/discord">
-<img src="https://img.shields.io/badge/Discord-7289DA?&logo=discord&logoColor=white"/>
-</a>
-
-<div style="background: linear-gradient(to right, #e0f7fa, #e1bee7, orange); padding: 20px; border-radius: 5px; margin-bottom: 20px; color: purple;">
-    <h2>¡La participación es abierta, gratuita y ahora!</h2>
-    <p>Este curso es abierto y revisado por la comunidad. Para participar, simplemente <strong>abre un pull request</strong> y envía tu trabajo para su revisión. Sigue estos pasos:</p>
-    <ol>
-        <li>Haz un fork del repositorio <a href="https://github.com/huggingface/smol-course/fork">aquí</a></li>
-        <li>Lee el material, haz cambios, completa los ejercicios y agrega tus ejemplos.</li>
-        <li>Abre un PR en la rama december_2024</li>
-        <li>Haz que se revise y se fusione</li>
-    </ol>
-    <p>Este proceso te ayudará a aprender y a construir un curso dirigido por la comunidad que mejora constantemente.</p>
-</div>
-
-Podemos discutir el proceso en este [hilo de discusión](https://github.com/huggingface/smol-course/discussions/2#discussion-7602932).
-
-## Estructura del Curso
-
-Este curso ofrece un enfoque práctico para trabajar con modelos pequeños de lenguaje, desde el entrenamiento inicial hasta el despliegue en producción.
-
-| Módulo | Descripción | Estado | Fecha de lanzamiento |
-|--------|-------------|--------|----------------------|
-| [Ajuste de Instrucciones](./1_instruction_tuning) | Aprende ajuste fino (fine-tuning) supervisado, plantillas de chat y seguimiento básico de instrucciones | ✅ Completo | 3 de diciembre de 2024 |
-| [Alineación de Preferencias](./2_preference_alignment) | Explora las técnicas DPO y ORPO para alinear modelos con las preferencias humanas | ✅ Completo | 6 de diciembre de 2024 |
-| [Ajuste Fino (Fine-tuning) Eficiente en Parámetros](./3_parameter_efficient_finetuning) | Aprende LoRA, ajuste de prompt y métodos de adaptación eficientes | [🚧 En Progreso](https://github.com/huggingface/smol-course/pull/41) | 9 de diciembre de 2024 |
-| [Evaluación](./4_evaluation) | Usa benchmarks automáticos y crea evaluaciones personalizadas para dominios | [🚧 En Progreso](https://github.com/huggingface/smol-course/issues/42) | 13 de diciembre de 2024 |
-| [Modelos Visión-Lenguaje](./5_vision_language_models) | Adapta modelos multimodales para tareas visión-lenguaje | [🚧 En Progreso](https://github.com/huggingface/smol-course/issues/49) | 16 de diciembre de 2024 |
-| [Conjuntos de Datos Sintéticos](./6_synthetic_datasets) | Crea y valida conjuntos de datos sintéticos para el entrenamiento | 📝 Planificado | 20 de diciembre de 2024 |
-| [Inferencia](./7_inference) | Inferencia eficiente con modelos | 📝 Planificado | 23 de diciembre de 2024 |
-
-## ¿Por qué Modelos Pequeños de Lenguaje?
-
-Si bien los modelos grandes de lenguaje han mostrado capacidades impresionantes, requieren recursos computacionales significativos y pueden ser excesivos para aplicaciones específicas. Los modelos pequeños de lenguaje ofrecen varias ventajas para aplicaciones de dominio:
-
-- **Eficiencia**: Requieren menos recursos computacionales para entrenar y desplegar
-- **Personalización**: Más fáciles de ajustar para dominios específicos
-- **Control**: Mayor control sobre el comportamiento del modelo
-- **Costo**: Menores costos operativos para el entrenamiento y la inferencia
-- **Privacidad**: Pueden ejecutarse localmente, manteniendo la privacidad de los datos
-- **Sostenibilidad**: Uso eficiente de recursos con una huella de carbono más pequeña
-- **Investigación Académica**: Facilita la investigación académica con menos restricciones logísticas
-
-## Requisitos Previos
-
-Antes de comenzar, asegúrate de tener:
-
-- Conocimientos básicos en aprendizaje automático y procesamiento de lenguaje natural
-- Familiaridad con Python, PyTorch y la librería `transformers`
-- Acceso a un modelo de lenguaje preentrenado y un conjunto de datos etiquetado
-
-## Instalación
-
-Mantenemos el curso como un paquete para facilitar la instalación de dependencias. Recomendamos usar [uv](https://github.com/astral-sh/uv), pero también puedes utilizar alternativas como `pip` o `pdm`.
-
-### Usando `uv`
-
-Con `uv` instalado, puedes configurar el entorno del curso de esta manera:
-
-```bash
-uv venv --python 3.11.0
-uv sync
-```
-
-### Usando `pip`
-
-Para un entorno **python 3.11**, utiliza los siguientes comandos para instalar las dependencias:
-
-```bash
-# python -m venv .venv
-# source .venv/bin/activate
-pip install -r requirements.txt
-```
-
-### Google Colab
-
-Para **Google Colab**, instala las dependencias de la siguiente manera:
-
-```bash
-pip install -r transformers trl datasets huggingface_hub
-```
-
-## Participación
-
-Compartamos este curso para que muchas personas puedan aprender a ajustar LLMs sin necesidad de hardware costoso.
-
-[![Star History Chart](https://api.star-history.com/svg?repos=huggingface/smol-course&type=Date)](https://star-history.com/#huggingface/smol-course&Date)
\ No newline at end of file
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diff --git a/ja/1_instruction_tuning/README.md b/ja/1_instruction_tuning/README.md
deleted file mode 100644
index 41167b7b..00000000
--- a/ja/1_instruction_tuning/README.md
+++ /dev/null
@@ -1,30 +0,0 @@
-# インストラクションチューニング
-
-このモジュールでは、言語モデルのインストラクションチューニングのプロセスをガイドします。インストラクションチューニングとは、特定のタスクに対してモデルを適応させるために、特定のタスクに関連するデータセットで追加のトレーニングを行うことを指します。このプロセスは、特定のタスクにおけるモデルのパフォーマンスを向上させるのに役立ちます。
-
-このモジュールでは、2つのトピックを探ります：1) チャットテンプレートと2) 教師あり微調整
-
-## 1️⃣ チャットテンプレート
-
-チャットテンプレートは、ユーザーとAIモデル間のインタラクションを構造化し、一貫性のある文脈に適した応答を保証します。これらのテンプレートには、システムメッセージや役割に基づくメッセージなどのコンポーネントが含まれます。詳細については、[チャットテンプレート](./chat_templates.md)セクションを参照してください。
-
-## 2️⃣ 教師あり微調整
-
-教師あり微調整（SFT）は、事前トレーニングされた言語モデルを特定のタスクに適応させるための重要なプロセスです。これは、ラベル付きの例を含む特定のタスクのデータセットでモデルをトレーニングすることを含みます。SFTの詳細なガイド、重要なステップ、およびベストプラクティスについては、[教師あり微調整](./supervised_fine_tuning.md)ページを参照してください。
-
-## 演習ノートブック
-
-| タイトル | 説明 | 演習 | リンク | Colab |
-|--------|-------------|-----------|--------|-------|
-| チャットテンプレート | SmolLM2を使用してチャットテンプレートを使用し、チャットml形式のデータセットを処理する方法を学びます | 🐢 `HuggingFaceTB/smoltalk`データセットをchatml形式に変換 <br> 🐕 `openai/gsm8k`データセットをchatml形式に変換 | [ノートブック](./notebooks/chat_templates_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/1_instruction_tuning/notebooks/chat_templates_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-| 教師あり微調整 | SFTTrainerを使用してSmolLM2を微調整する方法を学びます | 🐢 `HuggingFaceTB/smoltalk`データセットを使用 <br> 🐕 `bigcode/the-stack-smol`データセットを試す <br> 🦁 実際の使用ケースに関連するデータセットを選択 | [ノートブック](./notebooks/sft_finetuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-
-## 参考文献
-
-- [Transformersのチャットテンプレートに関するドキュメント](https://huggingface.co/docs/transformers/main/en/chat_templating)
-- [TRLの教師あり微調整スクリプト](https://github.com/huggingface/trl/blob/main/examples/scripts/sft.py)
-- [TRLの`SFTTrainer`](https://huggingface.co/docs/trl/main/en/sft_trainer)
-- [直接選好最適化に関する論文](https://arxiv.org/abs/2305.18290)
-- [TRLを使用した教師あり微調整](https://huggingface.co/docs/trl/main/en/tutorials/supervised_fine_tuning)
-- [ChatMLとHugging Face TRLを使用したGoogle Gemmaの微調整方法](https://www.philschmid.de/fine-tune-google-gemma)
-- [LLMを微調整してペルシャ語の商品カタログをJSON形式で生成する方法](https://huggingface.co/learn/cookbook/en/fine_tuning_llm_to_generate_persian_product_catalogs_in_json_format)
diff --git a/ja/1_instruction_tuning/chat_templates.md b/ja/1_instruction_tuning/chat_templates.md
deleted file mode 100644
index 1d92e7f6..00000000
--- a/ja/1_instruction_tuning/chat_templates.md
+++ /dev/null
@@ -1,115 +0,0 @@
-# チャットテンプレート
-
-チャットテンプレートは、言語モデルとユーザー間のインタラクションを構造化するために不可欠です。これらは会話の一貫した形式を提供し、モデルが各メッセージの文脈と役割を理解し、適切な応答パターンを維持することを保証します。
-
-## ベースモデル vs インストラクションモデル
-
-ベースモデルは次のトークンを予測するために生のテキストデータでトレーニングされる一方、インストラクションモデルは特定の指示に従い会話に参加するように微調整されたモデルです。例えば、`SmolLM2-135M`はベースモデルであり、`SmolLM2-135M-Instruct`はその指示に特化したバリアントです。
-
-ベースモデルをインストラクションモデルのように動作させるためには、モデルが理解できるようにプロンプトを一貫してフォーマットする必要があります。ここでチャットテンプレートが役立ちます。ChatMLは、システム、ユーザー、アシスタントの役割を明確に示すテンプレート形式で会話を構造化します。
-
-ベースモデルは異なるチャットテンプレートで微調整される可能性があるため、インストラクションモデルを使用する際には、正しいチャットテンプレートを使用していることを確認する必要があります。
-
-## チャットテンプレートの理解
-
-チャットテンプレートの核心は、言語モデルと通信する際に会話がどのようにフォーマットされるべきかを定義することです。これには、システムレベルの指示、ユーザーメッセージ、およびアシスタントの応答が含まれ、モデルが理解できる構造化された形式で提供されます。この構造は、インタラクションの一貫性を維持し、モデルがさまざまな種類の入力に適切に応答することを保証します。以下はチャットテンプレートの例です：
-
-```sh
-<|im_end|>ユーザー
-こんにちは！<|im_end|>
-<|im_end|>アシスタント
-はじめまして！<|im_end|>
-<|im_end|>ユーザー
-質問してもいいですか？<|im_end|>
-<|im_end|>アシスタント
-```
-
-`transformers`ライブラリは、モデルのトークナイザーに関連してチャットテンプレートを自動的に処理します。`transformers`でチャットテンプレートがどのように構築されるかについて詳しくは[こちら](https://huggingface.co/docs/transformers/en/chat_templating#how-do-i-use-chat-templates)を参照してください。私たちはメッセージを正しい形式で構造化するだけで、残りはトークナイザーが処理します。以下は基本的な会話の例です：
-
-```python
-messages = [
-    {"role": "system", "content": "あなたは技術的なトピックに焦点を当てた役立つアシスタントです。"},
-    {"role": "user", "content": "チャットテンプレートとは何か説明できますか？"},
-    {"role": "assistant", "content": "チャットテンプレートは、ユーザーとAIモデル間の会話を構造化します..."}
-]
-```
-
-上記の例を分解して、チャットテンプレート形式にどのようにマッピングされるかを見てみましょう。
-
-## システムメッセージ
-
-システムメッセージは、モデルの動作の基礎を設定します。これらは、以降のすべてのインタラクションに影響を与える持続的な指示として機能します。例えば：
-
-```python
-system_message = {
-    "role": "system",
-    "content": "あなたはプロフェッショナルなカスタマーサービスエージェントです。常に礼儀正しく、明確で、役立つようにしてください。"
-}
-```
-
-## 会話
-
-チャットテンプレートは、ユーザーとアシスタント間の以前のやり取りを保存し、会話の履歴を通じて文脈を維持します。これにより、複数ターンにわたる一貫した会話が可能になります：
-
-```python
-conversation = [
-    {"role": "user", "content": "注文に関して助けが必要です"},
-    {"role": "assistant", "content": "お手伝いします。注文番号を教えていただけますか？"},
-    {"role": "user", "content": "注文番号はORDER-123です"},
-]
-```
-
-## Transformersを使用した実装
-
-`transformers`ライブラリは、チャットテンプレートのための組み込みサポートを提供します。使用方法は以下の通りです：
-
-```python
-from transformers import AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-135M-Instruct")
-
-messages = [
-    {"role": "system", "content": "あなたは役立つプログラミングアシスタントです。"},
-    {"role": "user", "content": "リストをソートするPython関数を書いてください"},
-]
-
-# チャットテンプレートを適用
-formatted_chat = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-```
-
-## カスタムフォーマット
-
-異なる役割に対して特別なトークンやフォーマットを追加するなど、さまざまなメッセージタイプのフォーマットをカスタマイズできます。例えば：
-
-```python
-template = """
-<|system|>{system_message}
-<|user|>{user_message}
-<|assistant|>{assistant_message}
-""".lstrip()
-```
-
-## マルチターン会話のサポート
-
-テンプレートは、文脈を維持しながら複雑なマルチターン会話を処理できます：
-
-```python
-messages = [
-    {"role": "system", "content": "あなたは数学の家庭教師です。"},
-    {"role": "user", "content": "微積分とは何ですか？"},
-    {"role": "assistant", "content": "微積分は数学の一分野です..."},
-    {"role": "user", "content": "例を教えてください。"},
-]
-```
-
-⏭️ [次へ: Supervised Fine-Tuning](./supervised_fine_tuning.md)
-
-## リソース
-
-- [Hugging Face Chat Templating Guide](https://huggingface.co/docs/transformers/main/en/chat_templating)
-- [Transformers Documentation](https://huggingface.co/docs/transformers)
-- [Chat Templates Examples Repository](https://github.com/chujiezheng/chat_templates)
diff --git a/ja/1_instruction_tuning/notebooks/.env.example b/ja/1_instruction_tuning/notebooks/.env.example
deleted file mode 100644
index 532be08f..00000000
--- a/ja/1_instruction_tuning/notebooks/.env.example
+++ /dev/null
@@ -1,2 +0,0 @@
-# このファイルを.envにコピーし、シークレット値を記入します
-HF_TOKEN=
diff --git a/ja/1_instruction_tuning/notebooks/chat_templates_example.ipynb b/ja/1_instruction_tuning/notebooks/chat_templates_example.ipynb
deleted file mode 100644
index bde66dc4..00000000
--- a/ja/1_instruction_tuning/notebooks/chat_templates_example.ipynb
+++ /dev/null
@@ -1,280 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# SFTTrainerを使用した教師あり微調整\n",
-    "\n",
-    "このノートブックでは、`trl`ライブラリの`SFTTrainer`を使用して`HuggingFaceTB/SmolLM2-135M`モデルを微調整する方法を示します。ノートブックのセルを実行すると、モデルの微調整が行われます。さまざまなデータセットを試して、難易度を選択できます。\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>演習: SFTTrainerを使用したSmolLM2の微調整</h2>\n",
-    "    <p>Hugging Faceのリポジトリからデータセットを取得し、それを使用してモデルを微調整します。</p>\n",
-    "    <p><b>難易度レベル</b></p>\n",
-    "    <p>🐢 `HuggingFaceTB/smoltalk`データセットを使用</p>\n",
-    "    <p>🐕 `bigcode/the-stack-smol`データセットを試し、特定のサブセット`data/python`でコード生成モデルを微調整します。</p>\n",
-    "    <p>🦁 実際の使用ケースに関連するデータセットを選択します。</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Google Colabでの要件のインストール\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "\n",
-    "# Hugging Faceへの認証\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()\n",
-    "\n",
-    "# 便利のため、Hugging Faceのトークンを.envファイルのHF_TOKENとして環境変数に設定できます"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 必要なライブラリをインポート\n",
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "from datasets import load_dataset\n",
-    "from trl import SFTConfig, SFTTrainer, setup_chat_format\n",
-    "import torch\n",
-    "\n",
-    "# デバイスを動的に設定\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "# モデルとトークナイザーをロード\n",
-    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=model_name\n",
-    ").to(device)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
-    "\n",
-    "# チャット形式を設定\n",
-    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n",
-    "\n",
-    "# 微調整されたモデルを保存および/またはアップロードするための名前を設定\n",
-    "finetune_name = \"SmolLM2-FT-MyDataset\"\n",
-    "finetune_tags = [\"smol-course\", \"module_1\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ベースモデルでの生成\n",
-    "\n",
-    "ここでは、チャットテンプレートを持たないベースモデルを試してみます。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# トレーニング前のベースモデルを試してみましょう\n",
-    "prompt = \"プログラミングについての俳句を書いてください\"\n",
-    "\n",
-    "# テンプレートでフォーマット\n",
-    "messages = [{\"role\": \"user\", \"content\": prompt}]\n",
-    "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n",
-    "\n",
-    "# 応答を生成\n",
-    "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
-    "outputs = model.generate(**inputs, max_new_tokens=100)\n",
-    "print(\"トレーニング前:\")\n",
-    "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## データセットの準備\n",
-    "\n",
-    "例としてデータセットをロードし、トレーニング用にフォーマットします。データセットは、各入力がプロンプトで、出力がモデルの期待される応答である入力-出力ペアで構成されている必要があります。\n",
-    "\n",
-    "**TRLは、モデルのチャットテンプレートに従って入力メッセージをフォーマットします。** これらは、`role`と`content`キーを持つ辞書のリストとして表現される必要があります。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 例としてデータセットをロード\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "# TODO: パスと名前のパラメータを使用してデータセットと設定を定義\n",
-    "ds = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO: 🦁 データセットがTRLでチャットテンプレートに変換できない形式の場合、処理が必要です。 [モジュール](../chat_templates.md)を参照してください。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## SFTTrainerの設定\n",
-    "\n",
-    "`SFTTrainer`は、トレーニングプロセスを制御するさまざまなパラメータで設定されます。これには、トレーニングステップの数、バッチサイズ、学習率、および評価戦略が含まれます。これらのパラメータを特定の要件と計算リソースに応じて調整します。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# SFTTrainerを設定\n",
-    "sft_config = SFTConfig(\n",
-    "    output_dir=\"./sft_output\",  # トレーニング結果の出力ディレクトリ\n",
-    "    max_steps=1000,  # データセットのサイズとトレーニングの所要時間に応じて調整\n",
-    "    per_device_train_batch_size=4,  # GPUのメモリ容量に応じて調整\n",
-    "    learning_rate=5e-5,  # 微調整の一般的な開始点\n",
-    "    logging_steps=10,  # トレーニングメトリックのログ記録頻度\n",
-    "    save_steps=100,  # モデルのチェックポイント保存頻度\n",
-    "    evaluation_strategy=\"steps\",  # 定期的な間隔でモデルを評��\n",
-    "    eval_steps=50,  # 評価の頻度\n",
-    "    use_mps_device=(\n",
-    "        True if device == \"mps\" else False\n",
-    "    ),  # 混合精度トレーニングにMPSを使用\n",
-    "    hub_model_id=finetune_name,  # モデルに一意の名前を付ける\n",
-    ")\n",
-    "\n",
-    "# SFTTrainerを初期化\n",
-    "trainer = SFTTrainer(\n",
-    "    model=model,\n",
-    "    args=sft_config,\n",
-    "    train_dataset=ds[\"train\"],\n",
-    "    tokenizer=tokenizer,\n",
-    "    eval_dataset=ds[\"test\"],\n",
-    ")\n",
-    "\n",
-    "# TODO: 🦁 🐕 選択したデータセットに合わせてSFTTrainerのパラメータを調整します。例えば、`bigcode/the-stack-smol`データセットを使用する場合、`content`列を選択する必要があります。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## モデルのトレーニング\n",
-    "\n",
-    "トレーナーが設定されたので、モデルのトレーニングを進めることができます。トレーニングプロセスは、データセットを反復し、損失を計算し、この損失を最小化するためにモデルのパラメータを更新することを含みます。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# モデルをトレーニング\n",
-    "trainer.train()\n",
-    "\n",
-    "# モデルを保存\n",
-    "trainer.save_model(f\"./{finetune_name}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.push_to_hub(tags=finetune_tags)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>追加の演習: 微調整されたモデルでの生成</h2>\n",
-    "    <p>🐕 微調整されたモデルを使用して、ベースモデルの例と同じように応答を生成します。</p>\n",
-    "</div>\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 同じメッセージで微調整されたモデルをテスト\n",
-    "\n",
-    "# トレーニング前のベースモデルを試してみましょう\n",
-    "prompt = \"プログラミングについての俳句を書いてください\"\n",
-    "\n",
-    "# テンプレートでフォーマット\n",
-    "messages = [{\"role\": \"user\", \"content\": prompt}]\n",
-    "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n",
-    "\n",
-    "# 応答を生成\n",
-    "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
-    "\n",
-    "# TODO: 微調整されたモデルを使用して、ベースモデルの例と同じように応答を生成します。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 💐 完了しました！\n",
-    "\n",
-    "このノートブックでは、`SFTTrainer`を使用して`HuggingFaceTB/SmolLM2-135M`モデルを微調整するためのステップバイステップガイドを提供しました。これらの手順に従うことで、特定のタスクをより効果的に実行するためにモデルを適応させることができます。このコースを続けて作業したい場合、以下の提案を試してみてください：\n",
-    "\n",
-    "- より高い難易度レベルでこのノートブックを試してみてください。\n",
-    "- 同僚のPRをレビューしてください。\n",
-    "- IssueやPRを通じてコースの資料を改善してください。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py310",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.15"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/ja/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb b/ja/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb
deleted file mode 100644
index c1161abe..00000000
--- a/ja/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb
+++ /dev/null
@@ -1,280 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# SFTTrainerを使用した教師あり微調整\n",
-    "\n",
-    "このノートブックでは、`trl`ライブラリの`SFTTrainer`を使用して`HuggingFaceTB/SmolLM2-135M`モデルを微調整する方法を示します。ノートブックのセルを実行すると、モデルの微調整が行われます。さまざまなデータセットを試して、難易度を選択できます。\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>演習: SFTTrainerを使用したSmolLM2の微調整</h2>\n",
-    "    <p>Hugging Faceのリポジトリからデータセットを取得し、それを使用してモデルを微調整します。</p>\n",
-    "    <p><b>難易度レベル</b></p>\n",
-    "    <p>🐢 `HuggingFaceTB/smoltalk`データセットを使用</p>\n",
-    "    <p>🐕 `bigcode/the-stack-smol`データセットを試し、特定のサブセット`data/python`でコード生成モデルを微調整します。</p>\n",
-    "    <p>🦁 実際の使用ケースに関連するデータセットを選択します。</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Google Colabでの要件のインストール\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "\n",
-    "# Hugging Faceへの認証\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()\n",
-    "\n",
-    "# 便利のため、Hugging Faceのトークンを.envファイルのHF_TOKENとして環境変数に設定できます"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 必要なライブラリをインポート\n",
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "from datasets import load_dataset\n",
-    "from trl import SFTConfig, SFTTrainer, setup_chat_format\n",
-    "import torch\n",
-    "\n",
-    "# デバイスを動的に設定\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "# モデルとトークナイザーをロード\n",
-    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=model_name\n",
-    ").to(device)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
-    "\n",
-    "# チャット形式を設定\n",
-    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n",
-    "\n",
-    "# 微調整されたモデルを保存および/またはアップロードするための名前を設定\n",
-    "finetune_name = \"SmolLM2-FT-MyDataset\"\n",
-    "finetune_tags = [\"smol-course\", \"module_1\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ベースモデルでの生成\n",
-    "\n",
-    "ここでは、チャットテンプレートを持たないベースモデルを試してみます。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# トレーニング前のベースモデルを試してみましょう\n",
-    "prompt = \"プログラミングについての俳句を書いてください\"\n",
-    "\n",
-    "# テンプレートでフォーマット\n",
-    "messages = [{\"role\": \"user\", \"content\": prompt}]\n",
-    "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n",
-    "\n",
-    "# 応答を生成\n",
-    "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
-    "outputs = model.generate(**inputs, max_new_tokens=100)\n",
-    "print(\"トレーニング前:\")\n",
-    "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## データセットの準備\n",
-    "\n",
-    "例としてデータセットをロードし、トレーニング用にフォーマットします。データセットは、各入力がプロンプトで、出力がモデルの期待される応答である入力-出力ペアで構成されている必要があります。\n",
-    "\n",
-    "**TRLは、モデルのチャットテンプレートに従って入力メッセージをフォーマットします。** これらは、`role`と`content`キーを持つ辞書のリストとして表現される必要があります。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 例としてデータセットをロード\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "# TODO: パスと名前のパラメータを使用してデータセットと設定を定義\n",
-    "ds = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO: 🦁 データセットがTRLでチャットテンプレートに変換できない形式の場合、処理が必要です。 [モジュール](../chat_templates.md)を参照してください。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## SFTTrainerの設定\n",
-    "\n",
-    "`SFTTrainer`は、トレーニングプロセスを制御するさまざまなパラメータで設定されます。これには、トレーニングステップの数、バッチサイズ、学習率、および評価戦略が含まれます。これらのパラメータを特定の要件と計算リソースに応じて調整します。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# SFTTrainerを設定\n",
-    "sft_config = SFTConfig(\n",
-    "    output_dir=\"./sft_output\",  # トレーニング結果の出力ディレクトリ\n",
-    "    max_steps=1000,  # データセットのサイズとトレーニングの所要時間に応じて調整\n",
-    "    per_device_train_batch_size=4,  # GPUのメモリ容量に応じて調整\n",
-    "    learning_rate=5e-5,  # 微調整の一般的な開始点\n",
-    "    logging_steps=10,  # トレーニングメトリックのログ記録頻度\n",
-    "    save_steps=100,  # モデルのチェックポイント保存頻度\n",
-    "    evaluation_strategy=\"steps\",  # 定期的な間隔でモデルを評価\n",
-    "    eval_steps=50,  # 評価の頻度\n",
-    "    use_mps_device=(\n",
-    "        True if device == \"mps\" else False\n",
-    "    ),  # 混合精度トレーニングにMPSを使用\n",
-    "    hub_model_id=finetune_name,  # モデルに一意の名前を付ける\n",
-    ")\n",
-    "\n",
-    "# SFTTrainerを初期化\n",
-    "trainer = SFTTrainer(\n",
-    "    model=model,\n",
-    "    args=sft_config,\n",
-    "    train_dataset=ds[\"train\"],\n",
-    "    tokenizer=tokenizer,\n",
-    "    eval_dataset=ds[\"test\"],\n",
-    ")\n",
-    "\n",
-    "# TODO: 🦁 🐕 選択したデータセットに合わせてSFTTrainerのパラメータを調整します。例えば、`bigcode/the-stack-smol`データセットを使用する場合、`content`列を選択する必要があります。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## モデルのトレーニング\n",
-    "\n",
-    "トレーナーが設定されたので、モデルのトレーニングを進めることができます。トレーニングプロセスは、データセットを反復し、損失を計算し、この損失を最小化するためにモデルのパラメータを更新することを含みます。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# モデルをトレーニング\n",
-    "trainer.train()\n",
-    "\n",
-    "# モデルを保存\n",
-    "trainer.save_model(f\"./{finetune_name}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.push_to_hub(tags=finetune_tags)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>追加の演習: 微調整されたモデルでの生成</h2>\n",
-    "    <p>🐕 微調整されたモデルを使用して、ベースモデルの例と同じように応答を生成します。</p>\n",
-    "</div>\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 同じメッセージで微調整されたモデルをテスト\n",
-    "\n",
-    "# トレーニング前のベースモデルを試してみましょう\n",
-    "prompt = \"プログラミングについての俳句を書いてください\"\n",
-    "\n",
-    "# テンプレートでフォーマット\n",
-    "messages = [{\"role\": \"user\", \"content\": prompt}]\n",
-    "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n",
-    "\n",
-    "# 応答を生成\n",
-    "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
-    "\n",
-    "# TODO: 微調整されたモデルを使用して、ベースモデルの例と同じように応答を生成します。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 💐 完了しました！\n",
-    "\n",
-    "このノートブックでは、`SFTTrainer`を使用して`HuggingFaceTB/SmolLM2-135M`モデルを微調整するためのステップバイステップガイドを提供しました。これらの手順に従うことで、特定のタスクをより効果的に実行するためにモデルを適応させることができます。このコースを続けて作業したい場合、以下の提案を試してみてください：\n",
-    "\n",
-    "- より高い難易度レベルでこのノートブックを試してみてください。\n",
-    "- 同僚のPRをレビューしてください。\n",
-    "- IssueやPRを通じてコースの資料を改善してください。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py310",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.15"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/ja/1_instruction_tuning/supervised_fine_tuning.md b/ja/1_instruction_tuning/supervised_fine_tuning.md
deleted file mode 100644
index e6e51ad9..00000000
--- a/ja/1_instruction_tuning/supervised_fine_tuning.md
+++ /dev/null
@@ -1,41 +0,0 @@
-# 教師あり微調整
-
-教師あり微調整（SFT）は、事前トレーニングされた言語モデルを特定のタスクやドメインに適応させるための重要なプロセスです。事前トレーニングされたモデルは一般的な能力を持っていますが、特定のユースケースで優れたパフォーマンスを発揮するためにはカスタマイズが必要です。SFTは、慎重に選ばれた人間によって検証された例を含むデータセットを使用してモデルをトレーニングすることで、このギャップを埋めます。
-
-## 教師あり微調整の理解
-
-教師あり微調整の核心は、事前トレーニングされたモデルに特定のタスクを実行する方法を教えることです。これは、入力と出力の例を多数提示し、モデルが特定のユースケースのパターンを学習できるようにすることを含みます。
-
-SFTは、事前トレーニング中に獲得した基本的な知識を活用しながら、モデルの動作を特定のニーズに合わせて調整するため、効果的です。
-
-## 教師あり微調整を使用するタイミング
-
-SFTを使用するかどうかの決定は、現在のモデルの能力と特定の要件とのギャップに依存します。SFTは、モデルの出力を正確に制御する必要がある場合や、専門的なドメインで作業する場合に特に価値があります。
-
-例えば、カスタマーサービスアプリケーションを開発している場合、モデルが一貫して企業のガイドラインに従い、技術的な問い合わせを標準化された方法で処理することを望むかもしれません。同様に、医療や法律のアプリケーションでは、正確さと特定のドメインの用語に従うことが重要です。これらの場合、SFTはモデルの応答を専門的な基準とドメインの専門知識に合わせるのに役立ちます。
-
-## 教師あり微調整（SFT）のプロセス
-
-教師あり微調整のプロセスは、特定のタスクのデータセットでモデルの重みをトレーニングすることを含みます。
-
-まず、ターゲットタスクを表すデータセットを準備または選択する必要があります。このデータセットには、モデルが直面するさまざまなシナリオをカバーする多様な例が含まれている必要があります。これらのデータの品質は重要です。各例は、モデルが生成することを望む出力の種類を示している必要があります。次に、微調整のフェーズがあり、ここでは`transformers`やHugging Faceの`trl`などのフレームワークを使用して、データセットでモデルをトレーニングします。
-
-プロセス全体を通じて、継続的な評価が重要です。モデルが望ましい動作を学習していることを確認するために、検証セットでのパフォーマンスを監視する必要があります。これにより、モデルが一般的な能力を失うことなく、特定のタスクに適応していることを確認できます。[モジュール4](../4_evaluation)では、モデルの評価方法について詳しく説明します。
-
-## 教師あり微調整の役割
-
-SFTは、言語モデルを人間の好みに合わせる上で重要な役割を果たします。人間のフィードバックを使用した強化学習（RLHF）や直接選好最適化（DPO）などの技術は、SFTを使用してタスクの基本的な理解を形成し、その後、モデルの応答を望ましい結果にさらに合わせます。事前トレーニングされたモデルは、一般的な言語能力に優れているにもかかわらず、必ずしも人間の好みに一致する出力を生成するわけではありません。SFTは、特定のドメインのデータとガイダンスを導入することで、このギャップを埋め、モデルが人間の期待により密接に一致する応答を生成する能力を向上させます。
-
-## トランスフォーマーの強化学習（TRL）を使用した教師あり微調整
-
-教師あり微調整のための主要なソフトウェアパッケージは、トランスフォーマーの強化学習（TRL）です。TRLは、強化学習（RL）を使用してトランスフォーマーモデルをトレーニングするためのツールセットです。
-
-Hugging FaceのTransformersライブラリに基づいており、ユーザーが事前トレーニングされた言語モデルを直接ロードできるようにし、ほとんどのデコーダーおよびエンコーダーデコーダーアーキテクチャと互換性があります。このライブラリは、教師あり微調整（SFT）、報酬モデリング（RM）、近接ポリシー最適化（PPO）、および直接選好最適化（DPO）など、言語モデリングで使用される主要なRLプロセスを容易にします。このリポジトリ全体で、さまざまなモジュールでTRLを使用します。
-
-# 次のステップ
-
-次のチュートリアルを試して、TRLを使用したSFTの実践的な経験を積んでください：
-
-⏭️ [チャットテンプレートのチュートリアル](./notebooks/chat_templates_example.ipynb)
-
-⏭️ [教師あり微調整のチュートリアル](./notebooks/supervised_fine_tuning_tutorial.ipynb)
diff --git a/ja/2_preference_alignment/README.md b/ja/2_preference_alignment/README.md
deleted file mode 100644
index 33f0643c..00000000
--- a/ja/2_preference_alignment/README.md
+++ /dev/null
@@ -1,39 +0,0 @@
-# 選好の整合
-
-このモジュールでは、言語モデルを人間の選好に合わせるための技術について説明します。教師あり微調整（SFT）がモデルにタスクを学習させるのに役立つ一方で、選好の整合は出力が人間の期待や価値観に一致するようにします。
-
-## 概要
-
-選好の整合の典型的な方法には、複数のステージが含まれます：
-1. 教師あり微調整（SFT）でモデルを特定のドメインに適応させる。
-2. 選好の整合（RLHFやDPOなど）で応答の質を向上させる。
-
-ORPOのような代替アプローチは、指示調整と選好の整合を単一のプロセスに統合します。ここでは、DPOとORPOのアルゴリズムに焦点を当てます。
-
-さまざまな整合技術について詳しく知りたい場合は、[Argillaのブログ](https://argilla.io/blog/mantisnlp-rlhf-part-8)を参照してください。
-
-### 1️⃣ 直接選好最適化（DPO）
-
-直接選好最適化（DPO）は、選好データを使用してモデルを直接最適化することで、選好の整合を簡素化します。このアプローチは、別個の報酬モデルや複雑な強化学習を必要とせず、従来のRLHFよりも安定して効率的です。詳細については、[直接選好最適化（DPO）のドキュメント](./dpo.md)を参照してください。
-
-### 2️⃣ 選好確率比最適化（ORPO）
-
-ORPOは、指示調整と選好の整合を単一のプロセスに統合する新しいアプローチを導入します。これは、負の対数尤度損失とトークンレベルのオッズ比項を組み合わせて標準的な言語モデリングの目的を修正します。このアプローチは、単一のトレーニングステージ、参照モデル不要のアーキテクチャ、および計算効率の向上を提供します。ORPOは、さまざまなベンチマークで印象的な結果を示しており、従来の方法と比較してAlpacaEvalで優れたパフォーマンスを示しています。詳細については、[選好確率比最適化（ORPO）のドキュメント](./orpo.md)を参照してください。
-
-## 実習ノートブック
-
-| タイトル | 説明 | 実習内容 | リンク | Colab |
-|-------|-------------|----------|------|-------|
-| DPOトレーニング | 直接選好最適化を使用してモデルを��レーニングする方法を学ぶ | 🐢 AnthropicのHH-RLHFデータセットを使用してモデルをトレーニングする<br>🐕 独自の選好データセットを使用する<br>🦁 さまざまな選好データセットとモデルサイズで実験する | [ノートブック](./notebooks/dpo_finetuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-| ORPOトレーニング | 選好確率比最適化を使用してモデルをトレーニングする方法を学ぶ | 🐢 指示と選好データを使用してモデルをトレーニングする<br>🐕 損失の重みを変えて実験する<br>🦁 ORPOとDPOの結果を比較する | [ノートブック](./notebooks/orpo_finetuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-
-## リソース
-
-- [TRLのドキュメント](https://huggingface.co/docs/trl/index) - DPOを含むさまざまな整合技術を実装するためのTransformers Reinforcement Learning（TRL）ライブラリのドキュメント。
-- [DPO論文](https://arxiv.org/abs/2305.18290) - 人間のフィードバックを用いた強化学習の代替として、選好データを使用して言語モデルを直接最適化するシンプルなアプローチを紹介する論文。
-- [ORPO論文](https://arxiv.org/abs/2403.07691) - 指示調整と選好の整合を単一のトレーニングステージに統合する新しいアプローチを紹介する論文。
-- [ArgillaのRLHFガイド](https://argilla.io/blog/mantisnlp-rlhf-part-8/) - RLHF、DPOなどのさまざまな整合技術とその実践的な実装について説明するガイド。
-- [DPOに関するブログ記事](https://huggingface.co/blog/dpo-trl) - TRLライブラリを使用してDPOを実装する方法についての実践ガイド。コード例とベストプラクティスが含まれています。
-- [TRLのDPOスクリプト例](https://github.com/huggingface/trl/blob/main/examples/scripts/dpo.py) - TRLライブラリを使用してDPOトレーニングを実装する方法を示す完全なスクリプト例。
-- [TRLのORPOスクリプト例](https://github.com/huggingface/trl/blob/main/examples/scripts/orpo.py) - TRLライブラリを使用してORPOトレーニングを実装するためのリファレンス実装。詳細な設定オプションが含まれています。
-- [Hugging Faceの整合ハンドブック](https://github.com/huggingface/alignment-handbook) - SFT、DPO、RLHFなどのさまざまな技術を使用して言語モデルを整合させるためのガイドとコード。
diff --git a/ja/2_preference_alignment/dpo.md b/ja/2_preference_alignment/dpo.md
deleted file mode 100644
index 6d354d35..00000000
--- a/ja/2_preference_alignment/dpo.md
+++ /dev/null
@@ -1,73 +0,0 @@
-**直接選好最適化（DPO）**
-
-直接選好最適化（DPO）は、言語モデルを人間の好みに合わせるための簡素化されたアプローチを提供します。従来のRLHF（人間のフィードバックを用いた強化学習）メソッドとは異なり、DPOは別個の報酬モデルや複雑な強化学習アルゴリズムを必要とせず、選好データを使用してモデルを直接最適化します。
-
-## DPOの理解
-
-DPOは、選好の整合を人間の選好データに基づく分類問題として再定義します。従来のRLHFアプローチでは、別個の報酬モデルをトレーニングし、PPO（近接ポリシー最適化）などの複雑なアルゴリズムを使用してモデルの出力を整合させる必要があります。DPOは、好ましい出力と好ましくない出力に基づいてモデルのポリシーを直接最適化する損失関数を定義することで、このプロセスを簡素化します。
-
-このアプローチは実際に非常に効果的であり、Llamaなどのモデルのトレーニングに使用されています。別個の報酬モデルや強化学習のステージを必要としないため、DPOは選好の整合をよりアクセスしやすく、安定したものにします。
-
-## DPOの仕組み
-
-DPOのプロセスには、ターゲットドメインにモデルを適応させるための教師あり微調整（SFT）が必要です。これにより、標準的な指示追従データセットでトレーニングすることで、選好学習の基盤が形成されます。モデルは基本的なタスクを完了しながら、一般的な能力を維持することを学びます。
-
-次に、選好学習が行われ、モデルは好ましい出力と好ましくない出力のペアでトレーニングされます。選好ペアは、モデルがどの応答が人間の価値観や期待に最も一致するかを理解するのに役立ちます。
-
-DPOの中心的な革新は、その直接最適化アプローチにあります。別個の報酬モデルをトレーニングする代わりに、DPOはバイナリクロスエントロピー損失を使用して、選好データに基づいてモデルの重みを直接更新します。この簡素化されたプロセスにより、トレーニングがより安定し、効率的になり、従来のRLHFメソッドと同等またはそれ以上の結果が得られます。
-
-## DPOのデータセット
-
-DPOのデータセットは、通常、選好または非選好として注釈された応答ペアを含むように作成されます。これは手動で行うか、自動フィルタリング技術を使用して行うことができます。以下は、単一ターンのDPO選好データセットの構造の例です：
-
-| プロンプト | 選好 | 非選好 |
-|--------|---------|-----------|
-| ...    | ...     | ...       |
-| ...    | ...     | ...       |
-| ...    | ...     | ...       |
-
-`Prompt`列には、`選好`および`非選好`の応答を生成するために使用されたプロンプトが含まれています。`選好`および`非選好`列には、それぞれ好ましい応答と好ましくない応答が含まれています。この構造にはバリエーションがあり、例えば、`system_prompt`や`Input`列に参照資料を含めることができます。`選好`および`非選好`の値は、単一ターンの会話の場合は文字列として、または会話リストとして表現されることがあります。
-
-Hugging FaceでDPOデータセットのコレクションを[こちら](https://huggingface.co/collections/argilla/preference-datasets-for-dpo-656f0ce6a00ad2dc33069478)で見つけることができます。
-
-## TRLを使用した実装
-
-Transformers Reinforcement Learning（TRL）ライブラリは、DPOの実装を容易にします。`DPOConfig`および`DPOTrainer`クラスは、`transformers`スタイルのAPIに従います。
-
-以下は、DPOトレーニングを設定する基本的な例です：
-
-```python
-from trl import DPOConfig, DPOTrainer
-
-# 引数を定義
-training_args = DPOConfig(
-    ...
-)
-
-# トレーナーを初期化
-trainer = DPOTrainer(
-    model,
-    train_dataset=dataset,
-    tokenizer=tokenizer,
-    ...
-)
-
-# モデルをトレーニング
-trainer.train()
-```
-
-DPOConfigおよびDPOTrainerクラスの使用方法の詳細については、[DPOチュートリアル](./notebooks/dpo_finetuning_example.ipynb)を参照してください。
-
-## ベストプラクティス
-
-データの品質は、DPOの成功した実装にとって重要です。選好データセットには、望ましい行動のさまざまな側面をカバーする多様な例が含まれている必要があります。明確な注釈ガイドラインは、選好および非選好の応答の一貫したラベル付けを保証します。選好データセットの品質を向上させることで、モデルのパフォーマンスを向上させることができます。例えば、より大きなデータセットをフィルタリングして、高品質の例やユースケースに関連する例のみを含めることができます。
-
-トレーニング中は、損失の収束を慎重に監視し、保持データでパフォーマンスを検証します。選好学習とモデルの一般的な能力の維持をバランスさせるために、ベータパラメータを調整する必要がある場合があります。多様な例での定期的な評価は、モデルが望ましい選好を学習しながら過剰適合しないことを保証するのに役立ちます。
-
-モデルの出力を基準モデルと比較して、選好の整合性の向上を確認します。さまざまなプロンプト、特にエッジケースを使用してモデルをテストすることで、さまざまなシナリオでの選好学習の堅牢性を保証します。
-
-## 次のステップ
-
-⏩ DPOの実践的な経験を得るために、[DPOチュートリアル](./notebooks/dpo_finetuning_example.ipynb)を試してみてください。この実践ガイドでは、データの準備からトレーニングおよび評価まで、選好整合の実装方法を説明します。
-
-⏭️ チュートリアルを完了した後、別の選好整合技術について学ぶために[ORPOページ](./orpo.md)を探索してください。
diff --git a/ja/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb b/ja/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
deleted file mode 100644
index 38f63fde..00000000
--- a/ja/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
+++ /dev/null
@@ -1,167 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# DPO微調整の例\n",
-    "\n",
-    "このノートブックでは、`trl`ライブラリを使用してDPO（直接選好最適化）を実行する方法を示します。DPOは、モデルの出力を人間の選好に合わせるためのシンプルで効果的な方法です。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. 環境の設定\n",
-    "\n",
-    "まず、必要なライブラリをインストールし、Hugging Faceにログインします。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Google Colabでの要件のインストール\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "# Hugging Faceへの認証\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()\n",
-    "\n",
-    "# 便利のため、Hugging Faceのトークンを.envファイルのHF_TOKENとして環境変数に設定できます"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. モデルとデータセットのロード\n",
-    "\n",
-    "次に、事前学習済みモデルとデータセットをロードします。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "# モデルとトークナイザーをロード\n",
-    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
-    "model = AutoModelForCausalLM.from_pretrained(model_name)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
-    "\n",
-    "# データセットをロード\n",
-    "dataset = load_dataset(\"Anthropic/hh-rlhf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. DPOトレーナーの設定\n",
-    "\n",
-    "DPOトレーナーを設定し、トレーニングを開始します。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from trl import DPOConfig, DPOTrainer\n",
-    "\n",
-    "# DPOの設定\n",
-    "dpo_config = DPOConfig(\n",
-    "    model_name_or_path=model_name,\n",
-    "    learning_rate=5e-5,\n",
-    "    per_device_train_batch_size=4,\n",
-    "    num_train_epochs=3,\n",
-    "    logging_steps=10,\n",
-    "    save_steps=100,\n",
-    "    output_dir=\"./dpo_output\"\n",
-    ")\n",
-    "\n",
-    "# DPOトレーナーを初期化\n",
-    "trainer = DPOTrainer(\n",
-    "    model=model,\n",
-    "    args=dpo_config,\n",
-    "    train_dataset=dataset[\"train\"],\n",
-    "    eval_dataset=dataset[\"test\"],\n",
-    "    tokenizer=tokenizer\n",
-    ")\n",
-    "\n",
-    "# モデルをトレーニング\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4. 微調整されたモデルの保存\n",
-    "\n",
-    "トレーニングが完了したら、微調整されたモデルを保存します。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 微調整されたモデルを保存\n",
-    "trainer.save_model(\"./dpo_finetuned_model\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 5. 微調整されたモデルの評価\n",
-    "\n",
-    "最後に、微調整されたモデルを評価します。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 評価の実行\n",
-    "results = trainer.evaluate()\n",
-    "print(results)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/ja/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb b/ja/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
deleted file mode 100644
index e4fc09ca..00000000
--- a/ja/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
+++ /dev/null
@@ -1,167 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ORPO微調整の例\n",
-    "\n",
-    "このノートブックでは、`trl`ライブラリを使用してORPO（選好確率比最適化）を実行する方法を示します。ORPOは、モデルの出力を人間の選好に合わせるためのシンプルで効果的な方法です。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. 環境の設定\n",
-    "\n",
-    "まず、必要なライブラリをインストールし、Hugging Faceにログインします。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Google Colabでの要件のインストール\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "# Hugging Faceへの認証\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()\n",
-    "\n",
-    "# 便利のため、Hugging Faceのトークンを.envファイルのHF_TOKENとして環境変数に設定できます"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. モデルとデータセットのロード\n",
-    "\n",
-    "次に、事前学習済みモデルとデータセットをロードします。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "# モデルとトークナイザーをロード\n",
-    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
-    "model = AutoModelForCausalLM.from_pretrained(model_name)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
-    "\n",
-    "# データセットをロード\n",
-    "dataset = load_dataset(\"Anthropic/hh-rlhf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. ORPOトレーナーの設定\n",
-    "\n",
-    "ORPOトレーナーを設定し、トレーニングを開始します。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from trl import ORPOConfig, ORPOTrainer\n",
-    "\n",
-    "# ORPOの設定\n",
-    "orpo_config = ORPOConfig(\n",
-    "    model_name_or_path=model_name,\n",
-    "    learning_rate=5e-5,\n",
-    "    per_device_train_batch_size=4,\n",
-    "    num_train_epochs=3,\n",
-    "    logging_steps=10,\n",
-    "    save_steps=100,\n",
-    "    output_dir=\"./orpo_output\"\n",
-    ")\n",
-    "\n",
-    "# ORPOトレーナーを初期化\n",
-    "trainer = ORPOTrainer(\n",
-    "    model=model,\n",
-    "    args=orpo_config,\n",
-    "    train_dataset=dataset[\"train\"],\n",
-    "    eval_dataset=dataset[\"test\"],\n",
-    "    tokenizer=tokenizer\n",
-    ")\n",
-    "\n",
-    "# モデルをトレーニング\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4. 微調整されたモデルの保存\n",
-    "\n",
-    "トレーニングが完了したら、微調整されたモデルを保存します。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 微調整されたモデルを保存\n",
-    "trainer.save_model(\"./orpo_finetuned_model\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 5. 微調整されたモデルの評価\n",
-    "\n",
-    "最後に、微調整されたモデルを評価します。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 評価の実行\n",
-    "results = trainer.evaluate()\n",
-    "print(results)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
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diff --git a/ja/2_preference_alignment/orpo.md b/ja/2_preference_alignment/orpo.md
deleted file mode 100644
index ac3a4af6..00000000
--- a/ja/2_preference_alignment/orpo.md
+++ /dev/null
@@ -1,83 +0,0 @@
-**選好確率比最適化（ORPO）**
-
-選好確率比最適化（ORPO）は、微調整と選好整合を単一の統合プロセスで組み合わせる新しいアプローチです。この統合アプローチは、従来のRLHFやDPOなどの方法と比較して、効率とパフォーマンスの面で利点を提供します。
-
-## ORPOの理解
-
-DPOのような選好整合方法は、通常、微調整と選好整合の2つの別々のステップを含みます。微調整はモデルを特定のドメインや形式に適応させ、選好整合は人間の選好に合わせて出力を調整します。SFT（教師あり微調整）は、モデルをターゲットドメインに適応させるのに効果的ですが、望ましい応答と望ましくない応答の両方の確率を増加させる可能性があります。ORPOは、以下の比較図に示すように、これらのステップを単一のプロセスに統合することで、この制限に対処します。
-
-![モデル整合技術の比較](https://argilla.io/images/blog/mantisnlp-rlhf/part-8-alignments.png)
-*モデル整合技術の比較*
-
-## ORPOの仕組み
-
-ORPOのトレーニングプロセスは、DPOと同様に、入力プロンプトと2つの応答（1つは選好、もう1つは非選好）を含む選好データセットを使用します。他の整合方法とは異なり、ORPOは選好整合を教師あり微調整プロセスに直接統合します。この統合アプローチにより、参照モデルが不要になり、計算効率とメモリ効率が向上し、FLOP数が減少します。
-
-ORPOは、次の2つの主要なコンポーネントを組み合わせて新しい目的を作成します：
-
-1. **SFT損失**：負の対数尤度損失を使用して、参照トークンの生成確率を最大化します。これにより、モデルの一般的な言語能力が維持されます。
-2. **オッズ比損失**：望ましくない応答をペナルティし、選好される応答を報酬する新しいコンポーネントです。この損失関数は、トークンレベルで選好される応答と非選好の応答を効果的に対比するためにオッズ比を使用します。
-
-これらのコンポーネントを組み合わせることで、モデルは特定のドメインに対して望ましい生成を適応させながら、非選好の応答を積極的に抑制します。オッズ比メカニズムは、選好された応答と拒否された応答の間のモデルの選好を測定および最適化する自然な方法を提供します。数学的な詳細については、[ORPO論文](https://arxiv.org/abs/2403.07691)を参照してください。実装の観点からORPOについて学びたい場合は、[TRLライブラリ](https://github.com/huggingface/trl/blob/b02189aaa538f3a95f6abb0ab46c0a971bfde57e/trl/trainer/orpo_trainer.py#L660)でORPO損失がどのように計算されるかを確認してください。
-
-## パフォーマンスと結果
-
-ORPOは、さまざまなベンチマークで印象的な結果を示しています。MT-Benchでは、さまざまなカテゴリで競争力のあるスコアを達成しています：
-
-![MT-Benchの結果](https://argilla.io/images/blog/mantisnlp-rlhf/part-8-mtbench.png)
-*MT-Benchのカテゴリ別結果（Mistral-ORPOモデル）*
-
-他の整合方法と比較すると、ORPOはAlpacaEval 2.0で優れたパフォーマンスを示しています：
-
-![AlpacaEvalの結果](https://argilla.io/images/blog/mantisnlp-rlhf/part-8-winrate.png)
-*さまざまな整合方法におけるAlpacaEval 2.0スコア*
-
-SFT+DPOと比較して、ORPOは参照モデルが不要であり、バッチごとのフォワードパスの数を半減させることで計算要件を削減します。さらに、トレーニングプロセスは、さまざまなモデルサイズとデータセットでより安定しており、調整するハイパーパラメータが少なくて済みます。パフォーマンスに関しては、ORPOはより大きなモデルと同等のパフォーマンスを示しながら、人間の選好に対する整合性が向上しています。
-
-## 実装
-
-ORPOの成功した実装は、高品質の選好データに大きく依存します。トレーニングデータは明確な注釈ガイドラインに従い、さまざまなシナリオで好ましい応答と拒否された応答のバランスの取れた表現を提供する必要があります。
-
-### TRLを使用した実装
-
-ORPOは、Transformers Reinforcement Learning（TRL）ライブラリを使用して実装できます。以下は基本的な例です：
-
-```python
-from trl import ORPOConfig, ORPOTrainer
-
-# ORPOトレーニングの設定
-orpo_config = ORPOConfig(
-    learning_rate=1e-5,
-    per_device_train_batch_size=4,
-    gradient_accumulation_steps=4,
-    max_steps=1000,
-    orpo_alpha=1.0,  # 選好最適化の強度を制御
-    orpo_beta=0.1,   # オッズ比計算の温度パラメータ
-)
-
-# トレーナーを初期化
-trainer = ORPOTrainer(
-    model=model,
-    args=orpo_config,
-    train_dataset=dataset,
-    tokenizer=tokenizer,
-)
-
-# トレーニングを開始
-trainer.train()
-```
-
-考慮すべき主要なパラメータ：
-- `orpo_alpha`：選好最適化の強度を制御
-- `orpo_beta`：オッズ比計算の温度パラメータ
-- `learning_rate`：忘却のカタストロフィーを避けるために比較的小さく設定
-- `gradient_accumulation_steps`：トレーニングの安定性を向上
-
-## 次のステップ
-
-⏩ この統合された選好整合アプローチを実装するための[ORPOチュートリアル](./notebooks/orpo_tutorial.ipynb)を試してみてください。
-
-## リソース
-- [ORPO論文](https://arxiv.org/abs/2403.07691)
-- [TRLドキュメント](https://huggingface.co/docs/trl/index)
-- [ArgillaのRLHFガイド](https://argilla.io/blog/mantisnlp-rlhf-part-8/)
diff --git a/ja/3_parameter_efficient_finetuning/README.md b/ja/3_parameter_efficient_finetuning/README.md
deleted file mode 100644
index f375fc32..00000000
--- a/ja/3_parameter_efficient_finetuning/README.md
+++ /dev/null
@@ -1,39 +0,0 @@
-# パラメータ効率の良い微調整 (PEFT)
-
-言語モデルが大きくなるにつれて、従来の微調整はますます困難になります。1.7Bパラメータのモデルの完全な微調整には、かなりのGPUメモリが必要であり、別々のモデルコピーを保存することが高価であり、モデルの元の能力を破壊的に忘れるリスクがあります。パラメータ効率の良い微調整（PEFT）メソッドは、モデルパラメータの小さなサブセットのみを変更し、ほとんどのモデルを固定したままにすることで、これらの課題に対処します。
-
-従来の微調整は、トレーニング中にすべてのモデルパラメータを更新しますが、これは大規模なモデルには実用的ではありません。PEFTメソッドは、元のモデルサイズの1％未満のパラメータを使用してモデルを適応させるアプローチを導入します。この劇的な学習可能なパラメータの削減により、次のことが可能になります：
-
-- 限られたGPUメモリを持つ消費者向けハードウェアでの微調整
-- 複数のタスク固有の適応を効率的に保存
-- 低データシナリオでのより良い一般化
-- より速いトレーニングと反復サイクル
-
-## 利用可能なメソッド
-
-このモジュールでは、2つの人気のあるPEFTメソッドを紹介します：
-
-### 1️⃣ LoRA (低ランク適応)
-
-LoRAは、効率的なモデル適応のためのエレガントなソリューションを提供する最も広く採用されているPEFTメソッドとして浮上しました。モデル全体を変更する代わりに、**LoRAはモデルの注意層に学習可能な行列を注入します。**このアプローチは、通常、完全な微調整と比較して約90％の学習可能なパラメータを削減しながら、同等の性能を維持します。[LoRA (低ランク適応)](./lora_adapters.md)セクションでLoRAを詳しく見ていきます。
- 
-### 2️⃣ プロンプトチューニング
-
-プロンプトチューニングは、モデルの重みを変更するのではなく、**入力に学習可能なトークンを追加する**さらに軽量なアプローチを提供します。プロンプトチューニングはLoRAほど人気はありませんが、モデルを新しいタスクやドメインに迅速に適応させるための便利な技術です。[プロンプトチューニング](./prompt_tuning.md)セクションでプロンプトチューニングを詳しく見ていきます。
-
-## 演習ノートブック
-
-| タイトル | 説明 | 演習 | リンク | Colab |
-|-------|-------------|----------|------|-------|
-| LoRA微調整 | LoRAアダプタを使用してモデルを微調整する方法を学ぶ | 🐢 LoRAを使用してモデルをトレーニング<br>🐕 異なるランク値で実験<br>🦁 完全な微調整と性能を比較 | [ノートブック](./notebooks/finetune_sft_peft.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-| LoRAアダプタの読み込み | トレーニングされたLoRAアダプタを読み込んで使用する方法を学ぶ | 🐢 事前学習されたアダプタを読み込む<br>🐕 アダプタをベースモデルと統合<br>🦁 複数のアダプタ間を切り替える | [ノートブック](./notebooks/load_lora_adapter.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-<!-- | プロンプトチューニング | プロンプトチューニングを実装する方法を学ぶ | 🐢 ソフトプロンプトをトレーニング<br>🐕 異なる初期化戦略を比較<br>🦁 複数のタスクで評価 | [ノートブック](./notebooks/prompt_tuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/prompt_tuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | -->
-
-## リソース
-- [PEFTドキュメント](https://huggingface.co/docs/peft)
-- [LoRA論文](https://arxiv.org/abs/2106.09685)
-- [QLoRA論文](https://arxiv.org/abs/2305.14314)
-- [プロンプトチューニング論文](https://arxiv.org/abs/2104.08691)
-- [Hugging Face PEFTガイド](https://huggingface.co/blog/peft)
-- [2024年にHugging FaceでLLMを微調整する方法](https://www.philschmid.de/fine-tune-llms-in-2024-with-trl) 
-- [TRL](https://huggingface.co/docs/trl/index)
diff --git a/ja/3_parameter_efficient_finetuning/images/lora_adapter.png b/ja/3_parameter_efficient_finetuning/images/lora_adapter.png
deleted file mode 100644
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diff --git a/ja/3_parameter_efficient_finetuning/lora_adapters.md b/ja/3_parameter_efficient_finetuning/lora_adapters.md
deleted file mode 100644
index 1eef41eb..00000000
--- a/ja/3_parameter_efficient_finetuning/lora_adapters.md
+++ /dev/null
@@ -1,124 +0,0 @@
-# LoRA (低ランク適応)
-
-LoRAは最も広く採用されているPEFTメソッドとなっています。これは、注意重み行列に小さなランク分解行列を追加することで機能し、通常、学習可能なパラメータを約90％削減します。
-
-## LoRAの理解
-
-LoRA（低ランク適応）は、事前学習されたモデルの重みを固定し、学習可能なランク分解行列をモデルの層に注入するパラメータ効率の良い微調整技術です。微調整中にすべてのモデルパラメータを学習する代わりに、LoRAは低ランク分解を通じて重みの更新を小さな行列に分解し、学習可能なパラメータの数を大幅に削減しながらモデルの性能を維持します。例えば、GPT-3 175Bに適用した場合、LoRAは学習可能なパラメータを10,000倍、GPUメモリ要件を3倍削減しました。LoRAについての詳細は[LoRA論文](https://arxiv.org/pdf/2106.09685)を参照してください。
-
-LoRAは、通常、注意重みに焦点を当てて、トランスフォーマーレイヤーにランク分解行列のペアを追加することで機能します。推論中に、これらのアダプタ重みはベースモデルと統合され、追加の遅延オーバーヘッドが発生しません。LoRAは、大規模な言語モデルを特定のタスクやドメインに適応させるのに特に役立ち、リソース要件を管理可能に保ちます。
-
-## LoRAアダプタの読み込み
-
-アダプタは、load_adapter()を使用して事前学習されたモデルに読み込むことができ、これは重みが統合されていない異なるアダプタを試すのに便利です。set_adapter()関数を使用してアクティブなアダプタ重みを設定します。ベースモデルに戻るには、unload()を使用してすべてのLoRAモジュールをアンロードできます。これにより、異なるタスク固有の重み間の切り替えが容易になります。
-
-```python
-from transformers import AutoModelForCausalLM
-from peft import PeftModel
-
-base_model = AutoModelForCausalLM.from_pretrained("<base_model_name>")
-peft_model_id = "<peft_adapter_id>"
-model = PeftModel.from_pretrained(base_model, peft_model_id)
-```
-
-![lora_load_adapter](./images/lora_adapter.png)
-
-## LoRAアダプタの統合
-
-LoRAでトレーニングした後、アダプタ重みをベースモデルに統合して、デプロイを容易にすることができます。これにより、統合された重みを持つ単一のモデルが作成され、推論中にアダプタを別々に読み込む必要がなくなります。
-
-統合プロセスには、メモリ管理と精度に注意が必要です。ベースモデルとアダプタ重みの両方を同時に読み込む必要があるため、十分なGPU/CPUメモリが利用可能であることを確認してください。`transformers`の`device_map="auto"`を使用すると、自動メモリ管理が容易になります。トレーニング中に使用した精度（例：float16）を一貫して維持し、統合されたモデルを同じ形式で保存してデプロイします。デプロイ前に、アダプタベースのバージョンと出力および性能メトリックを比較して、統合されたモデルを検証してください。
-
-アダプタは、異なるタスクやドメイン間の切り替えにも便利です。ベースモデルとアダプタ重みを別々に読み込むことができます。これにより、異なるタスク固有の重み間の迅速な切り替えが可能になります。
-
-## 実装ガイド
-
-`notebooks/`ディレクトリには、さまざまなPEFTメソッドを実装するための実践的なチュートリアルと演習が含まれています。基本的な紹介には`load_lora_adapter_example.ipynb`を、LoRAとSFTを使用したモデルの微調整について詳しく知りたい場合は`lora_finetuning.ipynb`を参照してください。
-
-PEFTメソッドを実装する際は、LoRAのランク値（4-8）を小さく設定し、トレーニング損失を監視します。検証セットを使用して過学習を防ぎ、可能であればフルファインチューニングのベースラインと結果を比較します。異なるメソッドの有効性はタスクによって異なるため、実験が重要です。
-
-## OLoRA
-
-[OLoRA](https://arxiv.org/abs/2406.01775)は、QR分解を使用してLoRAアダプタを初期化します。OLoRAは、QR分解の係数によってモデルのベース重みを変換します。つまり、トレーニングを行う前に重みを変換します。このアプローチは、安定性を大幅に向上させ、収束速度を加速し、最終的に優れた性能を達成します。
-
-## TRLとPEFTの使用
-
-PEFTメソッドは、TRL（Transformers Reinforcement Learning）と組み合わせて効率的な微調整を行うことができます。この統合は、RLHF（Reinforcement Learning from Human Feedback）に特に有用であり、メモリ要件を削減します。
-
-```python
-from peft import LoraConfig
-from transformers import AutoModelForCausalLM
-
-# PEFT設定でモデルを読み込む
-lora_config = LoraConfig(
-    r=16,
-    lora_alpha=32,
-    lora_dropout=0.05,
-    bias="none",
-    task_type="CAUSAL_LM"
-)
-
-# 特定のデバイスにモデルを読み込む
-model = AutoModelForCausalLM.from_pretrained(
-    "your-model-name",
-    load_in_8bit=True,  # オプション: 8ビット精度を使用
-    device_map="auto",
-    peft_config=lora_config
-)
-```
-
-上記では、`device_map="auto"`を使用してモデルを自動的に適切なデバイスに割り当てました。また、`device_map={"": device_index}`を使用してモデルを特定のデバイスに手動で割り当てることもできます。メモリ使用量を効率的に保ちながら、複数のGPUにトレーニングをスケールすることもできます。
-
-## 基本的な統合実装
-
-LoRAアダプタをトレーニングした後、アダプタ重みをベースモデルに統合することができます。以下はその方法です：
-
-```python
-import torch
-from transformers import AutoModelForCausalLM
-from peft import PeftModel
-
-# 1. ベースモデルを読み込む
-base_model = AutoModelForCausalLM.from_pretrained(
-    "base_model_name",
-    torch_dtype=torch.float16,
-    device_map="auto"
-)
-
-# 2. アダプタを持つPEFTモデルを読み込む
-peft_model = PeftModel.from_pretrained(
-    base_model,
-    "path/to/adapter",
-    torch_dtype=torch.float16
-)
-
-# 3. アダプタ重みをベースモデルと統合
-try:
-    merged_model = peft_model.merge_and_unload()
-except RuntimeError as e:
-    print(f"統合に失敗しました: {e}")
-    # フォールバック戦略またはメモリ最適化を実装
-
-# 4. 統合されたモデルを保存
-merged_model.save_pretrained("path/to/save/merged_model")
-```
-
-保存されたモデルのサイズに不一致がある場合は、トークナイザーも保存していることを確認してください：
-
-```python
-# モデルとトークナイザーの両方を保存
-tokenizer = AutoTokenizer.from_pretrained("base_model_name")
-merged_model.save_pretrained("path/to/save/merged_model")
-tokenizer.save_pretrained("path/to/save/merged_model")
-```
-
-## 次のステップ
-
-⏩ [プロンプトチューニング](prompt_tuning.md)ガイドに進み、プロンプトチューニングでモデルを微調整する方法を学びましょう。
-⏩ [LoRAアダプタの読み込みチュートリアル](./notebooks/load_lora_adapter.ipynb)に進み、LoRAアダプタを読み込む方法を学びましょう。
-
-# リソース
-
-- [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/pdf/2106.09685)
-- [PEFTドキュメント](https://huggingface.co/docs/peft)
-- [Hugging FaceのPEFTに関するブログ記事](https://huggingface.co/blog/peft)
diff --git a/ja/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb b/ja/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
deleted file mode 100644
index 8fea097a..00000000
--- a/ja/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
+++ /dev/null
@@ -1,516 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "z-6LLOPZouLg"
-   },
-   "source": [
-    "# Hugging Face TRLを使用してLoRAアダプタでLLMを微調整する方法\n",
-    "\n",
-    "このノートブックでは、LoRA（低ランク適応）アダプタを使用して大規模言語モデルを効率的に微調整する方法を示します。LoRAは、次のようなパラメータ効率の良い微調整技術です：\n",
-    "- 事前学習されたモデルの重みを固定\n",
-    "- 注意層に小さな学習可能なランク分解行列を追加\n",
-    "- 通常、学習可能なパラメータを約90％削減\n",
-    "- メモリ効率を維持しながらモデル性能を維持\n",
-    "\n",
-    "以下の内容をカバーします：\n",
-    "1. 開発環境のセットアップとLoRA設定\n",
-    "2. アダプタトレーニング用のデータセットの作成と準備\n",
-    "3. `trl`と`SFTTrainer`を使用してLoRAアダプタで微調整\n",
-    "4. モデルのテストとアダプタの統合（オプション）\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "fXqd9BXgouLi"
-   },
-   "source": [
-    "## 1. 開発環境のセットアップ\n",
-    "\n",
-    "最初のステップは、Hugging FaceライブラリとPytorchをインストールすることです。`trl`、`transformers`、`datasets`を含みます。`trl`について聞いたことがない場合でも心配ありません。これは`transformers`と`datasets`の上に構築された新しいライブラリで、微調整、RLHF、オープンLLMの調整を容易にします。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "tKvGVxImouLi"
-   },
-   "outputs": [],
-   "source": [
-    "# Google Colabでの要件のインストール\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "# Hugging Faceへの認証\n",
-    "\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()\n",
-    "\n",
-    "# 便利のため、Hugging Faceのトークンを環境変数HF_TOKENとして設定できます"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "XHUzfwpKouLk"
-   },
-   "source": [
-    "## 2. データセットの読み込み"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "id": "z4p6Bvo7ouLk"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['full_topic', 'messages'],\n",
-       "        num_rows: 2260\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['full_topic', 'messages'],\n",
-       "        num_rows: 119\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# サンプルデータセットの読み込み\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "# TODO: パスと名前のパラメータを使用してデータセットと設定を定義\n",
-    "dataset = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")\n",
-    "dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "9TOhJdtsouLk"
-   },
-   "source": [
-    "## 3. `trl`と`SFTTrainer`を使用してLLMをLoRAで微調整\n",
-    "\n",
-    "`trl`の[SFTTrainer](https://huggingface.co/docs/trl/sft_trainer)は、[PEFT](https://huggingface.co/docs/peft/en/index)ライブラリを通じてLoRAアダプタとの統合を提供します。このセットアップの主な利点は次のとおりです：\n",
-    "\n",
-    "1. **メモリ効率**：\n",
-    "   - アダプタパラメータのみがGPUメモリに保存されます\n",
-    "   - ベースモデルの重みは固定され、低精度で読み込むことができます\n",
-    "   - 大規模モデルの消費者向けGPUでの微調整が可能\n",
-    "\n",
-    "2. **トレーニング機能**：\n",
-    "   - 最小限のセットアップでネイティブPEFT/LoRA統合\n",
-    "   - さらにメモリ効率を向上させるためのQLoRA（量子化LoRA）サポート\n",
-    "\n",
-    "3. **アダプタ管理**：\n",
-    "   - チェックポイント中のアダプタ重みの保存\n",
-    "   - ベースモデルにアダプタを統合する機能\n",
-    "\n",
-    "例としてLoRAを使用します。これは、LoRAと4ビット量子化を組み合わせて、性能を犠牲にせずにメモリ使用量をさらに削減します。セットアップには次の手順が必要です：\n",
-    "1. LoRA設定（ランク、アルファ、ドロップアウト）を定義\n",
-    "2. PEFT設定でSFTTrainerを作成\n",
-    "3. アダプタ重みをトレーニングして保存\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 必要なライブラリをインポート\n",
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "from datasets import load_dataset\n",
-    "from trl import SFTConfig, SFTTrainer, setup_chat_format\n",
-    "import torch\n",
-    "\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "# モデルとトークナイザーを読み込む\n",
-    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
-    "\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=model_name\n",
-    ").to(device)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
-    "\n",
-    "# チャット形式を設定\n",
-    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n",
-    "\n",
-    "# 微調整の名前を設定\n",
-    "finetune_name = \"SmolLM2-FT-MyDataset\"\n",
-    "finetune_tags = [\"smol-course\", \"module_1\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ZbuVArTHouLk"
-   },
-   "source": [
-    "`SFTTrainer`は、`peft`とのネイティブ統合をサポートしており、例えばLoRAを使用してLLMを効率的に微調整するのが非常に簡単です。`LoraConfig`を作成し、トレーナーに提供するだけです。\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>演習: 微調整のためのLoRAパラメータを定義</h2>\n",
-    "    <p>Hugging Faceのハブからデータセットを取得し、それを使用してモデルを微調整します。</p>\n",
-    "    <p><b>難易度レベル</b></p>\n",
-    "    <p>🐢 一般的なパラメータを使用して任意の微調整を行う</p>\n",
-    "    <p>🐕 パラメータを調整し、重みとバイアスでレビューする</p>\n",
-    "    <p>🦁 パラメータを調整し、推論結果の変化を示す</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "blDSs9swouLk"
-   },
-   "outputs": [],
-   "source": [
-    "from peft import LoraConfig\n",
-    "\n",
-    "# TODO: LoRAパラメータを設定\n",
-    "# r: LoRA更新行列のランク次元（小さいほど圧縮率が高い）\n",
-    "rank_dimension = 6\n",
-    "# lora_alpha: LoRA層のスケーリングファクター（高いほど適応が強い）\n",
-    "lora_alpha = 8\n",
-    "# lora_dropout: LoRA層のドロップアウト確率（過学習を防ぐのに役立つ）\n",
-    "lora_dropout = 0.05\n",
-    "\n",
-    "peft_config = LoraConfig(\n",
-    "    r=rank_dimension,  # ランク次元 - 通常4-32の範囲\n",
-    "    lora_alpha=lora_alpha,  # LoRAスケーリングファクター - 通常ランクの2倍\n",
-    "    lora_dropout=lora_dropout,  # LoRA層のドロップアウト確率\n",
-    "    bias=\"none\",  # LoRAのバイアスタイプ。対応するバイアスはトレーニング中に更新されます。\n",
-    "    target_modules=\"all-linear\",  # LoRAを適用するモジュール\n",
-    "    task_type=\"CAUSAL_LM\",  # モデルアーキテクチャのタスクタイプ\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "l5NUDPcaouLl"
-   },
-   "source": [
-    "トレーニングを開始する前に、使用するハイパーパラメータ（`TrainingArguments`）を定義する必要があります。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "NqT28VZlouLl"
-   },
-   "outputs": [],
-   "source": [
-    "# トレーニング設定\n",
-    "# QLoRA論文の推奨に基づくハイパーパラメータ\n",
-    "args = SFTConfig(\n",
-    "    # 出力設定\n",
-    "    output_dir=finetune_name,  # モデルチェックポイントを保存するディレクトリ\n",
-    "    # トレーニング期間\n",
-    "    num_train_epochs=1,  # トレーニングエポック数\n",
-    "    # バッチサイズ設定\n",
-    "    per_device_train_batch_size=2,  # GPUごとのバッチサイズ\n",
-    "    gradient_accumulation_steps=2,  # 大きな効果的なバッチのための勾配蓄積\n",
-    "    # メモリ最適化\n",
-    "    gradient_checkpointing=True,  # メモリ節約のための計算トレードオフ\n",
-    "    # オプティマイザ設定\n",
-    "    optim=\"adamw_torch_fused\",  # 効率のために融合されたAdamWを使用\n",
-    "    learning_rate=2e-4,  # 学習率（QLoRA論文）\n",
-    "    max_grad_norm=0.3,  # 勾配クリッピングの閾値\n",
-    "    # 学習率スケジュール\n",
-    "    warmup_ratio=0.03,  # ウォームアップのステップの割合\n",
-    "    lr_scheduler_type=\"constant\",  # ウォームアップ後に学習率を一定に保つ\n",
-    "    # ロギングと保存\n",
-    "    logging_steps=10,  # Nステップごとにメトリックをログ\n",
-    "    save_strategy=\"epoch\",  # 各エポックごとにチェックポイントを保存\n",
-    "    # 精度設定\n",
-    "    bf16=True,  # bfloat16精度を使用\n",
-    "    # 統合設定\n",
-    "    push_to_hub=False,  # HuggingFace Hubにプッシュしない\n",
-    "    report_to=\"none\",  # 外部ロギングを無効化\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "cGhR7uFBouLl"
-   },
-   "source": [
-    "すべてのビルディングブロックが揃ったので、`SFTTrainer`を作成してモデルのトレーニングを開始します。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "M00Har2douLl"
-   },
-   "outputs": [],
-   "source": [
-    "max_seq_length = 1512  # モデルとデータセットのパッキングの最大シーケンス長\n",
-    "\n",
-    "# LoRA設定でSFTTrainerを作成\n",
-    "trainer = SFTTrainer(\n",
-    "    model=model,\n",
-    "    args=args,\n",
-    "    train_dataset=dataset[\"train\"],\n",
-    "    peft_config=peft_config,  # LoRA設定\n",
-    "    max_seq_length=max_seq_length,  # 最大シーケンス長\n",
-    "    tokenizer=tokenizer,\n",
-    "    packing=True,  # 効率のために入力パッキングを有効化\n",
-    "    dataset_kwargs={\n",
-    "        \"add_special_tokens\": False,  # テンプレートで処理される特殊トークン\n",
-    "        \"append_concat_token\": False,  # 追加のセパレータは不要\n",
-    "    },\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "zQ_kRN24ouLl"
-   },
-   "source": [
-    "トレーニングを開始するには、`Trainer`インスタンスの`train()`メソッドを呼び出します。これにより、トレーニングループが開始され、モデルが3エポックにわたってトレーニングされます。PEFTメソッドを使用しているため、適応されたモデルの重みのみを保存し、完全なモデルは保存しません。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Tq4nIYqKouLl"
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "300e5dfbb4b54750b77324345c7591f9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/72 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "TrainOutput(global_step=72, training_loss=1.6402628521124523, metrics={'train_runtime': 195.2398, 'train_samples_per_second': 1.485, 'train_steps_per_second': 0.369, 'total_flos': 282267289092096.0, 'train_loss': 1.6402628521124523, 'epoch': 0.993103448275862})"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# トレーニングを開始し、モデルは自動的にハブと出力ディレクトリに保存されます\n",
-    "trainer.train()\n",
-    "\n",
-    "# モデルを保存\n",
-    "trainer.save_model()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "y4HHSYYzouLl"
-   },
-   "source": [
-    "Flash Attentionを使用して15kサンプルのデータセットで3エポックのトレーニングを行った結果、`g5.2xlarge`で4:14:36かかりました。このインスタンスのコストは`1.21$/h`で、合計コストは約`5.3$`です。\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "C309KsXjouLl"
-   },
-   "source": [
-    "### LoRAアダプタを元のモデルに統合\n",
-    "\n",
-    "LoRAを使用する場合、トレーニング中はアダプタ重みのみを学習し、ベースモデルは固定されます。トレーニング中は、これらの軽量なアダプタ重み（約2-10MB）のみを保存し、完全なモデルのコピーは保存しません。ただし、デプロイのためにアダプタをベースモデルに統合することを検討するかもしれません：\n",
-    "\n",
-    "1. **デプロイの簡素化**：ベースモデル+アダプタの代わりに単一のモデルファイル\n",
-    "2. **推論速度**：アダプタ計算のオーバーヘッドなし\n",
-    "3. **フレームワークの互換性**：サービングフレームワークとの互換性向上\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from peft import AutoPeftModelForCausalLM\n",
-    "\n",
-    "\n",
-    "# CPU上でPEFTモデルを読み込む\n",
-    "model = AutoPeftModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=args.output_dir,\n",
-    "    torch_dtype=torch.float16,\n",
-    "    low_cpu_mem_usage=True,\n",
-    ")\n",
-    "\n",
-    "# LoRAとベースモデルを統合して保存\n",
-    "merged_model = model.merge_and_unload()\n",
-    "merged_model.save_pretrained(\n",
-    "    args.output_dir, safe_serialization=True, max_shard_size=\"2GB\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "-yO6E9quouLl"
-   },
-   "source": [
-    "## 3. モデルをテストして推論を実行\n",
-    "\n",
-    "トレーニングが完了したら、モデルをテストしたいと思います。元のデータセットから異なるサンプルを読み込み、単純なループと精度をメトリックとして使用してモデルを評価します。\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>ボーナス演習: LoRAアダプタの読み込み</h2>\n",
-    "    <p>例のノートブックから学んだことを使用して、トレーニングされたLoRAアダプタを推論のために読み込みます。</p> \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "id": "I5B494OdouLl"
-   },
-   "outputs": [],
-   "source": [
-    "# メモリを再度解放\n",
-    "del model\n",
-    "del trainer\n",
-    "torch.cuda.empty_cache()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "P1UhohVdouLl"
-   },
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from peft import AutoPeftModelForCausalLM\n",
-    "from transformers import AutoTokenizer, pipeline\n",
-    "\n",
-    "# PEFTアダプタでモデルを読み込む\n",
-    "tokenizer = AutoTokenizer.from_pretrained(finetune_name)\n",
-    "model = AutoPeftModelForCausalLM.from_pretrained(\n",
-    "    finetune_name, device_map=\"auto\", torch_dtype=torch.float16\n",
-    ")\n",
-    "pipe = pipeline(\n",
-    "    \"text-generation\", model=merged_model, tokenizer=tokenizer, device=device\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "99uFDAuuouLl"
-   },
-   "source": [
-    "いくつかのプロンプトサンプルをテストし、モデルの性能を確認しましょう。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "id": "-shSmUbvouLl",
-    "outputId": "16d97c61-3b31-4040-c780-3c4de75c3824"
-   },
-   "outputs": [],
-   "source": [
-    "prompts = [\n",
-    "    \"ドイツの首都はどこですか？その理由と過去に異なっていたかどうかを説明してください。\",\n",
-    "    \"数の階乗を計算するPython関数を書いてください。\",\n",
-    "    \"長さ25フィート、幅15フィートの長方形の庭があります。庭全体を囲むフェンスを作りたい場合、何フィートのフェンスが必要ですか？\",\n",
-    "    \"果物と野菜の違いは何ですか？それぞれの例を挙げてください。\",\n",
-    "]\n",
-    "\n",
-    "\n",
-    "def test_inference(prompt):\n",
-    "    prompt = pipe.tokenizer.apply_chat_template(\n",
-    "        [{\"role\": \"user\", \"content\": prompt}],\n",
-    "        tokenize=False,\n",
-    "        add_generation_prompt=True,\n",
-    "    )\n",
-    "    outputs = pipe(\n",
-    "        prompt,\n",
-    "    )\n",
-    "    return outputs[0][\"generated_text\"][len(prompt) :].strip()\n",
-    "\n",
-    "\n",
-    "for prompt in prompts:\n",
-    "    print(f\"    prompt:\\n{prompt}\")\n",
-    "    print(f\"    response:\\n{test_inference(prompt)}\")\n",
-    "    print(\"-\" * 50)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.10"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/ja/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb b/ja/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
deleted file mode 100644
index 6ef9dd8c..00000000
--- a/ja/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
+++ /dev/null
@@ -1,161 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "z-6LLOPZouLg"
-   },
-   "source": [
-    "# LoRAアダプタの読み込み\n",
-    "\n",
-    "このノートブックでは、事前学習されたモデルにLoRAアダプタを読み込む方法を示します。LoRA（低ランク適応）は、事前学習されたモデルの重みを固定し、学習可能なランク分解行列をモデルの層に注入するパラメータ効率の良い微調整技術です。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "fXqd9BXgouLi"
-   },
-   "source": [
-    "## 1. 開発環境のセットアップ\n",
-    "\n",
-    "最初のステップは、Hugging FaceライブラリとPytorchをインストールすることです。`trl`、`transformers`、`datasets`を含みます。`trl`について聞いたことがない場合でも心配ありません。これは`transformers`と`datasets`の上に構築された新しいライブラリで、微調整、RLHF、オープンLLMの調整を容易にします。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "tKvGVxImouLi"
-   },
-   "outputs": [],
-   "source": [
-    "# Google Colabでの要件のインストール\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "# Hugging Faceへの認証\n",
-    "\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()\n",
-    "\n",
-    "# 便利のため、Hugging Faceのトークンを環境変数HF_TOKENとして設定できます"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "XHUzfwpKouLk"
-   },
-   "source": [
-    "## 2. モデルとアダプタの読み込み"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "id": "z4p6Bvo7ouLk"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Downloading (…)okenizer_config.json: 100%|██████████| 28.0/28.0 [00:00<00:00, 28.0kB/s]\n",
-       "Downloading (…)lve/main/config.json: 100%|██████████| 1.11k/1.11k [00:00<00:00, 1.11MB/s]\n",
-       "Downloading (…)olve/main/vocab.json: 100%|██████████| 1.06M/1.06M [00:00<00:00, 1.06MB/s]\n",
-       "Downloading (…)olve/main/merges.txt: 100%|██████████| 525k/525k [00:00<00:00, 525kB/s]\n",
-       "Downloading (…)/main/tokenizer.json: 100%|██████████| 2.13M/2.13M [00:00<00:00, 2.13MB/s]\n",
-       "Downloading (…)/main/adapter_config.json: 100%|██████████| 472/472 [00:00<00:00, 472kB/s]\n",
-       "Downloading (…)/adapter_model.bin: 100%|██████████| 1.22G/1.22G [00:10<00:00, 120MB/s]\n",
-       "Downloading pytorch_model.bin: 100%|██████████| 1.22G/1.22G [00:10<00:00, 120MB/s]\n"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# モデルとアダプタの読み込み\n",
-    "from transformers import AutoModelForCausalLM\n",
-    "from peft import PeftModel\n",
-    "\n",
-    "base_model = AutoModelForCausalLM.from_pretrained(\"HuggingFaceTB/SmolLM2-135M\")\n",
-    "peft_model_id = \"HuggingFaceTB/SmolLM2-135M-LoRA\"\n",
-    "model = PeftModel.from_pretrained(base_model, peft_model_id)\n",
-    "model.eval()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "9TOhJdtsouLk"
-   },
-   "source": [
-    "## 3. モデルのテスト\n",
-    "\n",
-    "モデルが正しく読み込まれたことを確認するために、いくつかのサンプル入力でテストします。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "id": "z4p6Bvo7ouLk"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Downloading (…)okenizer_config.json: 100%|██████████| 28.0/28.0 [00:00<00:00, 28.0kB/s]\n",
-       "Downloading (…)lve/main/config.json: 100%|██████████| 1.11k/1.11k [00:00<00:00, 1.11MB/s]\n",
-       "Downloading (…)olve/main/vocab.json: 100%|██████████| 1.06M/1.06M [00:00<00:00, 1.06MB/s]\n",
-       "Downloading (…)olve/main/merges.txt: 100%|██████████| 525k/525k [00:00<00:00, 525kB/s]\n",
-       "Downloading (…)/main/tokenizer.json: 100%|██████████| 2.13M/2.13M [00:00<00:00, 2.13MB/s]\n"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# トークナイザーの読み込み\n",
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"HuggingFaceTB/SmolLM2-135M\")\n",
-    "\n",
-    "# サンプル入力の生成\n",
-    "inputs = tokenizer(\"こんにちは、元気ですか？\", return_tensors=\"pt\")\n",
-    "outputs = model.generate(**inputs, max_new_tokens=50)\n",
-    "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.10"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/ja/3_parameter_efficient_finetuning/prompt_tuning.md b/ja/3_parameter_efficient_finetuning/prompt_tuning.md
deleted file mode 100644
index 2676bbf6..00000000
--- a/ja/3_parameter_efficient_finetuning/prompt_tuning.md
+++ /dev/null
@@ -1,74 +0,0 @@
-# プロンプトチューニング
-
-プロンプトチューニングは、モデルの重みではなく入力表現を変更するパラメータ効率の良いアプローチです。従来の微調整がすべてのモデルパラメータを更新するのに対し、プロンプトチューニングはベースモデルを固定したまま、少数の学習可能なトークンを追加して最適化します。
-
-## プロンプトチューニングの理解
-
-プロンプトチューニングは、学習可能な連続ベクトル（ソフトプロンプト）を入力テキストの前に追加するパラメータ効率の良い微調整方法です。従来の離散テキストプロンプトとは異なり、これらのソフトプロンプトはベースモデルを固定したまま、逆伝播を通じて学習されます。この方法は、["The Power of Scale for Parameter-Efficient Prompt Tuning"](https://arxiv.org/abs/2104.08691)（Lester et al., 2021）で紹介され、モデルサイズが大きくなるにつれてプロンプトチューニングがモデル微調整と競争力を持つようになることが示されました。論文内では、約100億パラメータのモデルで、プロンプトチューニングがモデル微調整の性能に匹敵し、タスクごとに数百のパラメータしか変更しないことが示されています。
-
-これらのソフトプロンプトは、トレーニング中に最適化されるモデルの埋め込み空間内の連続ベクトルです。従来の自然言語トークンを使用する離散プロンプトとは異なり、ソフトプロンプトは固有の意味を持たず、勾配降下を通じて固定モデルから望ましい動作を引き出すことを学習します。この技術は、各タスクに対して小さなプロンプトベクトル（通常は数百パラメータ）を保存するだけで済むため、マルチタスクシナリオに特に効果的です。このアプローチは、最小限のメモリフットプリントを維持するだけでなく、モデルの再読み込みなしにプロンプトベクトルを交換するだけで迅速なタスク切り替えを可能にします。
-
-## トレーニングプロセス
-
-ソフトプロンプトは通常、8〜32トークンで構成され、ランダムに初期化するか、既存のテキストから初期化されます。初期化方法はトレーニングプロセスにおいて重要な役割を果たし、テキストベースの初期化はランダム初期化よりも優れた性能を発揮することがよくあります。
-
-トレーニング中は、プロンプトパラメータのみが更新され、ベースモデルは固定されたままです。この集中アプローチは標準的なトレーニング目標を使用しますが、プロンプトトークンの学習率と勾配の挙動に注意を払う必要があります。
-
-## PEFTを使用した実装
-
-PEFTライブラリを使用すると、プロンプトチューニングの実装が簡単になります。以下は基本的な例です：
-
-```python
-from peft import PromptTuningConfig, TaskType, get_peft_model
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-# ベースモデルを読み込む
-model = AutoModelForCausalLM.from_pretrained("your-base-model")
-tokenizer = AutoTokenizer.from_pretrained("your-base-model")
-
-# プロンプトチューニングを設定
-peft_config = PromptTuningConfig(
-    task_type=TaskType.CAUSAL_LM,
-    num_virtual_tokens=8,  # 学習可能なトークンの数
-    prompt_tuning_init="TEXT",  # テキストから初期化
-    prompt_tuning_init_text="このテキストがポジティブかネガティブかを分類してください：",
-    tokenizer_name_or_path="your-base-model",
-)
-
-# プロンプトチューニング可能なモデルを作成
-model = get_peft_model(model, peft_config)
-```
-
-## 他の方法との比較
-
-他のPEFTアプローチと比較すると、プロンプトチューニングはその効率性で際立っています。LoRAは低パラメータ数とメモリ使用量を提供しますが、タスク切り替えにはアダプタの読み込みが必要です。プロンプトチューニングはさらに低いリソース使用量を達成し、即時のタスク切り替えを可能にします。対照的に、フルファインチューニングは多くのリソースを必要とし、異なるタスクごとに別々のモデルコピーが必要です。
-
-| 方法 | パラメータ | メモリ | タスク切り替え |
-|--------|------------|---------|----------------|
-| プロンプトチューニング | 非常に低い | 最小限 | 簡単 |
-| LoRA | 低い | 低い | 読み込みが必要 |
-| フルファインチューニング | 高い | 高い | 新しいモデルコピー |
-
-プロンプトチューニングを実装する際は、最初に少数の仮想トークン（8〜16）を使用し、タスクの複雑さが要求する場合にのみ増やします。タスクに関連するテキストを使用したテキスト初期化は、ランダム初期化よりも優れた結果をもたらすことがよくあります。初期化戦略は、ターゲットタスクの複雑さを反映する必要があります。
-
-トレーニングには、フルファインチューニングとは異なる考慮事項が必要です。高い学習率が効果的なことが多いですが、プロンプトトークンの勾配を注意深く監視することが重要です。多様な例で定期的に検証することで、さまざまなシナリオでの堅牢な性能を確保します。
-
-## 応用
-
-プロンプトチューニングは、次のようなシナリオで優れた効果を発揮します：
-
-1. マルチタスク展開
-2. リソース制約のある環境
-3. 迅速なタスク適応
-4. プライバシーに敏感なアプリケーション
-
-モデルが小さくなるにつれて、プロンプトチューニングはフルファインチューニングと比較して競争力が低下します。例えば、SmolLM2のようなモデルでは、プロンプトチューニングはフルファインチューニングよりも関連性が低くなります。
-
-## 次のステップ
-
-⏭️ [LoRAアダプタのチュートリアル](./notebooks/finetune_sft_peft.ipynb)に進み、LoRAアダプタでモデルを微調整する方法を学びましょう。
-
-## リソース
-- [PEFTドキュメント](https://huggingface.co/docs/peft)
-- [プロンプトチューニング論文](https://arxiv.org/abs/2104.08691)
-- [Hugging Face Cookbook](https://huggingface.co/learn/cookbook/prompt_tuning_peft)
diff --git a/ja/4_evaluation/README.md b/ja/4_evaluation/README.md
deleted file mode 100644
index bb3bfafd..00000000
--- a/ja/4_evaluation/README.md
+++ /dev/null
@@ -1,39 +0,0 @@
-# 評価
-
-評価は、言語モデルの開発と展開において重要なステップです。評価は、モデルがさまざまな能力にわたってどれだけうまく機能するかを理解し、改善の余地を特定するのに役立ちます。このモジュールでは、標準ベンチマークとドメイン固有の評価アプローチの両方をカバーし、smolモデルを包括的に評価します。
-
-Hugging Faceが開発した強力な評価ライブラリである[`lighteval`](https://github.com/huggingface/lighteval)を使用します。評価の概念とベストプラクティスについて詳しく知りたい場合は、評価[ガイドブック](https://github.com/huggingface/evaluation-guidebook)を参照してください。
-
-## モジュール概要
-
-徹底した評価戦略は、モデル性能の複数の側面を検討します。質問応答や要約などのタスク固有の能力を評価し、モデルがさまざまなタイプの問題にどれだけうまく対処できるかを理解します。出力の品質を一貫性や事実の正確性などの要素で測定します。安全性評価は、潜在的な有害な出力やバイアスを特定するのに役立ちます。最後に、ドメインの専門知識テストは、ターゲット分野でのモデルの専門知識を検証します。
-
-## コンテンツ
-
-### 1️⃣ [自動ベンチマーク](./automatic_benchmarks.md)
-
-標準化されたベンチマークとメトリクスを使用してモデルを評価する方法を学びます。MMLUやTruthfulQAなどの一般的なベンチマークを探求し、主要な評価メトリクスと設定を理解し、再現可能な評価のベストプラクティスをカバーします。
-
-### 2️⃣ [カスタムドメイン評価](./custom_evaluation.md)
-
-特定のユースケースに合わせた評価パイプラインを作成する方法を学びます。カスタム評価タスクの設計、専門的なメトリクスの実装、要件に合った評価データセットの構築について説明します。
-
-### 3️⃣ [ドメイン評価プロジェクト](./project/README.md)
-
-ドメイン固有の評価パイプラインを構築する完全な例を紹介します。評価データセットの生成、データ注釈のためのArgillaの使用、標準化されたデータセットの作成、LightEvalを使用したモデルの評価方法を学びます。
-
-### 演習ノートブック
-
-| タイトル | 説明 | 演習 | リンク | Colab |
-|-------|-------------|----------|------|-------|
-| LLMの評価と分析 | LightEvalを使用して特定のドメインでモデルを評価および比較する方法を学びます | 🐢 医療ドメインタスクを使用してモデルを評価する <br> 🐕 異なるMMLUタスクで新しいドメイン評価を作成する <br> 🦁 ドメイン固有のカスタム評価タスクを作成する | [ノートブック](./notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-
-## リソース
-
-- [評価ガイドブック](https://github.com/huggingface/evaluation-guidebook) - LLM評価の包括的なガイド
-- [LightEvalドキュメント](https://github.com/huggingface/lighteval) - LightEvalライブラリの公式ドキュメント
-- [Argillaドキュメント](https://docs.argilla.io) - Argillaアノテーションプラットフォームについて学ぶ
-- [MMLU論文](https://arxiv.org/abs/2009.03300) - MMLUベンチマークを説明する論文
-- [カスタムタスクの作成](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
-- [カスタムメトリクスの作成](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
-- [既存のメトリクスの使用](https://github.com/huggingface/lighteval/wiki/Metric-List)
diff --git a/ja/4_evaluation/automatic_benchmarks.md b/ja/4_evaluation/automatic_benchmarks.md
deleted file mode 100644
index 81df5393..00000000
--- a/ja/4_evaluation/automatic_benchmarks.md
+++ /dev/null
@@ -1,131 +0,0 @@
-# 自動ベンチマーク
-
-自動ベンチマークは、さまざまなタスクや能力にわたって言語モデルを評価するための標準化されたツールとして機能します。これらはモデルの性能を理解するための有用な出発点を提供しますが、包括的な評価戦略の一部に過ぎないことを認識することが重要です。
-
-## 自動ベンチマークの理解
-
-自動ベンチマークは通常、事前に定義されたタスクと評価指標を持つキュレーションされたデータセットで構成されます。これらのベンチマークは、基本的な言語理解から複雑な推論まで、モデルのさまざまな側面を評価することを目的としています。自動ベンチマークの主な利点はその標準化にあります。これにより、異なるモデル間で一貫した比較が可能となり、再現性のある結果が得られます。
-
-ただし、ベンチマークの性能が必ずしも実世界での有効性に直結するわけではないことを理解することが重要です。学術的なベンチマークで優れた成績を収めたモデルでも、特定のドメインアプリケーションや実際のユースケースでは苦労することがあります。
-
-## ベンチマークとその限界
-
-### 一般知識ベンチマーク
-
-MMLU（Massive Multitask Language Understanding）は、科学から人文科学まで57の科目にわたる知識をテストします。包括的ではありますが、特定のドメインに必要な専門知識の深さを反映しているわけではありません。TruthfulQAは、モデルが一般的な誤解を再現する傾向を評価しますが、すべての形態の誤情報を捉えることはできません。
-
-### 推論ベンチマーク
-BBH（Big Bench Hard）とGSM8Kは、複雑な推論タスクに焦点を当てています。BBHは論理的思考と計画をテストし、GSM8Kは特に数学的な問題解決を対象としています。これらのベンチマークは分析能力を評価するのに役立ちますが、実世界のシナリオで必要とされる微妙な推論を完全に捉えることはできません。
-
-### 言語理解
-HELMは包括的な評価フレームワークを提供し、WinoGrandeは代名詞の曖昧性解消を通じて常識をテストします。これらのベンチマークは言語処理能力に関する洞察を提供しますが、自然な会話やドメイン固有の用語の複雑さを完全に表現することはできません。
-
-## 代替評価アプローチ
-
-多くの組織は、標準ベンチマークの限界に対処するために代替評価方法を開発しています：
-
-### LLM-as-Judge
-ある言語モデルを使用して別のモデルの出力を評価する方法がますます人気を集めています。このアプローチは、従来の指標よりも微妙なフィードバックを提供することができますが、それ自体のバイアスと限界も伴います。
-
-### 評価アリーナ
-AnthropicのConstitutional AI Arenaのようなプラットフォームでは、モデルが相互に対話し、制御された環境で互いを評価することができます。これにより、従来のベンチマークでは明らかにならない強みと弱みが明らかになります。
-
-### カスタムベンチマークスイート
-組織は、特定のニーズやユースケースに合わせた内部ベンチマークスイートを開発することがよくあります。これには、ドメイン固有の知識テストや実際の展開条件を反映した評価シナリオが含まれることがあります。
-
-## 独自の評価戦略の作成
-
-LightEvalを使用して標準ベンチマークを実行するのは簡単ですが、ユースケースに特化した評価方法の開発にも時間を投資する必要があります。
-
-標準ベンチマークは有用なベースラインを提供しますが、それだけが評価方法であってはなりません。より包括的なアプローチを開発する方法は次のとおりです：
-
-1. 関連する標準ベンチマークから始めて、ベースラインを確立し、他のモデルとの比較を可能にします。
-
-2. ユースケースの特定の要件と課題を特定します。モデルが実際に実行するタスクは何ですか？どのようなエラーが最も問題になりますか？
-
-3. 実際のユースケースを反映したカスタム評価データセットを開発します。これには次のようなものが含まれるかもしれません：
-   - ドメインからの実際のユーザークエリ
-   - 遭遇した一般的なエッジケース
-   - 特に挑戦的なシナリオの例
-
-4. 多層評価戦略の実装を検討します：
-   - クイックフィードバックのための自動指標
-   - 微妙な理解のための人間の評価
-   - 専門家によるレビュー
-   - 制御された環境でのA/Bテスト
-
-## LightEvalを使用したベンチマーク
-
-LightEvalタスクは特定の形式を使用して定義されます：
-```
-{suite}|{task}|{num_few_shot}|{auto_reduce}
-```
-
-- **suite**: ベンチマークスイート（例：'mmlu', 'truthfulqa'）
-- **task**: スイート内の特定のタスク（例：'abstract_algebra'）
-- **num_few_shot**: プロンプトに含める例の数（ゼロショットの場合は0）
-- **auto_reduce**: プロンプトが長すぎる場合に少数ショットの例を自動的に削減するかどうか（0または1）
-
-例：`"mmlu|abstract_algebra|0|0"`は、ゼロショット推論でMMLUの抽象代数学タスクを評価します。
-
-### 評価パイプラインの例
-
-特定のドメインに関連する自動ベンチマークの評価を設定して実行する完全な例を次に示します：
-
-```python
-from lighteval.tasks import Task, Pipeline
-from transformers import AutoModelForCausalLM
-
-# 評価するタスクを定義
-domain_tasks = [
-    "mmlu|anatomy|0|0",
-    "mmlu|high_school_biology|0|0", 
-    "mmlu|high_school_chemistry|0|0",
-    "mmlu|professional_medicine|0|0"
-]
-
-# パイプラインパラメータを設定
-pipeline_params = {
-    "max_samples": 40,  # 評価するサンプル数
-    "batch_size": 1,    # 推論のバッチサイズ
-    "num_workers": 4    # ワーカープロセスの数
-}
-
-# 評価トラッカーを作成
-evaluation_tracker = EvaluationTracker(
-    output_path="./results",
-    save_generations=True
-)
-
-# モデルを読み込み、パイプラインを作成
-model = AutoModelForCausalLM.from_pretrained("your-model-name")
-pipeline = Pipeline(
-    tasks=domain_tasks,
-    pipeline_parameters=pipeline_params,
-    evaluation_tracker=evaluation_tracker,
-    model=model
-)
-
-# 評価を実行
-pipeline.evaluate()
-
-# 結果を取得して表示
-results = pipeline.get_results()
-pipeline.show_results()
-```
-
-結果は次のような表形式で表示されます：
-```
-|                  Task                  |Version|Metric|Value |   |Stderr|
-|----------------------------------------|------:|------|-----:|---|-----:|
-|all                                     |       |acc   |0.3333|±  |0.1169|
-|leaderboard:mmlu:_average:5             |       |acc   |0.3400|±  |0.1121|
-|leaderboard:mmlu:anatomy:5              |      0|acc   |0.4500|±  |0.1141|
-|leaderboard:mmlu:high_school_biology:5  |      0|acc   |0.1500|±  |0.0819|
-```
-
-結果をpandas DataFrameで処理し、視覚化や表現を自由に行うこともできます。
-
-# 次のステップ
-
-⏩ [カスタムドメイン評価](./custom_evaluation.md)を探索し、特定のニーズに合わせた評価パイプラインの作成方法を学びましょう
diff --git a/ja/4_evaluation/custom_evaluation.md b/ja/4_evaluation/custom_evaluation.md
deleted file mode 100644
index 4dc41642..00000000
--- a/ja/4_evaluation/custom_evaluation.md
+++ /dev/null
@@ -1,132 +0,0 @@
-# カスタムドメイン評価
-
-標準ベンチマークは貴重な洞察を提供しますが、多くのアプリケーションでは特定のドメインやユースケースに合わせた評価アプローチが必要です。このガイドでは、ターゲットドメインでモデルの性能を正確に評価するためのカスタム評価パイプラインを作成する方法を説明します。
-
-## 評価戦略の設計
-
-成功するカスタム評価戦略は、明確な目標から始まります。ドメインでモデルが示すべき具体的な能力を考慮してください。これには、技術的な知識、推論パターン、ドメイン固有の形式が含まれるかもしれません。これらの要件を慎重に文書化してください。これがタスク設計とメトリック選択の両方を導きます。
-
-評価は、標準的なユースケースとエッジケースの両方をテストする必要があります。例えば、医療ドメインでは、一般的な診断シナリオと稀な状態の両方を評価するかもしれません。金融アプリケーションでは、通常の取引と複数の通貨や特別な条件を含む複雑なエッジケースの両方をテストするかもしれません。
-
-## LightEvalを使用した実装
-
-LightEvalは、カスタム評価を実装するための柔軟なフレームワークを提供します。カスタムタスクを作成する方法は次のとおりです：
-
-```python
-from lighteval.tasks import Task, Doc
-from lighteval.metrics import SampleLevelMetric, MetricCategory, MetricUseCase
-
-class CustomEvalTask(Task):
-    def __init__(self):
-        super().__init__(
-            name="custom_task",
-            version="0.0.1",
-            metrics=["accuracy", "f1"],  # 選択したメトリック
-            description="カスタム評価タスクの説明"
-        )
-    
-    def get_prompt(self, sample):
-        # 入力をプロンプトにフォーマット
-        return f"質問: {sample['question']}\n回答:"
-    
-    def process_response(self, response, ref):
-        # モデルの出力を処理し、参照と比較
-        return response.strip() == ref.strip()
-```
-
-## カスタムメトリック
-
-ドメイン固有のタスクには、専門的なメトリックが必要なことがよくあります。LightEvalは、ドメインに関連する性能の側面を捉えるカスタムメトリックを作成するための柔軟なフレームワークを提供します：
-
-```python
-from aenum import extend_enum
-from lighteval.metrics import Metrics, SampleLevelMetric, SampleLevelMetricGrouping
-import numpy as np
-
-# サンプルレベルのメトリック関数を定義
-def custom_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> dict:
-    """サンプルごとに複数のスコアを返す例のメトリック"""
-    response = predictions[0]
-    return {
-        "accuracy": response == formatted_doc.choices[formatted_doc.gold_index],
-        "length_match": len(response) == len(formatted_doc.reference)
-    }
-
-# サンプルごとに複数の値を返すメトリックを作成
-custom_metric_group = SampleLevelMetricGrouping(
-    metric_name=["accuracy", "length_match"],  # サブメトリックの名前
-    higher_is_better={  # 各メトリックで高い値が良いかどうか
-        "accuracy": True,
-        "length_match": True
-    },
-    category=MetricCategory.CUSTOM,
-    use_case=MetricUseCase.SCORING,
-    sample_level_fn=custom_metric,
-    corpus_level_fn={  # 各メトリックを集計する方法
-        "accuracy": np.mean,
-        "length_match": np.mean
-    }
-)
-
-# LightEvalにメトリックを登録
-extend_enum(Metrics, "custom_metric_name", custom_metric_group)
-```
-
-サンプルごとに1つのメトリック値のみが必要な場合の簡単なケース：
-
-```python
-def simple_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> bool:
-    """サンプルごとに単一のスコアを返す例のメトリック"""
-    response = predictions[0]
-    return response == formatted_doc.choices[formatted_doc.gold_index]
-
-simple_metric_obj = SampleLevelMetric(
-    metric_name="simple_accuracy",
-    higher_is_better=True,
-    category=MetricCategory.CUSTOM,
-    use_case=MetricUseCase.SCORING,
-    sample_level_fn=simple_metric,
-    corpus_level_fn=np.mean  # サンプル全体で集計する方法
-)
-
-extend_enum(Metrics, "simple_metric", simple_metric_obj)
-```
-
-カスタムメトリックを評価タスクで参照することで、評価タスクで自動的に計算され、指定された関数に従って集計されます。
-
-より複雑なメトリックの場合、次のことを検討してください：
-- フォーマットされたドキュメントのメタデータを使用してスコアを重み付けまたは調整
-- コーパスレベルの統計のためのカスタム集計関数の実装
-- メトリック入力の検証チェックの追加
-- エッジケースと期待される動作の文書化
-
-これらの概念を実装する完全な例については、[ドメイン評価プロジェクト](./project/README.md)を参照してください。
-
-## データセットの作成
-
-高品質の評価には、慎重にキュレーションされたデータセットが必要です。データセット作成のアプローチを次に示します：
-
-1. 専門家のアノテーション：ドメインの専門家と協力して評価例を作成および検証します。[Argilla](https://github.com/argilla-io/argilla)のようなツールを使用すると、このプロセスがより効率的になります。
-
-2. 実世界のデータ：実際の使用データを収集し、匿名化して、実際の展開シナリオを反映します。
-
-3. 合成生成：LLMを使用して初期例を生成し、専門家がそれを検証および洗練します。
-
-## ベストプラクティス
-
-- 評価方法論を徹底的に文書化し、仮定や制限を含める
-- ドメインのさまざまな側面をカバーする多様なテストケースを含める
-- 自動メトリックと人間の評価の両方を適用する
-- 評価データセットとコードをバージョン管理する
-- 新しいエッジケースや要件を発見するたびに評価スイートを定期的に更新する
-
-## 参考文献
-
-- [LightEvalカスタムタスクガイド](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
-- [LightEvalカスタムメトリック](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
-- データセットアノテーションのための[Argillaドキュメント](https://docs.argilla.io)
-- 一般的な評価原則のための[評価ガイドブック](https://github.com/huggingface/evaluation-guidebook)
-
-# 次のステップ
-
-⏩ これらの概念を実装する完全な例については、[ドメイン評価プロジェクト](./project/README.md)を参照してください。
diff --git a/ja/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb b/ja/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
deleted file mode 100644
index 3e4f34a9..00000000
--- a/ja/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
+++ /dev/null
@@ -1,210 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "z-6LLOPZouLg"
-   },
-   "source": [
-    "# LightEvalを使用してLLMを評価および分析する方法\n",
-    "\n",
-    "このノートブックでは、LightEvalを使用して大規模言語モデル（LLM）を評価および分析する方法を示します。LightEvalは、さまざまなタスクにわたってモデルの性能を評価するための柔軟で使いやすいフレームワークです。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "fXqd9BXgouLi"
-   },
-   "source": [
-    "## 1. 開発環境のセットアップ\n",
-    "\n",
-    "最初のステップは、必要なライブラリをインストールすることです。`lighteval`、`transformers`、`datasets`を含みます。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "tKvGVxImouLi"
-   },
-   "outputs": [],
-   "source": [
-    "# Google Colabでの要件のインストール\n",
-    "# !pip install lighteval transformers datasets\n",
-    "\n",
-    "# Hugging Faceへの認証\n",
-    "\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()\n",
-    "\n",
-    "# 便利のため、Hugging Faceのトークンを環境変数HF_TOKENとして設定できます"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "XHUzfwpKouLk"
-   },
-   "source": [
-    "## 2. モデルとデータセットの読み込み"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "id": "z4p6Bvo7ouLk"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Downloading (…)okenizer_config.json: 100%|██████████| 28.0/28.0 [00:00<00:00, 28.0kB/s]\n",
-       "Downloading (…)lve/main/config.json: 100%|██████████| 1.11k/1.11k [00:00<00:00, 1.11MB/s]\n",
-       "Downloading (…)olve/main/vocab.json: 100%|██████████| 1.06M/1.06M [00:00<00:00, 1.06MB/s]\n",
-       "Downloading (…)olve/main/merges.txt: 100%|██████████| 525k/525k [00:00<00:00, 525kB/s]\n",
-       "Downloading (…)/main/tokenizer.json: 100%|██████████| 2.13M/2.13M [00:00<00:00, 2.13MB/s]\n",
-       "Downloading (…)/main/adapter_config.json: 100%|██████████| 472/472 [00:00<00:00, 472kB/s]\n",
-       "Downloading (…)/adapter_model.bin: 100%|██████████| 1.22G/1.22G [00:10<00:00, 120MB/s]\n",
-       "Downloading pytorch_model.bin: 100%|██████████| 1.22G/1.22G [00:10<00:00, 120MB/s]\n"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# モデルとデータセットの読み込み\n",
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
-    "model = AutoModelForCausalLM.from_pretrained(model_name)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
-    "\n",
-    "# サンプルデータセットの読み込み\n",
-    "dataset = load_dataset(\"lighteval\", \"mmlu\")\n",
-    "dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "9TOhJdtsouLk"
-   },
-   "source": [
-    "## 3. LightEvalを使用した評価\n",
-    "\n",
-    "LightEvalを使用してモデルを評価するために、評価タスクを定義し、パイプラインを設定します。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# LightEvalのタスクとパイプラインをインポート\n",
-    "from lighteval.tasks import Task, Pipeline\n",
-    "from lighteval.metrics import EvaluationTracker\n",
-    "\n",
-    "# 評価するタスクを定義\n",
-    "tasks = [\n",
-    "    \"mmlu|anatomy|0|0\",\n",
-    "    \"mmlu|high_school_biology|0|0\",\n",
-    "    \"mmlu|high_school_chemistry|0|0\",\n",
-    "    \"mmlu|professional_medicine|0|0\"\n",
-    "]\n",
-    "\n",
-    "# パイプラインパラメータを設定\n",
-    "pipeline_params = {\n",
-    "    \"max_samples\": 40,  # 評価するサンプル数\n",
-    "    \"batch_size\": 1,    # 推論のバッチサイズ\n",
-    "    \"num_workers\": 4    # ワーカープロセスの数\n",
-    "}\n",
-    "\n",
-    "# 評価トラッカーを作成\n",
-    "evaluation_tracker = EvaluationTracker(\n",
-    "    output_path=\"./results\",\n",
-    "    save_generations=True\n",
-    ")\n",
-    "\n",
-    "# パイプラインを作成\n",
-    "pipeline = Pipeline(\n",
-    "    tasks=tasks,\n",
-    "    pipeline_parameters=pipeline_params,\n",
-    "    evaluation_tracker=evaluation_tracker,\n",
-    "    model=model\n",
-    ")\n",
-    "\n",
-    "# 評価を実行\n",
-    "pipeline.evaluate()\n",
-    "\n",
-    "# 結果を取得して表示\n",
-    "results = pipeline.get_results()\n",
-    "pipeline.show_results()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "-yO6E9quouLl"
-   },
-   "source": [
-    "## 4. 結果の分析\n",
-    "\n",
-    "評価結果を分析し、モデルの性能を理解します。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# pandasを使用して結果を分析\n",
-    "import pandas as pd\n",
-    "\n",
-    "# 結果をDataFrameに変換\n",
-    "df = pd.DataFrame(results)\n",
-    "\n",
-    "# 結果を表示\n",
-    "print(df)\n",
-    "\n",
-    "# 結果を視覚化\n",
-    "df.plot(kind=\"bar\", x=\"Task\", y=\"Value\", legend=False)\n",
-    "plt.ylabel(\"Accuracy\")\n",
-    "plt.title(\"Model Performance on Different Tasks\")\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.10"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/ja/4_evaluation/project/README.md b/ja/4_evaluation/project/README.md
deleted file mode 100644
index ad6ec93f..00000000
--- a/ja/4_evaluation/project/README.md
+++ /dev/null
@@ -1,83 +0,0 @@
-# Argilla、Distilabel、LightEvalを使用したドメイン固有の評価
-
-ほとんどの人気のあるベンチマークは、一般的な能力（推論、数学、コード）を評価しますが、より具体的な能力を評価する必要がある場合はどうすればよいでしょうか？
-
-カスタムドメインに関連するモ���ルを評価する必要がある場合はどうすればよいでしょうか？（例えば、金融、法務、医療のユースケース）
-
-このチュートリアルでは、[Argilla](https://github.com/argilla-io/argilla)、[distilabel](https://github.com/argilla-io/distilabel)、および[LightEval](https://github.com/huggingface/lighteval)を使用して、関連するデータを作成し、サンプルに注釈を付け、モデルを評価するための完全なパイプラインを示します。例として、複数のドキュメントから試験問題を生成することに焦点を当てます。
-
-## プロジェクト構造
-
-このプロセスでは、データセットの生成、注釈の追加、評価のためのサンプルの抽出、実際のモデル評価の4つのステップを実行します。各ステップにはスクリプトがあります。
-
-| スクリプト名 | 説明 |
-|-------------|-------------|
-| generate_dataset.py | 指定された言語モデルを使用して複数のテキストドキュメントから試験問題を生成します。 |
-| annotate_dataset.py | 生成された試験問題の手動注釈のためのArgillaデータセットを作成します。 |
-| create_dataset.py | Argillaから注釈付きデータを処理し、Hugging Faceデータセットを作成します。 |
-| evaluation_task.py | 試験問題データセットの評価のためのカスタムLightEvalタスクを定義します。 |
-
-## ステップ
-
-### 1. データセットの生成
-
-`generate_dataset.py`スクリプトは、distilabelライブラリを使用して複数のテキストドキュメントに基づいて試験問題を生成します。指定されたモデル（デフォルト：Meta-Llama-3.1-8B-Instruct）を使用して質問、正しい回答、および誤った回答（ディストラクター）を作成します。独自のデータサンプルを追加し、異なるモデルを使用することもできます。
-
-生成を実行するには：
-
-```sh
-python generate_dataset.py --input_dir path/to/your/documents --model_id your_model_id --output_path output_directory
-```
-
-これにより、入力ディレクトリ内のすべてのドキュメントに対して生成された試験問題を含む[Distiset](https://distilabel.argilla.io/dev/sections/how_to_guides/advanced/distiset/)が作成されます。
-
-### 2. データセットの注釈
-
-`annotate_dataset.py`スクリプトは、生成された質問を取得し、注釈のためのArgillaデータセットを作成します。データセットの構造を設定し、生成された質問と回答をランダムな順序で入力します。これにより、バイアスを避けることができます。Argillaでは、正しい回答が提案として表示されます。
-
-LLMからの提案された正しい回答がランダムな順序で表示され、正しい回答を承認するか、別の回答を選択できます。このプロセスの所要時間は、評価データセットの規模、ドメインデータの複雑さ、およびLLMの品質によって異なります。例えば、Llama-3.1-70B-Instructを使用して、転移学習のドメインで150サンプルを1時間以内に作成できました。
-
-注釈プロセスを実行するには：
-
-```sh
-python annotate_dataset.py --dataset_path path/to/distiset --output_dataset_name argilla_dataset_name
-```
-
-これにより、手動レビューと注釈のためのArgillaデータセットが作成されます。
-
-![argilla_dataset](./images/domain_eval_argilla_view.png)
-
-Argillaを使用していない場合は、この[クイックスタートガイド](https://docs.argilla.io/latest/getting_started/quickstart/)に従ってローカルまたはスペースにデプロイしてください。
-
-### 3. データセットの作成
-
-`create_dataset.py`スクリプトは、Argillaから注釈付きデータを処理し、Hugging Faceデータセットを作成します。提案された回答と手動で注釈された回答の両方を処理します。このスクリプトは、質問、可能な回答、および正しい回答の列名を含むデータセットを作成します。最終データセットを作成するには：
-
-```sh
-huggingface_hub login
-python create_dataset.py --dataset_path argilla_dataset_name --dataset_repo_id your_hf_repo_id
-```
-
-これにより、指定されたリポジトリにデータセットがHugging Face Hubにプッシュされます。サンプルデータセットは[こちら](https://huggingface.co/datasets/burtenshaw/exam_questions/viewer/default/train)で確認できます。データセットのプレビューは次のようになります：
-
-![hf_dataset](./images/domain_eval_dataset_viewer.png)
-
-### 4. 評価タスク
-
-`evaluation_task.py`スクリプトは、試験問題データセットの評価のためのカスタムLightEvalタスクを定義します。プロンプト関数、カスタム精度メトリック、およびタスク構成が含まれます。
-
-カスタム試験問題タスクを使用してlightevalでモデルを評価するには：
-
-```sh
-lighteval accelerate \
-    --model_args "pretrained=HuggingFaceH4/zephyr-7b-beta" \
-    --tasks "community|exam_questions|0|0" \
-    --custom_tasks domain-eval/evaluation_task.py \
-    --output_dir "./evals"
-```
-
-詳細なガイドはlighteval wikiで確認できます：
-
-- [カスタムタスクの作成](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
-- [カスタムメトリックの作成](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
-- [既存のメトリックの使用](https://github.com/huggingface/lighteval/wiki/Metric-List)
diff --git a/ja/4_evaluation/project/annotate_dataset.py b/ja/4_evaluation/project/annotate_dataset.py
deleted file mode 100644
index f57a4fb6..00000000
--- a/ja/4_evaluation/project/annotate_dataset.py
+++ /dev/null
@@ -1,129 +0,0 @@
-import argparse
-import json
-from random import choices, sample
-
-import argilla as rg
-from distilabel.distiset import Distiset
-
-################################################################################
-# スクリプトのパラメータ
-################################################################################
-
-parser = argparse.ArgumentParser(
-    description="Argillaを使用して試験問題データセットに注釈を付けます。"
-)
-parser.add_argument(
-    "--argilla_api_key",
-    type=str,
-    default="argilla.apikey",
-    help="ArgillaのAPIキー",
-)
-parser.add_argument(
-    "--argilla_api_url",
-    type=str,
-    default="http://localhost:6900",
-    help="ArgillaのAPI URL",
-)
-parser.add_argument(
-    "--dataset_path",
-    type=str,
-    default="exam_questions",
-    help="試験問題データセットのパス",
-)
-parser.add_argument(
-    "--dataset_config",
-    type=str,
-    default="default",
-    help="データセットの構成",
-)
-parser.add_argument(
-    "--dataset_split",
-    type=str,
-    default="train",
-    help="使用するデータセットの分割",
-)
-parser.add_argument(
-    "--output_dataset_name",
-    type=str,
-    default="exam_questions",
-    help="出力Argillaデータセットの名前",
-)
-
-args = parser.parse_args()
-
-################################################################################
-# 検証のためのフィードバックタスクを使用してArgillaデータセットを作成
-################################################################################
-
-client = rg.Argilla(api_key=args.argilla_api_key, api_url=args.argilla_api_url)
-
-if client.datasets(args.output_dataset_name):
-    print(f"既存のデータセット '{args.output_dataset_name}' を削除します")
-    client.datasets(args.output_dataset_name).delete()
-
-settings = rg.Settings(
-    fields=[
-        rg.TextField("question"),
-        rg.TextField("answer_a"),
-        rg.TextField("answer_b"),
-        rg.TextField("answer_c"),
-        rg.TextField("answer_d"),
-    ],
-    questions=[
-        rg.LabelQuestion(
-            name="correct_answer",
-            labels=["answer_a", "answer_b", "answer_c", "answer_d"],
-        ),
-        rg.TextQuestion(
-            name="improved_question",
-            description="質問を改善できますか？",
-        ),
-        rg.TextQuestion(
-            name="improved_answer",
-            description="最良の回答を改善できますか？",
-        ),
-    ],
-)
-
-dataset = rg.Dataset(settings=settings, name=args.output_dataset_name)
-dataset.create()
-
-################################################################################
-# Distisetを読み込み、Argillaデータセットにレコードを処理して追加
-# バイアスを避けるために質問がランダムな順序で表示されることを確認します
-# ただし、Argilla UIでは正しい回答を提案として表示します。
-################################################################################
-
-distiset = Distiset.load_from_disk(args.dataset_path)
-answer_names = ["answer_a", "answer_b", "answer_c", "answer_d"]
-dataset_records = []
-
-for exam in distiset[args.dataset_config][args.dataset_split]:
-    exam_json = json.loads(exam["generation"])["exam"]
-
-    for question in exam_json:
-        answer = question["answer"]
-        distractors = question["distractors"]
-        distractors = choices(distractors, k=3)
-        answers = distractors + [answer]
-        answers = sample(answers, len(answers))
-        suggestion_idx = answers.index(answer)
-        fields = dict(zip(answer_names, answers))
-        fields["question"] = question["question"]
-
-        record = rg.Record(
-            fields=fields,
-            suggestions=[
-                rg.Suggestion(
-                    question_name="correct_answer",
-                    value=answer_names[suggestion_idx],
-                )
-            ],
-        )
-        dataset_records.append(record)
-
-dataset.records.log(dataset_records)
-
-print(
-    f"データセット '{args.output_dataset_name}' がArgillaに作成され、入力されました。"
-)
diff --git a/ja/4_evaluation/project/create_dataset.py b/ja/4_evaluation/project/create_dataset.py
deleted file mode 100644
index f3b5c812..00000000
--- a/ja/4_evaluation/project/create_dataset.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import argparse
-
-import argilla as rg
-from datasets import Dataset
-
-################################################################################
-# スクリプトのパラメータ
-################################################################################
-
-parser = argparse.ArgumentParser(
-    description="注釈付きArgillaデータからHugging Faceデータセットを作成します。"
-)
-parser.add_argument(
-    "--argilla_api_key",
-    type=str,
-    default="argilla.apikey",
-    help="ArgillaのAPIキー",
-)
-parser.add_argument(
-    "--argilla_api_url",
-    type=str,
-    default="http://localhost:6900",
-    help="ArgillaのAPI URL",
-)
-parser.add_argument(
-    "--dataset_path",
-    type=str,
-    default="exam_questions",
-    help="Argillaデータセットのパス",
-)
-parser.add_argument(
-    "--dataset_repo_id",
-    type=str,
-    default="burtenshaw/exam_questions",
-    help="Hugging FaceデータセットリポジトリID",
-)
-
-args = parser.parse_args()
-
-################################################################################
-# Argillaクライアントを初期化し、データセットを読み込む
-################################################################################
-
-client = rg.Argilla(api_key=args.argilla_api_key, api_url=args.argilla_api_url)
-dataset = client.datasets(args.dataset_path)
-
-################################################################################
-# Argillaレコードを処理
-################################################################################
-
-dataset_rows = []
-
-for record in dataset.records(with_suggestions=True, with_responses=True):
-    row = record.fields
-
-    if len(record.responses) == 0:
-        answer = record.suggestions["correct_answer"].value
-        row["correct_answer"] = answer
-    else:
-        for response in record.responses:
-            if response.question_name == "correct_answer":
-                row["correct_answer"] = response.value
-    dataset_rows.append(row)
-
-################################################################################
-# Hugging Faceデータセットを作成し、Hubにプッシュ
-################################################################################
-
-hf_dataset = Dataset.from_list(dataset_rows)
-hf_dataset.push_to_hub(repo_id=args.dataset_repo_id)
-
-print(f"データセットが{args.dataset_repo_id}に正常にプッシュされました")
diff --git a/ja/4_evaluation/project/evaluation_task.py b/ja/4_evaluation/project/evaluation_task.py
deleted file mode 100644
index 9c643c9f..00000000
--- a/ja/4_evaluation/project/evaluation_task.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import numpy as np
-
-from lighteval.tasks.lighteval_task import LightevalTaskConfig
-from lighteval.tasks.requests import Doc
-from lighteval.metrics.utils.metric_utils import (
-    SampleLevelMetric,
-    MetricCategory,
-    MetricUseCase,
-)
-
-################################################################################
-# データセットの構造に基づいてプロンプト関数を定義
-################################################################################
-
-
-def prompt_fn(line, task_name: str = None):
-    """データセットの行を評価用のDocオブジェクトに変換します。"""
-    instruction = "次の試験問題に対して正しい答えを選んでください："
-    return Doc(
-        task_name=task_name,
-        query=f"{instruction} {line['question']}",
-        choices=[
-            f" {line['answer_a']}",
-            f" {line['answer_b']}",
-            f" {line['answer_c']}",
-            f" {line['answer_d']}",
-        ],
-        gold_index=["answer_a", "answer_b", "answer_c", "answer_d"].index(
-            line["correct_answer"]
-        ),
-        instruction=instruction,
-    )
-
-
-################################################################################
-# カスタムメトリックを定義
-# ガイドに基づいて https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric
-# または既存のメトリックを使用 https://github.com/huggingface/lighteval/wiki/Metric-List
-# 既存のメトリックは lighteval.metrics.metrics からインポート可能
-################################################################################
-
-
-def sample_level_fn(formatted_doc: Doc, **kwargs) -> bool:
-    response = np.argmin(kwargs["choices_logprob"])
-    return response == formatted_doc.gold_index
-
-
-custom_metric = SampleLevelMetric(
-    metric_name="exam_question_accuracy",
-    higher_is_better=True,
-    category=MetricCategory.MULTICHOICE,
-    use_case=MetricUseCase.NONE,
-    sample_level_fn=sample_level_fn,
-    corpus_level_fn=np.mean,
-)
-
-################################################################################
-# プロンプト関数とカスタムメトリックに基づいてタスクを定義
-# ガイドに基づいて https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task
-################################################################################
-
-task = LightevalTaskConfig(
-    name="example",
-    prompt_function=prompt_fn,
-    suite=["community"],
-    hf_repo="burtenshaw/exam_questions",
-    hf_subset="default",
-    hf_avail_splits=["train"],
-    evaluation_splits=["train"],
-    few_shots_split=None,
-    few_shots_select=None,
-    metric=[custom_metric],
-)
-
-# TASKS_TABLEにタスクを追加
-TASKS_TABLE = [task]
-
-# モジュールロジック
-if __name__ == "__main__":
-    print([t.name for t in TASKS_TABLE])
-    print(len(TASKS_TABLE))
-
-# lighteval accelerate \
-# "pretrained=HuggingFaceTB/SmolLM2-135M-Instruct" \
-# "community|example|0|0" \
-# --custom-tasks "submitted_tasks/example.py" \
-# --output-dir "results"
diff --git a/ja/4_evaluation/project/generate_dataset.py b/ja/4_evaluation/project/generate_dataset.py
deleted file mode 100644
index 3bd42bfd..00000000
--- a/ja/4_evaluation/project/generate_dataset.py
+++ /dev/null
@@ -1,168 +0,0 @@
-import argparse
-import os
-from pydantic import BaseModel, Field
-from datasets import Dataset
-from typing import List
-
-from distilabel.llms import InferenceEndpointsLLM
-from distilabel.pipeline import Pipeline
-from distilabel.steps.tasks import TextGeneration
-
-
-################################################################################
-# スクリプトのパラメータ
-################################################################################
-
-parser = argparse.ArgumentParser(
-    description="ディレクトリ内のテキストファイルから試験問題を生成します。"
-)
-parser.add_argument(
-    "--model_id",
-    type=str,
-    default="Qwen/Qwen2.5-7B-Instruct",
-    help="テキスト生成のためのモデルID",
-)
-parser.add_argument(
-    "--tokenizer_id",
-    type=str,
-    default="Qwen/Qwen2.5-7B-Instruct",
-    help="テキスト生成のためのトークナイザーID",
-)
-parser.add_argument(
-    "--input_dir",
-    type=str,
-    help="入力テキストファイルを含むディレクトリ",
-    default="data",
-)
-parser.add_argument(
-    "--max_new_tokens",
-    type=int,
-    default=2048,
-    help="生成する新しいトークンの最大数",
-)
-parser.add_argument(
-    "--output_path",
-    type=str,
-    default="exam_questions_output",
-    help="生成されたデータセットを保存するディレクトリ",
-)
-
-args = parser.parse_args()
-
-################################################################################
-# ドキュメントの読み込み
-# ドキュメントは入力ディレクトリにあり、各ファイルが同じトピックに関する
-# 個別のドキュメントであると仮定します。
-################################################################################
-
-# 入力ディレクトリ内のすべてのテキストファイルを処理
-documents = []
-for filename in os.listdir(args.input_dir):
-    if filename.endswith(".txt"):
-        file_path = os.path.join(args.input_dir, filename)
-        with open(file=file_path, mode="r", encoding="utf-8") as file:
-            document_content = file.read()
-            documents.append(document_content)
-
-# すべてのドキュメント内容から単一のデータセットを作成
-dataset = Dataset.from_dict({"document": documents})
-
-################################################################################
-# プロンプトの定義
-# モデルが正しい出力形式を生成するようにシステムプロンプトを使用します。
-# テンプレートを使用してドキュメントをプロンプトに挿入します。
-################################################################################
-
-SYSTEM_PROMPT = """\
-あなたは学生のための試験問題を作成する専門家です。
-提供されたドキュメントに基づいて質問と回答を作成し、
-質問に対する正しい回答と、誤っているが妥当な回答のリストを作成してください。
-回答は次の形式に従う必要があります：
-```
-[
-    {
-        "question": "質問内容",
-        "answer": "質問に対する正しい回答",
-        "distractors": ["誤った回答1", "誤った回答2", "誤った回答3"]
-    },
-    ... (必要に応じてさらに質問と回答を追加)
-]
-```
-""".strip()
-
-INSTRUCTION_TEMPLATE = """\
-    ドキュメントに関する質問と回答のリストを生成してください。 
-    ドキュメント:\n\n{{ instruction }}"""
-
-################################################################################
-# 出力構造の定義
-# パイプラインの出力のデータモデルを定義し、評価タスクの正しい形式で
-# 出力があることを確認します。
-################################################################################
-
-
-class ExamQuestion(BaseModel):
-    question: str = Field(..., description="回答すべき質問")
-    answer: str = Field(..., description="質問に対する正しい回答")
-    distractors: List[str] = Field(
-        ..., description="質問に対する誤っているが妥当な回答のリスト"
-    )
-
-
-class ExamQuestions(BaseModel):
-    exam: List[ExamQuestion]
-
-
-################################################################################
-# パイプラインの作成
-# ドキュメントに基づいて試験問題を生成し、正しい形式で出力する単一のタスクを
-# 持つパイプラインを作成します。Hugging Face InferenceEndpointsと
-# 引数で指定されたモデルを使用します。
-################################################################################
-
-with Pipeline(
-    name="Domain-Eval-Questions",
-    description="提供されたドキュメントに基づいて試験問題を生成します。",
-) as pipeline:
-    # テキスト生成タスクの設定
-    text_generation = TextGeneration(
-        name="exam_generation",
-        llm=InferenceEndpointsLLM(
-            model_id=args.model_id,
-            tokenizer_id=args.model_id,
-            api_key=os.environ["HF_TOKEN"],
-            structured_output={
-                "schema": ExamQuestions.model_json_schema(),
-                "format": "json",
-            },
-        ),
-        input_batch_size=8,
-        output_mappings={"model_name": "generation_model"},
-        input_mappings={"instruction": "document"},
-        system_prompt=SYSTEM_PROMPT,
-        template=INSTRUCTION_TEMPLATE,
-    )
-
-
-################################################################################
-# パイプラインの実行
-# すべてのドキュメントに対してパイプラインを実行し、結果を出力パスに保存します。
-################################################################################
-
-if __name__ == "__main__":
-    # すべてのドキュメントに対してパイプラインを実行
-    distiset = pipeline.run(
-        parameters={
-            "exam_generation": {
-                "llm": {
-                    "generation_kwargs": {
-                        "max_new_tokens": args.max_new_tokens,
-                    }
-                }
-            }
-        },
-        use_cache=False,
-        dataset=dataset,
-    )
-
-    distiset.save_to_disk(args.output_path)
diff --git a/ja/5_vision_language_models/README.md b/ja/5_vision_language_models/README.md
deleted file mode 100644
index 9fac3c40..00000000
--- a/ja/5_vision_language_models/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
-# ビジョン言語モデル
-
-## 1. VLMの使用
-
-ビジョン言語モデル（VLM）は、画像キャプション生成、視覚質問応答、マルチモーダル推論などのタスクを可能にするために、テキストと並行して画像入力を処理します。
-
-典型的なVLMアーキテクチャは、視覚的特徴を抽出する画像エンコーダ、視覚的およびテキスト表現を整列させるプロジェクション層、およびテキストを処理または生成する言語モデルで構成されます。これにより、モデルは視覚要素と言語概念の間の接続を確立できます。
-
-VLMは、使用ケースに応じてさまざまな構成で使用できます。ベースモデルは一般的なビジョン言語タスクを処理し、チャット最適化されたバリアントは会話型インタラクションをサポートします。一部のモデルには、視覚的証拠に基づいて予測を行うための追加コンポーネントや、物体検出などの特定のタスクに特化したコンポーネントが含まれています。
-
-VLMの技術的な詳細と使用方法については、[VLMの使用](./vlm_usage.md)ページを参照してください。
-
-## 2. VLMのファインチューニング
-
-VLMのファインチューニングは、特定のタスクを実行するため、または特定のデータセットで効果的に動作するように、事前トレーニングされたモデルを適応させるプロセスです。このプロセスは、モジュール1および2で紹介されたように、教師ありファインチューニング、好みの最適化、またはその両方を組み合わせたハイブリッドアプローチなどの方法論に従うことができます。
-
-コアツールと技術はLLMで使用されるものと似ていますが、VLMのファインチューニングには、画像のデータ表現と準備に特に注意を払う必要があります。これにより、モデルが視覚データとテキストデータの両方を効果的に統合および処理し、最適なパフォーマンスを発揮できるようになります。デモモデルであるSmolVLMは、前のモジュールで使用された言語モデルよりも大幅に大きいため、効率的なファインチューニング方法を探ることが重要です。量子化やPEFTなどの技術を使用することで、プロセスをよりアクセスしやすく、コスト効果の高いものにし、より多くのユーザーがモデルを試すことができます。
-
-VLMのファインチューニングに関する詳細なガイダンスについては、[VLMのファインチューニング](./vlm_finetuning.md)ページを参照してください。
-
-## 演習ノートブック
-
-| タイトル | 説明 | 演習 | リンク | Colab |
-|-------|-------------|----------|------|-------|
-| VLMの使用 | 事前トレーニングされたVLMをさまざまなタスクに使用する方法を学ぶ | 🐢 画像を処理する<br>🐕 バッチ処理で複数の画像を処理する<br>🦁 ビデオ全体を処理する | [ノートブック](./notebooks/vlm_usage_sample.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/5_vision_language_models/notebooks/vlm_usage_sample.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-| VLMのファインチューニング | タスク固有のデータセットに対して事前トレーニングされたVLMをファインチューニングする方法を学ぶ | 🐢 基本的なデータセットを使用してファインチューニングする<br>🐕 新しいデータセットを試す<br>🦁 代替のファインチューニング方法を試す | [ノートブック](./notebooks/vlm_sft_sample.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/5_vision_language_models/notebooks/vlm_sft_sample.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-
-## 参考文献
-- [Hugging Face Learn: Supervised Fine-Tuning VLMs](https://huggingface.co/learn/cookbook/fine_tuning_vlm_trl)
-- [Hugging Face Learn: Supervised Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_smol_vlm_sft_trl)
-- [Hugging Face Learn: Preference Optimization Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_vlm_dpo_smolvlm_instruct)
-- [Hugging Face Blog: Preference Optimization for VLMs](https://huggingface.co/blog/dpo_vlm)
-- [Hugging Face Blog: Vision Language Models](https://huggingface.co/blog/vlms)
-- [Hugging Face Blog: SmolVLM](https://huggingface.co/blog/smolvlm)
-- [Hugging Face Model: SmolVLM-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct)
-- [CLIP: Learning Transferable Visual Models from Natural Language Supervision](https://arxiv.org/abs/2103.00020)
-- [Align Before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://arxiv.org/abs/2107.07651)
diff --git a/ja/5_vision_language_models/images/VLM_Architecture.png b/ja/5_vision_language_models/images/VLM_Architecture.png
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diff --git a/ja/5_vision_language_models/notebooks/vlm_sft_sample.ipynb b/ja/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
deleted file mode 100644
index 56ad5d65..00000000
--- a/ja/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
+++ /dev/null
@@ -1,469 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 画像とテキストをVLMで処理する\n",
-    "\n",
-    "このノートブックでは、`HuggingFaceTB/SmolVLM-Instruct` 4bit量子化モデルを使用して、以下のようなさまざまなマルチモーダルタスクを実行する方法を示します：\n",
-    "- 視覚質問応答（VQA）：画像の内容に基づいて質問に答える。\n",
-    "- テキスト認識（OCR）：画像内のテキストを抽出して解釈する。\n",
-    "- ビデオ理解：連続フレーム解析を通じてビデオを説明する。\n",
-    "\n",
-    "プロンプトを効果的に構築することで、シーン理解、ドキュメント分析、動的視覚推論など、多くのアプリケーションにモデルを活用できます。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Google Colabでの要件をインストール\n",
-    "# !pip install transformers datasets trl huggingface_hub bitsandbytes\n",
-    "\n",
-    "# Hugging Faceに認証\n",
-    "from huggingface_hub import notebook_login\n",
-    "notebook_login()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`low_cpu_mem_usage` was None, now default to True since model is quantized.\n",
-      "You shouldn't move a model that is dispatched using accelerate hooks.\n",
-      "Some kwargs in processor config are unused and will not have any effect: image_seq_len. \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'longest_edge': 1536}\n"
-     ]
-    }
-   ],
-   "source": [
-    "import torch, PIL\n",
-    "from transformers import AutoProcessor, AutoModelForVision2Seq, BitsAndBytesConfig\n",
-    "from transformers.image_utils import load_image\n",
-    "\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "quantization_config = BitsAndBytesConfig(load_in_4bit=True)\n",
-    "model_name = \"HuggingFaceTB/SmolVLM-Instruct\"\n",
-    "model = AutoModelForVision2Seq.from_pretrained(\n",
-    "    model_name,\n",
-    "    quantization_config=quantization_config,\n",
-    ").to(device)\n",
-    "processor = AutoProcessor.from_pretrained(\"HuggingFaceTB/SmolVLM-Instruct\")\n",
-    "\n",
-    "print(processor.image_processor.size)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 画像の処理"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "まず、画像のキャプションを生成し、画像に関する質問に答えることから始めましょう。また、複数の画像を処理する方法も探ります。\n",
-    "### 1. 単一画像のキャプション生成"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<img src=\"https://cdn.pixabay.com/photo/2024/11/20/09/14/christmas-9210799_1280.jpg\"/>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<img src=\"https://cdn.pixabay.com/photo/2024/11/23/08/18/christmas-9218404_1280.jpg\"/>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from IPython.display import Image, display\n",
-    "\n",
-    "image_url1 = \"https://cdn.pixabay.com/photo/2024/11/20/09/14/christmas-9210799_1280.jpg\"\n",
-    "display(Image(url=image_url1))\n",
-    "\n",
-    "image_url2 = \"https://cdn.pixabay.com/photo/2024/11/23/08/18/christmas-9218404_1280.jpg\"\n",
-    "display(Image(url=image_url2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/duydl/Miniconda3/envs/py310/lib/python3.10/site-packages/bitsandbytes/nn/modules.py:451: UserWarning: Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference or training speed.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['User:<image>Can you describe the image?\\nAssistant: The image is a scene of a person walking in a forest. The person is wearing a coat and a cap. The person is holding the hand of another person. The person is walking on a path. The path is covered with dry leaves. The background of the image is a forest with trees.']\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 画像を1枚読み込む\n",
-    "image1 = load_image(image_url1)\n",
-    "\n",
-    "# 入力メッセージを作成\n",
-    "messages = [\n",
-    "    {\n",
-    "        \"role\": \"user\",\n",
-    "        \"content\": [\n",
-    "            {\"type\": \"image\"},\n",
-    "            {\"type\": \"text\", \"text\": \"Can you describe the image?\"}\n",
-    "        ]\n",
-    "    },\n",
-    "]\n",
-    "\n",
-    "# 入力を準備\n",
-    "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
-    "inputs = processor(text=prompt, images=[image1], return_tensors=\"pt\")\n",
-    "inputs = inputs.to(device)\n",
-    "\n",
-    "# 出力を生成\n",
-    "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
-    "generated_texts = processor.batch_decode(\n",
-    "    generated_ids,\n",
-    "    skip_special_tokens=True,\n",
-    ")\n",
-    "\n",
-    "print(generated_texts)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2. 複数画像の比較\n",
-    "モデルは複数の画像を処理して比較することもできます。2つの画像の共通のテーマを判断してみましょう。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['User:<image>What event do they both represent?\\nAssistant: Christmas.']\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "# 画像を読み込む\n",
-    "image2 = load_image(image_url2)\n",
-    "\n",
-    "# 入力メッセージを作成\n",
-    "messages = [\n",
-    "    # {\n",
-    "    #     \"role\": \"user\",\n",
-    "    #     \"content\": [\n",
-    "    #         {\"type\": \"image\"},\n",
-    "    #         {\"type\": \"image\"},\n",
-    "    #         {\"type\": \"text\", \"text\": \"Can you describe the two images?\"}\n",
-    "    #     ]\n",
-    "    # },\n",
-    "    {\n",
-    "        \"role\": \"user\",\n",
-    "        \"content\": [\n",
-    "            {\"type\": \"image\"},\n",
-    "            {\"type\": \"image\"},\n",
-    "            {\"type\": \"text\", \"text\": \"What event do they both represent?\"}\n",
-    "        ]\n",
-    "    },\n",
-    "]\n",
-    "\n",
-    "# 入力を準備\n",
-    "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
-    "inputs = processor(text=prompt, images=[image1, image2], return_tensors=\"pt\")\n",
-    "inputs = inputs.to(device)\n",
-    "\n",
-    "# 出力を生成\n",
-    "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
-    "generated_texts = processor.batch_decode(\n",
-    "    generated_ids,\n",
-    "    skip_special_tokens=True,\n",
-    ")\n",
-    "\n",
-    "print(generated_texts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 🔠 テキスト認識（OCR）\n",
-    "VLMは画像内のテキストを認識して解釈することもでき、ドキュメント分析などのタスクに適しています。\n",
-    "テキストが密集している画像で実験してみてください。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<img src=\"https://cdn.pixabay.com/photo/2020/11/30/19/23/christmas-5792015_960_720.png\"/>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['User:<image>What is written?\\nAssistant: MERRY CHRISTMAS AND A HAPPY NEW YEAR']\n"
-     ]
-    }
-   ],
-   "source": [
-    "document_image_url = \"https://cdn.pixabay.com/photo/2020/11/30/19/23/christmas-5792015_960_720.png\"\n",
-    "display(Image(url=document_image_url))\n",
-    "\n",
-    "# ドキュメント画像を読み込む\n",
-    "document_image = load_image(document_image_url)\n",
-    "\n",
-    "# 分析のための入力メッセージを作成\n",
-    "messages = [\n",
-    "    {\n",
-    "        \"role\": \"user\",\n",
-    "        \"content\": [\n",
-    "            {\"type\": \"image\"},\n",
-    "            {\"type\": \"text\", \"text\": \"What is written?\"}\n",
-    "        ]\n",
-    "    }\n",
-    "]\n",
-    "\n",
-    "# 入力を準備\n",
-    "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
-    "inputs = processor(text=prompt, images=[document_image], return_tensors=\"pt\")\n",
-    "inputs = inputs.to(device)\n",
-    "\n",
-    "# 出力を生成\n",
-    "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
-    "generated_texts = processor.batch_decode(\n",
-    "    generated_ids,\n",
-    "    skip_special_tokens=True,\n",
-    ")\n",
-    "\n",
-    "print(generated_texts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## ビデオの処理\n",
-    "\n",
-    "視覚言語モデル（VLM）は、キーフレームを抽出し、時間順に推論することで間接的にビデオを処理できます。VLMは専用のビデオモデルの時間的モデリング機能を欠いていますが、それでも以下のことが可能です：\n",
-    "- サンプリングされたフレームを順次分析することで、アクションやイベントを説明する。\n",
-    "- 代表的なキーフレームに基づいてビデオに関する質問に答える。\n",
-    "- 複数のフレームのテキスト記述を組み合わせてビデオの内容を要約する。\n",
-    "\n",
-    "例を使って実験してみましょう：\n",
-    "\n",
-    "<video width=\"640\" height=\"360\" controls>\n",
-    "  <source src=\"https://cdn.pixabay.com/video/2023/10/28/186794-879050032_large.mp4\" type=\"video/mp4\">\n",
-    "  Your browser does not support the video tag.\n",
-    "</video>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# !pip install opencv-python"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from IPython.display import Video\n",
-    "import cv2\n",
-    "import numpy as np\n",
-    "\n",
-    "def extract_frames(video_path, max_frames=50, target_size=None):\n",
-    "    cap = cv2.VideoCapture(video_path)\n",
-    "    if not cap.isOpened():\n",
-    "        raise ValueError(f\"Could not open video: {video_path}\")\n",
-    "    \n",
-    "    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
-    "    frame_indices = np.linspace(0, total_frames - 1, max_frames, dtype=int)\n",
-    "\n",
-    "    frames = []\n",
-    "    for idx in frame_indices:\n",
-    "        cap.set(cv2.CAP_PROP_POS_FRAMES, idx)\n",
-    "        ret, frame = cap.read()\n",
-    "        if ret:\n",
-    "            frame = PIL.Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n",
-    "            if target_size:\n",
-    "                frames.append(resize_and_crop(frame, target_size))\n",
-    "            else:\n",
-    "                frames.append(frame)\n",
-    "    cap.release()\n",
-    "    return frames\n",
-    "\n",
-    "def resize_and_crop(image, target_size):\n",
-    "    width, height = image.size\n",
-    "    scale = target_size / min(width, height)\n",
-    "    image = image.resize((int(width * scale), int(height * scale)), PIL.Image.Resampling.LANCZOS)\n",
-    "    left = (image.width - target_size) // 2\n",
-    "    top = (image.height - target_size) // 2\n",
-    "    return image.crop((left, top, left + target_size, top + target_size))\n",
-    "\n",
-    "# ビデオリンク\n",
-    "video_link = \"https://cdn.pixabay.com/video/2023/10/28/186794-879050032_large.mp4\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Response: User: Following are the frames of a video in temporal order.<image>Describe what the woman is doing.\n",
-      "Assistant: The woman is hanging an ornament on a Christmas tree.\n"
-     ]
-    }
-   ],
-   "source": [
-    "question = \"Describe what the woman is doing.\"\n",
-    "\n",
-    "def generate_response(model, processor, frames, question):\n",
-    "\n",
-    "    image_tokens = [{\"type\": \"image\"} for _ in frames]\n",
-    "    messages = [\n",
-    "        {\n",
-    "            \"role\": \"user\",\n",
-    "            \"content\": [{\"type\": \"text\", \"text\": \"Following are the frames of a video in temporal order.\"}, *image_tokens, {\"type\": \"text\", \"text\": question}]\n",
-    "        }\n",
-    "    ]\n",
-    "    inputs = processor(\n",
-    "        text=processor.apply_chat_template(messages, add_generation_prompt=True),\n",
-    "        images=frames,\n",
-    "        return_tensors=\"pt\"\n",
-    "    ).to(model.device)\n",
-    "\n",
-    "    outputs = model.generate(\n",
-    "        **inputs, max_new_tokens=100, num_beams=5, temperature=0.7, do_sample=True, use_cache=True\n",
-    "    )\n",
-    "    return processor.decode(outputs[0], skip_special_tokens=True)\n",
-    "\n",
-    "\n",
-    "# ビデオからフレームを抽出\n",
-    "frames = extract_frames(video_link, max_frames=15, target_size=384)\n",
-    "\n",
-    "processor.image_processor.size = (384, 384)\n",
-    "processor.image_processor.do_resize = False\n",
-    "# 応答を生成\n",
-    "response = generate_response(model, processor, frames, question)\n",
-    "\n",
-    "# 結果を表示\n",
-    "# print(\"Question:\", question)\n",
-    "print(\"Response:\", response)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 💐 完了です！\n",
-    "\n",
-    "このノートブックでは、マルチモーダルタスクのプロンプトをフォーマットする方法など、視覚言語モデル（VLM）を使用する方法を示しました。ここで説明した手順に従うことで、VLMとそのアプリケーションを実験できます。\n",
-    "\n",
-    "### 次のステップ\n",
-    "- VLMのさらなる使用例を試してみてください。\n",
-    "- 同僚と協力して、彼らのプルリクエスト（PR）をレビューしてください。\n",
-    "- 新しい使用例、例、概念を導入するために、問題を開いたりPRを提出したりして、このコース資料の改善に貢献してください。\n",
-    "\n",
-    "楽しい探求を！🌟"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py310",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/ja/5_vision_language_models/notebooks/vlm_usage_sample.ipynb b/ja/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
deleted file mode 100644
index abdaf7fd..00000000
--- a/ja/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
+++ /dev/null
@@ -1,469 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 画像とテキストをVLMで処理する\n",
-    "\n",
-    "このノートブックでは、`HuggingFaceTB/SmolVLM-Instruct` 4bit量子化モデルを使用して、以下のようなさまざまなマルチモーダルタスクを実行する方法を示します：\n",
-    "- 視覚質問応答（VQA）：画像の内容に基づいて質問に答える。\n",
-    "- テキスト認識（OCR）：画像内のテキストを抽出して解釈する。\n",
-    "- ビデオ理解：連続フレーム解析を通じてビデオを説明する。\n",
-    "\n",
-    "プロンプトを効果的に構築することで、シーン理解、ドキュメント分析、動的視覚推論など、多くのアプリケーションにモデルを活用できます。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Google Colabでの要件をインストール\n",
-    "# !pip install transformers datasets trl huggingface_hub bitsandbytes\n",
-    "\n",
-    "# Hugging Faceに認証\n",
-    "from huggingface_hub import notebook_login\n",
-    "notebook_login()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`low_cpu_mem_usage` was None, now default to True since model is quantized.\n",
-      "You shouldn't move a model that is dispatched using accelerate hooks.\n",
-      "Some kwargs in processor config are unused and will not have any effect: image_seq_len. \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'longest_edge': 1536}\n"
-     ]
-    }
-   ],
-   "source": [
-    "import torch, PIL\n",
-    "from transformers import AutoProcessor, AutoModelForVision2Seq, BitsAndBytesConfig\n",
-    "from transformers.image_utils import load_image\n",
-    "\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "quantization_config = BitsAndBytesConfig(load_in_4bit=True)\n",
-    "model_name = \"HuggingFaceTB/SmolVLM-Instruct\"\n",
-    "model = AutoModelForVision2Seq.from_pretrained(\n",
-    "    model_name,\n",
-    "    quantization_config=quantization_config,\n",
-    ").to(device)\n",
-    "processor = AutoProcessor.from_pretrained(\"HuggingFaceTB/SmolVLM-Instruct\")\n",
-    "\n",
-    "print(processor.image_processor.size)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 画像の処理"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "まず、画像のキャプションを生成し、画像に関する質問に答えることから始めましょう。また、複数の画像を処理する方法も探ります。\n",
-    "### 1. 単一画像のキャプション生成"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<img src=\"https://cdn.pixabay.com/photo/2024/11/20/09/14/christmas-9210799_1280.jpg\"/>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<img src=\"https://cdn.pixabay.com/photo/2024/11/23/08/18/christmas-9218404_1280.jpg\"/>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from IPython.display import Image, display\n",
-    "\n",
-    "image_url1 = \"https://cdn.pixabay.com/photo/2024/11/20/09/14/christmas-9210799_1280.jpg\"\n",
-    "display(Image(url=image_url1))\n",
-    "\n",
-    "image_url2 = \"https://cdn.pixabay.com/photo/2024/11/23/08/18/christmas-9218404_1280.jpg\"\n",
-    "display(Image(url=image_url2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/duydl/Miniconda3/envs/py310/lib/python3.10/site-packages/bitsandbytes/nn/modules.py:451: UserWarning: Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference or training speed.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['User:<image>Can you describe the image?\\nAssistant: The image is a scene of a person walking in a forest. The person is wearing a coat and a cap. The person is holding the hand of another person. The person is walking on a path. The path is covered with dry leaves. The background of the image is a forest with trees.']\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 画像を1枚読み込む\n",
-    "image1 = load_image(image_url1)\n",
-    "\n",
-    "# 入力メッセージを作成\n",
-    "messages = [\n",
-    "    {\n",
-    "        \"role\": \"user\",\n",
-    "        \"content\": [\n",
-    "            {\"type\": \"image\"},\n",
-    "            {\"type\": \"text\", \"text\": \"Can you describe the image?\"}\n",
-    "        ]\n",
-    "    },\n",
-    "]\n",
-    "\n",
-    "# 入力を準備\n",
-    "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
-    "inputs = processor(text=prompt, images=[image1], return_tensors=\"pt\")\n",
-    "inputs = inputs.to(device)\n",
-    "\n",
-    "# 出力を生成\n",
-    "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
-    "generated_texts = processor.batch_decode(\n",
-    "    generated_ids,\n",
-    "    skip_special_tokens=True,\n",
-    ")\n",
-    "\n",
-    "print(generated_texts)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2. 複数画像の比較\n",
-    "モデルは複数の画像を処理して比較することもできます。2つの画像の共通のテーマを判断してみましょう。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['User:<image>What event do they both represent?\\nAssistant: Christmas.']\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "# 画像を読み込む\n",
-    "image2 = load_image(image_url2)\n",
-    "\n",
-    "# 入力メッセージを作成\n",
-    "messages = [\n",
-    "    # {\n",
-    "    #     \"role\": \"user\",\n",
-    "    #     \"content\": [\n",
-    "    #         {\"type\": \"image\"},\n",
-    "    #         {\"type\": \"image\"},\n",
-    "    #         {\"type\": \"text\", \"text\": \"Can you describe the two images?\"}\n",
-    "    #     ]\n",
-    "    # },\n",
-    "    {\n",
-    "        \"role\": \"user\",\n",
-    "        \"content\": [\n",
-    "            {\"type\": \"image\"},\n",
-    "            {\"type\": \"image\"},\n",
-    "            {\"type\": \"text\", \"text\": \"What event do they both represent?\"}\n",
-    "        ]\n",
-    "    },\n",
-    "]\n",
-    "\n",
-    "# 入力を準備\n",
-    "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
-    "inputs = processor(text=prompt, images=[image1, image2], return_tensors=\"pt\")\n",
-    "inputs = inputs.to(device)\n",
-    "\n",
-    "# 出力を生成\n",
-    "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
-    "generated_texts = processor.batch_decode(\n",
-    "    generated_ids,\n",
-    "    skip_special_tokens=True,\n",
-    ")\n",
-    "\n",
-    "print(generated_texts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 🔠 テキスト認識（OCR）\n",
-    "VLMは画像内のテキストを認識して解釈することもでき、ドキュメント分析などのタスクに適しています。\n",
-    "テキストが密集している画像で実験してみてください。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<img src=\"https://cdn.pixabay.com/photo/2020/11/30/19/23/christmas-5792015_960_720.png\"/>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['User:<image>What is written?\\nAssistant: MERRY CHRISTMAS AND A HAPPY NEW YEAR']\n"
-     ]
-    }
-   ],
-   "source": [
-    "document_image_url = \"https://cdn.pixabay.com/photo/2020/11/30/19/23/christmas-5792015_960_720.png\"\n",
-    "display(Image(url=document_image_url))\n",
-    "\n",
-    "# ドキュメント画像を読み込む\n",
-    "document_image = load_image(document_image_url)\n",
-    "\n",
-    "# 分析のための入力メッセージを作成\n",
-    "messages = [\n",
-    "    {\n",
-    "        \"role\": \"user\",\n",
-    "        \"content\": [\n",
-    "            {\"type\": \"image\"},\n",
-    "            {\"type\": \"text\", \"text\": \"What is written?\"}\n",
-    "        ]\n",
-    "    }\n",
-    "]\n",
-    "\n",
-    "# 入力を準備\n",
-    "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
-    "inputs = processor(text=prompt, images=[document_image], return_tensors=\"pt\")\n",
-    "inputs = inputs.to(device)\n",
-    "\n",
-    "# 出力を生成\n",
-    "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
-    "generated_texts = processor.batch_decode(\n",
-    "    generated_ids,\n",
-    "    skip_special_tokens=True,\n",
-    ")\n",
-    "\n",
-    "print(generated_texts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## ビデオの処理\n",
-    "\n",
-    "視覚言語モデル（VLM）は、キーフレームを抽出し、時間順に推���することで間接的にビデオを処理できます。VLMは専用のビデオモデルの時間的モデリング機能を欠いていますが、それでも以下のことが可能です：\n",
-    "- サンプリングされたフレームを順次分析することで、アクションやイベントを説明する。\n",
-    "- 代表的なキーフレームに基づいてビデオに関する質問に答える。\n",
-    "- 複数のフレームのテキスト記述を組み合わせてビデオの内容を要約する。\n",
-    "\n",
-    "例を使って実験してみましょう：\n",
-    "\n",
-    "<video width=\"640\" height=\"360\" controls>\n",
-    "  <source src=\"https://cdn.pixabay.com/video/2023/10/28/186794-879050032_large.mp4\" type=\"video/mp4\">\n",
-    "  Your browser does not support the video tag.\n",
-    "</video>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# !pip install opencv-python"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from IPython.display import Video\n",
-    "import cv2\n",
-    "import numpy as np\n",
-    "\n",
-    "def extract_frames(video_path, max_frames=50, target_size=None):\n",
-    "    cap = cv2.VideoCapture(video_path)\n",
-    "    if not cap.isOpened():\n",
-    "        raise ValueError(f\"Could not open video: {video_path}\")\n",
-    "    \n",
-    "    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
-    "    frame_indices = np.linspace(0, total_frames - 1, max_frames, dtype=int)\n",
-    "\n",
-    "    frames = []\n",
-    "    for idx in frame_indices:\n",
-    "        cap.set(cv2.CAP_PROP_POS_FRAMES, idx)\n",
-    "        ret, frame = cap.read()\n",
-    "        if ret:\n",
-    "            frame = PIL.Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n",
-    "            if target_size:\n",
-    "                frames.append(resize_and_crop(frame, target_size))\n",
-    "            else:\n",
-    "                frames.append(frame)\n",
-    "    cap.release()\n",
-    "    return frames\n",
-    "\n",
-    "def resize_and_crop(image, target_size):\n",
-    "    width, height = image.size\n",
-    "    scale = target_size / min(width, height)\n",
-    "    image = image.resize((int(width * scale), int(height * scale)), PIL.Image.Resampling.LANCZOS)\n",
-    "    left = (image.width - target_size) // 2\n",
-    "    top = (image.height - target_size) // 2\n",
-    "    return image.crop((left, top, left + target_size, top + target_size))\n",
-    "\n",
-    "# ビデオリンク\n",
-    "video_link = \"https://cdn.pixabay.com/video/2023/10/28/186794-879050032_large.mp4\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Response: User: Following are the frames of a video in temporal order.<image>Describe what the woman is doing.\n",
-      "Assistant: The woman is hanging an ornament on a Christmas tree.\n"
-     ]
-    }
-   ],
-   "source": [
-    "question = \"Describe what the woman is doing.\"\n",
-    "\n",
-    "def generate_response(model, processor, frames, question):\n",
-    "\n",
-    "    image_tokens = [{\"type\": \"image\"} for _ in frames]\n",
-    "    messages = [\n",
-    "        {\n",
-    "            \"role\": \"user\",\n",
-    "            \"content\": [{\"type\": \"text\", \"text\": \"Following are the frames of a video in temporal order.\"}, *image_tokens, {\"type\": \"text\", \"text\": question}]\n",
-    "        }\n",
-    "    ]\n",
-    "    inputs = processor(\n",
-    "        text=processor.apply_chat_template(messages, add_generation_prompt=True),\n",
-    "        images=frames,\n",
-    "        return_tensors=\"pt\"\n",
-    "    ).to(model.device)\n",
-    "\n",
-    "    outputs = model.generate(\n",
-    "        **inputs, max_new_tokens=100, num_beams=5, temperature=0.7, do_sample=True, use_cache=True\n",
-    "    )\n",
-    "    return processor.decode(outputs[0], skip_special_tokens=True)\n",
-    "\n",
-    "\n",
-    "# ビデオからフレームを抽出\n",
-    "frames = extract_frames(video_link, max_frames=15, target_size=384)\n",
-    "\n",
-    "processor.image_processor.size = (384, 384)\n",
-    "processor.image_processor.do_resize = False\n",
-    "# 応答を生成\n",
-    "response = generate_response(model, processor, frames, question)\n",
-    "\n",
-    "# 結果を表示\n",
-    "# print(\"Question:\", question)\n",
-    "print(\"Response:\", response)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 💐 完了です！\n",
-    "\n",
-    "このノートブックでは、マルチモーダルタスクのプロンプトをフォーマットする方法など、視覚言語モデル（VLM）を使用する方法を示しました。ここで説明した手順に従うことで、VLMとそのアプリケーションを実験できます。\n",
-    "\n",
-    "### 次のステップ\n",
-    "- VLMのさらなる使用例を試してみてください。\n",
-    "- 同僚と協力して、彼らのプルリクエスト（PR）をレビューしてください。\n",
-    "- 新しい使用例、例、概念を導入するために、問題を開いたりPRを提出したりして、このコース資料の改善に貢献してください。\n",
-    "\n",
-    "楽しい探求を！🌟"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py310",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/ja/5_vision_language_models/vlm_finetuning.md b/ja/5_vision_language_models/vlm_finetuning.md
deleted file mode 100644
index 9f69d14b..00000000
--- a/ja/5_vision_language_models/vlm_finetuning.md
+++ /dev/null
@@ -1,96 +0,0 @@
-# VLMファインチューニング
-## 効率的なファインチューニング
-
-### 量子化
-量子化は、モデルの重みとアクティベーションの精度を低下させ、メモリ使用量を大幅に削減し、計算速度を向上させます。たとえば、`float32`から`bfloat16`に切り替えると、パラメータごとのメモリ要件が半分になり、パフォーマンスを維持できます。より積極的な圧縮のために、8ビットおよび4ビットの量子化を使用してメモリ使用量をさらに削減できますが、精度が若干低下する可能性があります。これらの技術は、モデルとオプティマイザの設定の両方に適用でき、リソースが限られたハードウェアでの効率的なトレーニングを可能にします。
-
-### PEFT & LoRA
-モジュール3で紹介されたように、LoRA（Low-Rank Adaptation）は、元のモデルの重みを固定したまま、コンパクトなランク分解行列を学習することに焦点を当てています。これにより、トレーニング可能なパラメータの数が大幅に削減され、リソース要件が大幅に削減されます。LoRAはPEFTと統合されることで、大規模モデルのファインチューニングを可能にし、トレーニング可能なパラメータの小さなサブセットのみを調整します。このアプローチは、タスク固有の適応に特に効果的であり、数十億のトレーニング可能なパラメータを数百万に削減しながらパフォーマンスを維持します。
-
-### バッチサイズの最適化
-ファインチューニングのバッチサイズを最適化するには、大きな値から始め、メモリ不足（OOM）エラーが発生した場合は減らします。`gradient_accumulation_steps`を増やして補い、複数の更新にわたって合計バッチサイズを実質的に維持します。さらに、`gradient_checkpointing`を有効にして、逆伝播中に中間状態を再計算することでメモリ使用量を削減し、計算時間を犠牲にしてアクティベーションメモリ要件を削減します。これらの戦略は、ハードウェアの利用を最大化し、メモリ制約を克服するのに役立ちます。
-
-```python
-from transformers import TrainingArguments
-
-training_args = TrainingArguments(
-    output_dir="./fine_tuned_model",  # モデルチェックポイントのディレクトリ
-    per_device_train_batch_size=4,   # デバイスごとのバッチサイズ（GPU/TPU）
-    num_train_epochs=3,              # トレーニングの総エポック数
-    learning_rate=5e-5,              # 学習率
-    save_steps=1000,                 # 1000ステップごとにチェックポイントを保存
-    bf16=True,                       # トレーニングに混合精度を使用
-    gradient_checkpointing=True,     # アクティベーションメモリ使用量を削減するために有効にする
-    gradient_accumulation_steps=16,  # 16ステップにわたって勾配を蓄積
-    logging_steps=50                 # 50ステップごとにメトリクスをログ
-)
-```
-
-## **教師ありファインチューニング（SFT）**
-
-教師ありファインチューニング（SFT）は、事前トレーニングされたビジョン言語モデル（VLM）を特定のタスクに適応させるために、画像と対応するテキストを含むラベル付きデータセットを活用するプロセスです。この方法は、視覚質問応答、画像キャプション生成、チャート解釈などのドメイン固有またはタスク固有の機能を実行するモデルの能力を向上させます。
-
-### **概要**
-SFTは、ベースモデルの一般的な能力が不足する場合に、特定のドメインや特定の問題を解決するためにVLMを専門化する必要がある場合に不可欠です。たとえば、モデルが独自の視覚的特徴やドメイン固有の用語に苦労する場合、SFTはラベル付きデータから学習することでこれらの領域に焦点を当てることができます。
-
-SFTは非常に効果的ですが、いくつかの制限があります：
-- **データ依存性**：タスクに合わせた高品質のラベル付きデータセットが必要です。
-- **計算リソース**：大規模なVLMのファインチューニングはリソース集約的です。
-- **過学習のリスク**：モデルがあまりにも狭くファインチューニングされると、一般化能力を失う可能性があります。
-
-これらの課題にもかかわらず、SFTは特定のコンテキストでのモデルパフォーマンスを向上させるための強力な手法です。
-
-### **使用方法**
-1. **データ準備**：画像とテキストのペアを含むラベル付きデータセットから始めます。たとえば、チャート分析のタスクでは、`HuggingFaceM4/ChartQA`データセットにはチャート画像、クエリ、および簡潔な応答が含まれています。
-
-2. **モデル設定**：タスクに適した事前トレーニングされたVLM（例：`HuggingFaceTB/SmolVLM-Instruct`）と、テキストと画像の入力を準備するためのプロセッサをロードします。モデルを教師あり学習に設定し、ハードウェアに適した設定を行います。
-
-3. **ファインチューニングプロセス**：
-   - **データのフォーマット**：データセットをチャットボットのような形式に構造化し、システムメッセージ、ユーザークエリ、および対応する回答をペアにします。
-   - **トレーニング設定**：Hugging Faceの`TrainingArguments`やTRLの`SFTConfig`などのツールを使用してトレーニングパラメータを設定します。これには、バッチサイズ、学習率、およびリソース使用を最適化するための勾配蓄積ステップが含まれます。
-   - **最適化技術**：トレーニング中のメモリを節約するために**gradient checkpointing**を使用します。メモリ要件を削減し、計算速度を向上させるために量子化モデルを使用します。
-   - TRLライブラリの`SFTTrainer`トレーナーを使用して、トレーニングプロセスを簡素化します。
-
-## 好みの最適化
-
-好みの最適化、特に直接好みの最適化（DPO）は、ビジョン言語モデル（VLM）を人間の好みに合わせるためのトレーニング手法です。事前定義された指示に厳密に従うのではなく、モデルは人間が主観的に好む出力を優先することを学習します。このアプローチは、創造的な判断、微妙な推論、または許容される回答が異なるタスクに特に有用です。
-
-### **概要**
-好みの最適化は、主観的な人間の好みがタスクの成功にとって重要なシナリオに対処します。好みをエンコードしたデータセットでファインチューニングすることで、DPOはモデルの応答生成能力を強化し、ユーザーの期待に文脈的およびスタイル的に一致させます。この方法は、創造的な執筆、顧客との対話、または複数の選択肢があるシナリオなどのタスクに特に効果的です。
-
-その利点にもかかわらず、好みの最適化にはいくつかの課題があります：
-- **データ品質**：高品質の好みが注釈されたデータセットが必要であり、データ収集がボトルネックになることがあります。
-- **複雑さ**：トレーニングには、好みのペアワイズサンプリングや計算リソースのバランスなどの高度なプロセスが含まれることがあります。
-
-好みのデータセットは、候補出力間の明確な好みをキャプチャする必要があります。たとえば、データセットは質問と2つの応答（1つは好まれ、もう1つは受け入れられない）をペアにすることがあります。モデルは、完全に正しくなくても、より人間の判断に一致する好ましい応答を予測することを学習します。
-
-### **使用方法**
-1. **データセットの準備**  
-   トレーニングには、好みがラベル付けされたデータセットが重要です。各例は通常、プロンプト（例：画像と質問）と2つの候補応答（1つは選択され、もう1つは拒否される）で構成されます。たとえば：
-
-   - **質問**：家族は何人ですか？  
-     - **拒否**：画像には家族に関する情報はありません。  
-     - **選択**：画像には18,000家族のユニオン組織のテーブル設定が表示されています。  
-
-   データセットは、モデルに完全に正しくなくても、より人間の判断に一致する好ましい応答を優先することを教えます。
-
-2. **モデル設定**  
-   事前トレーニングされたVLMをロードし、DPOをサポートするHugging FaceのTRLライブラリと統合し、テキストと画像の入力を準備するためのプロセッサを使用します。モデルを教師あり学習に設定し、ハードウェアに適した設定を行います。
-
-3. **トレーニングパイプライン**  
-   トレーニングには、DPO固有のパラメータの設定が含まれます。プロセスの概要は次のとおりです：
-
-   - **データセットのフォーマット**：各サンプルをプロンプト、画像、および候補応答で構成します。
-   - **損失関数**：好みに基づく損失関数を使用して、好ましい出力を選択するようにモデルを最適化します。
-   - **効率的なトレーニング**：量子化、勾配蓄積、LoRAアダプタなどの技術を組み合わせて、メモリと計算を最適化します。
-
-## リソース
-
-- [Hugging Face Learn: Supervised Fine-Tuning VLMs](https://huggingface.co/learn/cookbook/fine_tuning_vlm_trl) 
-- [Hugging Face Learn: Supervised Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_smol_vlm_sft_trl)  
-- [Hugging Face Learn: Preference Optimization Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_vlm_dpo_smolvlm_instruct)  
-- [Hugging Face Blog: Preference Optimization for VLMs](https://huggingface.co/blog/dpo_vlm)
-
-## 次のステップ
-
-⏩ この統一アプローチを好みの最適化に実装するには、[vlm_finetune_sample.ipynb](./notebooks/vlm_finetune_sample.ipynb)を試してください。
diff --git a/ja/5_vision_language_models/vlm_usage.md b/ja/5_vision_language_models/vlm_usage.md
deleted file mode 100644
index 35f5ac85..00000000
--- a/ja/5_vision_language_models/vlm_usage.md
+++ /dev/null
@@ -1,76 +0,0 @@
-# ビジュアル言語モデル
-
-ビジュアル言語モデル（VLM）は、画像とテキストの間のギャップを埋め、画像キャプションの生成、視覚に基づく質問への回答、テキストと視覚データの関係の理解などの高度なタスクを可能にします。そのアーキテクチャは、両方のモダリティをシームレスに処理するように設計されています。
-
-### アーキテクチャ
-
-VLMは、画像処理コンポーネントとテキスト生成モデルを組み合わせて、統一された理解を実現します。主な要素は次のとおりです：
-
-![VLMアーキテクチャ](./images/VLM_Architecture.png)
-
-- **画像エンコーダ**：生の画像をコンパクトな数値表現に変換します。CLIPやビジョントランスフォーマー（ViT）などの事前トレーニングされたエンコーダが一般的に使用されます。
-- **埋め込みプロジェクタ**：画像特徴をテキスト埋め込みと互換性のある空間にマッピングします。通常、密な層や線形変換を使用します。
-- **テキストデコーダ**：マルチモーダル情報を翻訳して一貫したテキストを生成する言語生成コンポーネントです。LlamaやVicunaなどの生成モデルが例として挙げられます。
-- **マルチモーダルプロジェクタ**：画像とテキストの表現をブレンドするための追加の層を提供します。LLaVAのようなモデルでは、2つのモダリティ間の強力な接続を確立するために重要です。
-
-ほとんどのVLMは、事前トレーニングされた画像エンコーダとテキストデコーダを活用し、ペアになった画像テキストデータセットで追加のファインチューニングを行います。このアプローチにより、効率的なトレーニングが可能になり、モデルが効果的に一般化できるようになります。
-
-### 使用方法
-
-![VLMプロセス](./images/VLM_Process.png)
-
-VLMは、さまざまなマルチモーダルタスクに適用されます。その適応性により、さまざまなドメインで異なるレベルのファインチューニングを行うことができます：
-
-- **画像キャプション生成**：画像の説明を生成します。
-- **視覚質問応答（VQA）**：画像の内容に関する質問に答えます。
-- **クロスモーダル検索**：特定の画像に対応するテキストを見つける、またはその逆を行います。
-- **クリエイティブなアプリケーション**：デザイン支援、アート生成、魅力的なマルチメディアコンテンツの作成などに役立ちます。
-
-![VLMの使用](./images/VLM_Usage.png)
-
-VLMのトレーニングとファインチューニングには、画像とテキストの注釈がペアになった高品質なデータセットが必要です。Hugging Faceの`transformers`ライブラリなどのツールを使用すると、事前トレーニングされたVLMに簡単にアクセスでき、カスタムファインチューニングのワークフローが簡素化されます。
-
-### チャット形式
-
-多くのVLMは、使いやすさを向上させるためにチャットボットのような形式で構造化されています。この形式には次の要素が含まれます：
-
-- モデルの役割やコンテキストを設定する**システムメッセージ**（例：「あなたは視覚データを分析するアシスタントです」）。
-- テキスト入力と関連する画像を組み合わせた**ユーザークエリ**。
-- マルチモーダル分析から得られたテキスト出力を提供する**アシスタントの応答**。
-
-この会話形式は直感的であり、特にカスタマーサービスや教育ツールなどのインタラクティブなアプリケーションにおいて、ユーザーの期待に沿ったものです。
-
-フォーマットされた入力の例を次に示します：
-
-```json
-[
-    {
-        "role": "system",
-        "content": [{"type": "text", "text": "あなたはチャート画像の視覚データを解釈する専門のビジュアル言語モデルです..."}]
-    },
-    {
-        "role": "user",
-        "content": [
-            {"type": "image", "image": "<image_data>"},
-            {"type": "text", "text": "棒グラフの最高値は何ですか？"}
-        ]
-    },
-    {
-        "role": "assistant",
-        "content": [{"type": "text", "text": "42"}]
-    }
-]
-```
-
-**複数の画像やビデオの処理**
-
-VLMは、入力構造を適応させることで、複数の画像やビデオを処理することもできます。ビデオの場合、フレームを抽出して個々の画像として処理し、時間順序を維持します。
-
-## リソース
-
-- [Hugging Face Blog: Vision Language Models](https://huggingface.co/blog/vlms)
-- [Hugging Face Blog: SmolVLM](https://huggingface.co/blog/smolvlm)
-
-## 次のステップ
-
-⏩ SMOLVLMのさまざまな使用方法を試すには、[vlm_usage_sample.ipynb](./notebooks/vlm_usage_sample.ipynb)を試してください。
diff --git a/ja/6_synthetic_datasets/README.md b/ja/6_synthetic_datasets/README.md
deleted file mode 100644
index c898cb26..00000000
--- a/ja/6_synthetic_datasets/README.md
+++ /dev/null
@@ -1,39 +0,0 @@
-# 合成データセット
-
-合成データは、実際の使用状況を模倣する人工的に生成されたデータです。データセットを拡張または強化することで、データの制限を克服することができます。合成データはすでにいくつかのユースケースで使用されていましたが、大規模な言語モデルは、言語モデルの事前および事後トレーニング、および評価のための合成データセットをより一般的にしました。
-
-私たちは、検証済みの研究論文に基づいた迅速で信頼性が高くスケーラブルなパイプラインを必要とするエンジニアのための合成データとAIフィードバックのフレームワークである[`distilabel`](https://distilabel.argilla.io/latest/)を使用します。パッケージとベストプラクティスの詳細については、[ドキュメント](https://distilabel.argilla.io/latest/)を参照してください。
-
-## モジュール概要
-
-言語モデルの合成データは、インストラクション、嗜好、批評の3つの分類に分類できます。私たちは、インストラクションチューニングと嗜好調整のためのデータセットの生成に焦点を当てます。両方のカテゴリでは、既存のデータをモデルの批評とリライトで改善するための第3のカテゴリの側面もカバーします。
-
-![合成データの分類](./images/taxonomy-synthetic-data.png)
-
-## コンテンツ
-
-### 1. [インストラクションデータセット](./instruction_datasets.md)
-
-インストラクションチューニングのためのインストラクションデータセットの生成方法を学びます。基本的なプロンプトを使用してインストラクションチューニングデータセットを作成する方法や、論文から得られたより洗練された技術を使用する方法を探ります。SelfInstructやMagpieのような方法を使用して、インコンテキスト学習のためのシードデータを使用してインストラクションチューニングデータセットを作成できます。さらに、EvolInstructを通じたインストラクションの進化についても探ります。[学び始める](./instruction_datasets.md)。
-
-### 2. [嗜好データセット](./preference_datasets.md)
-
-嗜好調整のための嗜好データセットの生成方法を学びます。セクション1で紹介した方法と技術を基に構築し、追加の応答を生成します。次に、EvolQualityプロンプトを使用して応答を改善する方法を学びます。最後に、スコアと批評を生成するUltraFeedbackプロンプトを探り、嗜好ペアを作成します。[学び始める](./preference_datasets.md)。
-
-### 演習ノートブック
-
-| タイトル | 説明 | 演習 | リンク | Colab |
-|-------|-------------|----------|------|-------|
-| インストラクションデータセット | インストラクションチューニングのためのデータセットを生成する | 🐢 インストラクションチューニングデータセットを生成する <br> 🐕 シードデータを使用してインストラクションチューニングのためのデータセットを生成する <br> 🦁 シードデータとインストラクションの進化を使用してインストラクションチューニングのためのデータセットを生成する | [リンク](./notebooks/instruction_sft_dataset.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb) |
-| 嗜好データセット | 嗜好調整のためのデータセットを生成する | 🐢 嗜好調整データセットを生成する <br> 🐕 応答の進化を使用して嗜好調整のためのデータセットを生成する <br> 🦁 応答の進化と批評を使用して嗜好調整のためのデータセットを生成する | [リンク](./notebooks/preference_alignment_dataset.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/6_synthetic_datasets/notebooks/preference_alignment_dataset.ipynb) |
-
-## リソース
-
-- [Distilabel Documentation](https://distilabel.argilla.io/latest/)
-- [Synthetic Data Generator is UI app](https://huggingface.co/blog/synthetic-data-generator)
-- [SmolTalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk)
-- [Self-instruct](https://arxiv.org/abs/2212.10560)
-- [Evol-Instruct](https://arxiv.org/abs/2304.12244)
-- [Magpie](https://arxiv.org/abs/2406.08464)
-- [UltraFeedback](https://arxiv.org/abs/2310.01377)
-- [Deita](https://arxiv.org/abs/2312.15685)
diff --git a/ja/6_synthetic_datasets/images/pipeline.png b/ja/6_synthetic_datasets/images/pipeline.png
deleted file mode 100644
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zr<E17ofK66b;+B2YtY;;mO|d1P;K^EVbEcr-N|gZ1_k(%6P*52zFgZnM(NeWe)?|f
zBz{rd-y$PT%cQBJ(}MY+qSASdW1J}>^_^AhMtj_qhP7-W+Pnyn+fA}h`$_}vdmOLe
zq!i-#OgHvv4bkv8__tq_(b=y7pezsyfcq_R_i`)>Vuu8{Z$k3)Mviw}g1(u;9I#EE
zk$<xy7z}JGFN9FK@!bZ8w!Xi>`03N9ZHtYJQs}@ZYo6ai!v8am10oQhpJS253~C4r
z^#BD5I%XGd=eM)?Nta1i;!o(#4tfjXTv14s^=PGr7DEi38($+WT=$b#`xVG0>f|`^
zn*Ix)y_LOo5)<<>J!7-KuR)AcS$Na3tq<VBcXss)dQ!8m`}pYOJ2~8x!IIz^&JI4)
zxlc+zQX^sA$!hH3v75=p&c)4L%)?Q#XSt;F*TJR|72IJoTA`7X$1bN3L(TWCnEmKr
zXKCck9UWx`Lfe6hjJqspbfOrh-IzaVj{h`9lOh2Ik?d$hj};n^3B@?c{XeEdbU`EM
z^;`yl53axUIxrqSF(ZC(3Cn*f|Fvj_MhAlurfT4ev44vpaPNi^gcuJc_zSW6PXHh$
opsN_rXW)ON{#rGG^wh~Y(rU);Y?(VFc)(8?sV@Ig)*|$O0KvT#?*IS*

diff --git a/ja/6_synthetic_datasets/instruction_datasets.md b/ja/6_synthetic_datasets/instruction_datasets.md
deleted file mode 100644
index b4ae48d1..00000000
--- a/ja/6_synthetic_datasets/instruction_datasets.md
+++ /dev/null
@@ -1,197 +0,0 @@
-# インストラクションデータセットの生成
-
-[インストラクションチューニングの章](../1_instruction_tuning/README.md)では、教師あり微調整によるモデルの微調整について学びました。このセクションでは、SFTのためのインストラクションデータセットの生成方法を探ります。基本的なプロンプトを使用してインストラクションチューニングデータセットを作成する方法や、論文から得られたより洗練された技術を使用する方法を探ります。インストラクションチューニングデータセットは、SelfInstructやMagpieのような方法を使用して、インコンテキスト学習のためのシードデータを使用して作成できます。さらに、EvolInstructを通じたインストラクションの進化についても探ります。最後に、distilabelパイプラインを使用してインストラクションチューニングのためのデータセットを生成する方法を探ります。
-
-## プロンプトからデータへ
-
-合成データは一見複雑に見えますが、モデルから知識を抽出するための効果的なプロンプトを作成することに簡略化できます。つまり、特定のタスクのためのデータを生成する方法と考えることができます。課題は、データが多様で代表的であることを保証しながら、効果的にプロンプトを作成することです。幸いなことに、多くの論文がこの問題を探求しており、このコースではいくつかの有用なものを探ります。まずは、手動でプロンプトを作成して合成データを生成する方法を探ります。
-
-### 基本的なプロンプト
-
-基本的な例から始めましょう。[HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct)モデルを`distilabel`ライブラリの`transformers`統合を使用してロードします。`TextGeneration`クラスを使用して合成`プロンプト`を生成し、それを使用して`completion`を生成します。
-
-次に、`distilabel`ライブラリを使用してモデルをロードします。
-
-```python
-from distilabel.llms import TransformersLLM
-from distilabel.steps.tasks import TextGeneration
-
-llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
-gen = TextGeneration(llm=llm)
-gen.load()
-```
-
-!!! note
-    Distilabelは`llm`をメモリにロードするため、ノートブックで作業する場合は、メモリの問題を避けるために使用後に`gen.unload()`する必要があります。
-
-次に、`llm`を使用してインストラクションチューニングのための`プロンプト`を生成します。
-
-```python
-next(gen.process([{"instruction": "Hugging Faceの小規模AIモデルに関するSmol-Courseについての質問を生成してください。"}]))
-# Smol-Courseの目的は何ですか？
-```
-
-最後に、同じ`プロンプト`を入力として使用して`completion`を生成します。
-
-```python
-next(gen.process([{"instruction": "Smol-Courseの目的は何ですか？"}]))
-# Smol-Courseはコンピュータサイエンスの概念を学ぶためのプラットフォームです。
-```
-
-素晴らしい！合成`プロンプト`と対応する`completion`を生成できました。このシンプルなアプローチをスケールアップして、より多くのデータを生成することができますが、データの品質はそれほど高くなく、コースやドメインのニュアンスを考慮していません。さらに、現在のコードを再実行すると、データがそれほど多様でないことがわかります。幸いなことに、この問題を解決する方法があります。
-
-### SelfInstruct
-
-SelfInstructは、シードデータセットに基づいて新しいインストラクションを生成するプロンプトです。このシードデータは単一のインストラクションやコンテキストの一部である場合があります。プロセスは、初期のシードデータのプールから始まります。言語モデルは、インコンテキスト学習を使用してこのシードデータに基づいて新しいインストラクションを生成するようにプロンプトされます。このプロンプトは[distilabelで実装されています](https://github.com/argilla-io/distilabel/blob/main/src/distilabel/steps/tasks/templates/self-instruct.jinja2)が、簡略化されたバージョンは以下の通りです：
-
-```
-# タスクの説明
-与えられたAIアプリケーションが受け取ることができる{{ num_instructions }}のユーザークエリを開発してください。モデルのテキスト能力内で動詞と言語構造の多様性を強調してください。
-
-# コンテキスト
-{{ input }}
-
-# 出力
-```
-
-これを使用するには、`llm`を[SelfInstructクラス](https://distilabel.argilla.io/dev/components-gallery/tasks/selfinstruct/)に渡す必要があります。[プロンプトからデータセクション](#prompt-to-data)のテキストをコンテキストとして使用し、新しいインストラクションを生成してみましょう。
-
-```python
-from distilabel.steps.tasks import SelfInstruct
-
-self_instruct = SelfInstruct(llm=llm)
-self_instruct.load()
-
-context = "<prompt_to_data_section>"
-
-next(self_instruct.process([{"input": text}]))["instructions"][0]
-# 手動プロンプトを使用して合成データを生成するプロセスは何ですか？
-```
-
-生成されたインストラクションはすでにかなり良くなっており、実際のコンテンツやドメインに適しています。しかし、プロンプトを進化させることでさらに良くすることができます。
-
-### EvolInstruct
-
-EvolInstructは、入力インストラクションを進化させて、同じインストラクションのより良いバージョンにするプロンプト技術です。このより良いバージョンは、一連の基準に従って定義され、元のインストラクションに制約、深化、具体化、推論、または複雑化を追加します。このプロセスは、元のインストラクションのさまざまな進化を作成するために複数回繰り返すことができ、理想的には元のインストラクションのより良いバージョンに導きます。このプロンプトは[distilabelで実装されています](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/evol_instruct)が、簡略化されたバージョンは以下の通りです：
-
-```
-プロンプトリライターとして行動してください。
-与えられたプロンプトを、より複雑なバージョンに書き換えてください。
-以下の基準に基づいてプロンプトを複雑化してください：
-{{ criteria }}
-
-# プロンプト
-{{ input }}
-
-# 出力
-```
-
-これを使用するには、`llm`を[EvolInstructクラス](https://distilabel.argilla.io/dev/components-gallery/tasks/evolinstruct/)に渡す必要があります。[SelfInstructセクション](#selfinstruct)の合成プロンプトを入力として使用し、それをより良いバージョンに進化させてみましょう。この例では、1世代だけ進化させます。
-
-```python
-from distilabel.steps.tasks import EvolInstruct
-
-evol_instruct = EvolInstruct(llm=llm, num_evolutions=1)
-evol_instruct.load()
-
-text = "手動プロンプトを使用して合成データを生成するプロセスは何ですか？"
-
-next(evol_instruct.process([{"instruction": text}]))
-# 手動プロンプトを使用して合成データを生成するプロセスは何ですか？
-# そして、人工知能システムであるGPT4が機械学習アルゴリズムを使用して入力データを合成データに変換する方法は？
-```
-
-インストラクションはより複雑になりましたが、元の意味を失っています。したがって、進化させることは両刃の剣であり、生成するデータの品質に注意する必要があります。
-
-### Magpie
-
-Magpieは、言語モデルの自己回帰的要因と、インストラクションチューニングプロセス中に使用されていた[チャットテンプレート](../1_instruction_tuning/chat_templates.md)に依存する技術です。覚えているかもしれませんが、チャットテンプレートは、システム、ユーザー、アシスタントの役割を明確に示す形式で会話を構造化します。インストラクションチューニングフェーズ中に、言語モデルはこの形式を再現するように最適化されており、Magpieはそれを利用します。チャットテンプレートに基づいた事前クエリプロンプトから始めますが、ユーザーメッセージインジケーター、例：<|im_end|>ユーザー\nの前で停止し、その後、言語モデルを使用してユーザープロンプトを生成し、アシスタントインジケーター、例：<|im_end|>まで生成します。このアプローチにより、多くのデータを非常に効率的に生成でき、マルチターンの会話にもスケールアップできます。この生成されたデータは、使用されたモデルのインストラクションチューニングフェーズのトレーニングデータを再現すると仮定されています。
-
-このシナリオでは、プロンプトテンプレートはチャットテンプレート形式に基づいているため、モデルごとに異なります。しかし、プロセスをステップバイステップで簡略化して説明できます。
-
-```bash
-# ステップ1：事前クエリプロンプトを提供する
-<|im_end|>ユーザー\n
-
-# ステップ2：言語モデルがユーザープロンプトを生成する
-<|im_end|>ユーザー\n
-Smol-Courseの目的は何ですか？
-
-# ステップ3：生成を停止する
-<|im_end|>
-```
-
-distilabelで使用するには、`llm`を[Magpieクラス](https://distilabel.argilla.io/dev/components-gallery/tasks/magpie/)に渡す必要があります。
-
-```python
-from distilabel.steps.tasks import Magpie
-
-magpie = Magpie(llm=llm)
-magpie.load()
-
-next(magpie.process([{"system_prompt": "あなたは役立つアシスタントです。"}]))
-# [{
-#   "role": "user",
-#   "content": "トップ3の大学のリストを提供できますか？"
-# },
-# {
-#   "role": "assistant",
-#   "content": "トップ3の大学は：MIT、イェール、スタンフォードです。"
-# }]
-```
-
-すぐに`プロンプト`と`completion`を含むデータセットが得られます。ドメインに特化したパフォーマンスを向上させるために、`system_prompt`に追加のコンテキストを挿入できます。LLMが特定のドメインデータを生成するために、システムプロンプトでユーザーのクエリがどのようなものかを説明することが役立ちます。これは、ユーザープロンプトを生成する前の事前クエリプロンプトで使用され、LLMがそのドメインのユーザークエリを生成するようにバイアスをかけます。
-
-```
-あなたは数学の問題を解決するためにユーザーを支援するAIアシスタントです。
-```
-
-システムプロンプトに追加のコンテキストを渡すことは、一般的に言語モデルが最適化されていないため、カスタマイズには他の技術ほど効果的ではないことが多いです。
-
-### プロンプトからパイプラインへ
-
-これまで見てきたクラスはすべて、パイプラインで使用できるスタンドアロンのクラスです。これは良いスタートですが、`Pipeline`クラスを使用してデータセットを生成することでさらに良くすることができます。`TextGeneration`ステップを使用してインストラクションチューニングのための合成データセットを生成します。パイプラインは、データをロードするための`LoadDataFromDicts`ステップ、`プロンプト`を生成するための`TextGeneration`ステップ、およびそのプロンプトの`completion`を生成するためのステップで構成されます。ステップを接続し、`>>`演算子を使用してデータをパイプラインに流します。distilabelの[ドキュメント](https://distilabel.argilla.io/dev/components-gallery/tasks/textgeneration/#input-output-columns)には、ステップの入力および出力列に関する情報が記載されています。データがパイプラインを正しく流れるようにするために、`output_mappings`パラメータを使用して出力列を次のステップの入力列にマッピングします。
-
-```python
-from distilabel.llms import TransformersLLM
-from distilabel.pipeline import Pipeline
-from distilabel.steps import LoadDataFromDicts
-from distilabel.steps.tasks import TextGeneration
-
-with Pipeline() as pipeline:
-    data = LoadDataFromDicts(data=[{"instruction": "Hugging Faceの小規模AIモデルに関するSmol-Courseについての短い質問を生成してください。"}])
-    llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
-    gen_a = TextGeneration(llm=llm, output_mappings={"generation": "instruction"})
-    gen_b = TextGeneration(llm=llm, output_mappings={"generation": "response"})
-    data >> gen_a >> gen_b
-
-if __name__ == "__main__":
-    distiset = pipeline.run(use_cache=False)
-    print(distiset["default"]["train"][0])
-# [{
-#   "instruction": "Smol-Courseの目的は何ですか？",
-#   "response": "Smol-Courseはコンピュータサイエンスの概念を学ぶためのプラットフォームです。"
-# }]
-```
-
-このパイプラインの背後には多くのクールな機能があります。生成結果を自動的にキャッシュするため、生成ステップを再実行する必要がありません。フォールトトレランスが組み込まれており、生成ステップが失敗してもパイプラインは実行を続けます。また、すべての生成ステップを並行して実行するため、生成が高速です。`draw`メソッドを使用してパイプラインを視覚化することもできます。ここでは、データがパイプラインをどのように流れ、`output_mappings`が出力列を次のステップの入力列にどのようにマッピングするかを確認できます。
-
-![Pipeline](./images/pipeline.png)
-
-## ベストプラクティス
-
-- 多様なシードデータを確保して、さまざまなシナリオをカバーする
-- 生成されたデータが多様で高品質であることを定期的に評価する
-- データの品質を向上させるために（システム）プロンプトを繰り返し改善する
-
-## 次のステップ
-
-👨🏽‍💻 コード - [演習ノートブック](./notebooks/instruction_sft_dataset.ipynb)でインストラクションチューニングのためのデータセットを生成する
-🧑‍🏫 学ぶ - [preference datasetsの生成](./preference_datasets.md)について学ぶ
-
-## 参考文献
-
-- [Distilabel Documentation](https://distilabel.argilla.io/latest/)
-- [Self-instruct](https://arxiv.org/abs/2212.10560)
-- [Evol-Instruct](https://arxiv.org/abs/2304.12244)
-- [Magpie](https://arxiv.org/abs/2406.08464)
diff --git a/ja/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb b/ja/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb
deleted file mode 100644
index 69c72fbb..00000000
--- a/ja/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb
+++ /dev/null
@@ -1,119 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# インストラクションチューニングのためのデータセットを生成する\n",
-    "\n",
-    "このノートブックでは、インストラクションチューニングのためのデータセットを生成するプロセスを案内します。`distilabel`パッケージを使用してインストラクションチューニングのためのデータセットを生成します。\n",
-    "\n",
-    "それでは、インストラクションチューニングデータセットを見てみましょう。\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>演習: インストラクションチューニングのためのデータセットを生成する</h2>\n",
-    "    <p>インストラクションチューニングのためのデータセットを生成する方法を見たので、実際にインストラクションチューニングのためのデータセットを生成してみましょう。</p>\n",
-    "    <p><b>難易度レベル</b></p>\n",
-    "    <p>🐢 インストラクションチューニングデータセットを生成する</p>\n",
-    "    <p>🐕 シードデータを使用してインストラクションチューニングのためのデータセットを生成する</p>\n",
-    "    <p>🦁 シードデータとインストラクションの進化を使用してインストラクションチューニングのためのデータセットを生成する</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "vscode": {
-     "languageId": "plaintext"
-    }
-   },
-   "source": [
-    "## 依存関係のインストール\n",
-    "\n",
-    "transformersの代わりに、`vllm`や`hf-inference-endpoints`をインストールすることもできます。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install \"distilabel[hf-transformers,outlines,instructor]\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 合成の開始\n",
-    "\n",
-    "前のコース内容で見たように、インストラクションデータセット生成のためのdistilabelパイプラインを作成できます。最低限のパイプラインはすでに提供されています。このパイプラインをスケールアップして、インストラクションチューニングのための大規模なデータセットを生成してください。モデル、モデルプロバイダー、生成引数を入れ替えて、データセットの品質にどのように影響するかを確認してください。小規模に実験し、後でスケールアップしましょう。\n",
-    "\n",
-    "[distilabelコンポーネントギャラリー](https://distilabel.argilla.io/latest/components-gallery/)で、処理クラスの情報とその使用方法を確認してください。\n",
-    "\n",
-    "辞書からデータをロードする代わりに、Hubからデータをロードする例を以下に示します。\n",
-    "\n",
-    "```python\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "with Pipeline(...) as pipeline:\n",
-    "    ...\n",
-    "\n",
-    "if __name__ == \"__main__:\n",
-    "    dataset = load_dataset(\"my-dataset\", split=\"train\")\n",
-    "    distiset = pipeline.run(dataset=dataset)\n",
-    "```\n",
-    "\n",
-    "パイプラインを実行した後、データセットをHubにプッシュするのを忘れないでください！"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from distilabel.llms import TransformersLLM\n",
-    "from distilabel.pipeline import Pipeline\n",
-    "from distilabel.steps import LoadDataFromDicts\n",
-    "from distilabel.steps.tasks import TextGeneration\n",
-    "\n",
-    "with Pipeline() as pipeline:\n",
-    "    data = LoadDataFromDicts(data=[{\"instruction\": \"Hugging Faceの小規模AIモデルに関するSmol-Courseについての短い質問を生成してください。\"}])\n",
-    "    llm = TransformersLLM(model=\"HuggingFaceTB/SmolLM2-1.7B-Instruct\")\n",
-    "    gen_a = TextGeneration(llm=llm, output_mappings={\"generation\": \"instruction\"})\n",
-    "    gen_b = TextGeneration(llm=llm, output_mappings={\"generation\": \"response\"})\n",
-    "    data >> gen_a >> gen_b\n",
-    "\n",
-    "if __name__ == \"__main__\":\n",
-    "    distiset = pipeline.run(use_cache=False)\n",
-    "    distiset.push_to_hub(\"huggingface-smol-course-instruction-tuning-dataset\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 🌯 まとめ\n",
-    "\n",
-    "インストラクションチューニングのためのデータセットを生成する方法を見てきました。これを使用して次のことができます：\n",
-    "\n",
-    "- インストラクションチューニングのためのデータセットを生成する。\n",
-    "- インストラクションチューニングのための評価データセットを作成する。\n",
-    "\n",
-    "次に\n",
-    "\n",
-    "🧑‍🏫 学ぶ - [preference datasetsの生成](./preference_datasets.md)について学ぶ\n",
-    "🏋️‍♂️ 合成データセットに基づいて[インストラクションチューニングの章](../../1_instruction_tuning/README.md)でモデルを微調整する\n"
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/ja/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb b/ja/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb
deleted file mode 100644
index 2dc1835b..00000000
--- a/ja/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb
+++ /dev/null
@@ -1,120 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 嗜好調整のためのデータセットを生成する\n",
-    "\n",
-    "このノートブックでは、嗜好調整のためのデータセットを生成するプロセスを案内します。`distilabel`パッケージを使用して嗜好調整のためのデータセットを生成します。\n",
-    "\n",
-    "それでは、嗜好調整データセットを見てみましょう。\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>演習: 嗜好調整のためのデータセットを生成する</h2>\n",
-    "    <p>嗜好調整のためのデータセットを生成する方法を見たので、実際に嗜好調整のためのデータセットを生成してみましょう。</p>\n",
-    "    <p><b>難易度レベル</b></p>\n",
-    "    <p>🐢 嗜好調整のためのデータセットを生成する</p>\n",
-    "    <p>🐕 応答の進化を使用して嗜好調整のためのデータセットを生成する</p>\n",
-    "    <p>🦁 応答の進化とモデルプーリングを使用して嗜好調整のためのデータセットを生成する</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "vscode": {
-     "languageId": "plaintext"
-    }
-   },
-   "source": [
-    "## 依存関係のインストール\n",
-    "\n",
-    "transformersの代わりに、`vllm`や`hf-inference-endpoints`をインストールすることもできます。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install \"distilabel[hf-transformers,outlines,instructor]\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 合成の開始\n",
-    "\n",
-    "前のノートブックで見たように、嗜好データセット生成のためのdistilabelパイプラインを作成できます。最低限のパイプラインはすでに提供されています。このパイプラインをスケールアップして、嗜好調整のための大規模なデータセットを生成してください。モデル、モデルプロバイダー、生成引数を入れ替えて、データセットの品質にどのように影響するかを確認してください。小規模に実験し、後でスケールアップしましょう。\n",
-    "\n",
-    "[distilabelコンポーネントギャラリー](https://distilabel.argilla.io/latest/components-gallery/)で、処理クラスの情報とその使用方法を確認してください。\n",
-    "\n",
-    "辞書からデータをロードする代わりに、Hubからデータをロードする例を以下に示します。\n",
-    "\n",
-    "```python\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "with Pipeline(...) as pipeline:\n",
-    "    ...\n",
-    "\n",
-    "if __name__ == \"__main__:\n",
-    "    dataset = load_dataset(\"my-dataset\", split=\"train\")\n",
-    "    distiset = pipeline.run(dataset=dataset)\n",
-    "```\n",
-    "\n",
-    "パイプラインを実行した後、データセットをHubにプッシュするのを忘れないでください！"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from distilabel.llms import TransformersLLM\n",
-    "from distilabel.pipeline import Pipeline\n",
-    "from distilabel.steps import GroupColumns, LoadDataFromDicts\n",
-    "from distilabel.steps.tasks import TextGeneration\n",
-    "\n",
-    "with Pipeline() as pipeline:\n",
-    "    data = LoadDataFromDicts(data=[{\"instruction\": \"合成データとは何ですか？\"}])\n",
-    "    llm_a = TransformersLLM(model=\"HuggingFaceTB/SmolLM2-1.7B-Instruct\")\n",
-    "    gen_a = TextGeneration(llm=llm_a)\n",
-    "    llm_b = TransformersLLM(model=\"Qwen/Qwen2.5-1.5B-Instruct\")\n",
-    "    gen_b = TextGeneration(llm=llm_b)\n",
-    "    group = GroupColumns(columns=[\"generation\"])\n",
-    "    data >> [gen_a, gen_b] >> group\n",
-    "\n",
-    "if __name__ == \"__main__\":\n",
-    "    distiset = pipeline.run()\n",
-    "    distiset.push_to_hub(\"huggingface-smol-course-preference-tuning-dataset\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 🌯 まとめ\n",
-    "\n",
-    "嗜好調整のためのデータセットを生成する方法を見てきました。これを使用して次のことができます：\n",
-    "\n",
-    "- 嗜好調整のためのデータセットを生成する。\n",
-    "- 嗜好調整のための評価データセットを作成する。\n",
-    "\n",
-    "次に\n",
-    "\n",
-    "🏋️‍♂️ 合成データセットに基づいて[嗜好調整の章](../../2_preference_alignment/README.md)でモデルを微調整する\n"
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/ja/6_synthetic_datasets/preference_datasets.md b/ja/6_synthetic_datasets/preference_datasets.md
deleted file mode 100644
index 92a29b29..00000000
--- a/ja/6_synthetic_datasets/preference_datasets.md
+++ /dev/null
@@ -1,155 +0,0 @@
-# 嗜好データセットの生成
-
-[嗜好調整の章](../2_preference_alignment/README.md)では、直接嗜好最適化について学びました。このセクションでは、DPOのような方法のための嗜好データセットを生成する方法を探ります。[インストラクションデータセットの生成](./instruction_datasets.md)で紹介した方法を基に構築します。さらに、基本的なプロンプトを使用してデータセットに追加の完了を追加する方法や、EvolQualityを使用して応答の品質を向上させる方法を示します。最後に、UltraFeedbackを使用してスコアと批評を生成する方法を示します。
-
-## 複数の完了を作成する
-
-嗜好データは、同じ`インストラクション`に対して複数の`完了`を持つデータセットです。モデルにプロンプトを与えて追加の`完了`を生成することで、データセットにより多くの`完了`を追加できます。この際、2つ目の完了が全体的な品質や表現において最初の完了とあまり似ていないことを確認する必要があります。これは、モデルが明確な嗜好に最適化される必要があるためです。通常、`選ばれた`と`拒否された`と呼ばれる完了のどちらが好まれるかを知りたいのです。[スコアの作成セクション](#creating-scores)で、選ばれた完了と拒否された完了を決定する方法について詳しく説明します。
-
-### モデルプーリング
-
-異なるモデルファミリーからモデルを使用して2つ目の完了を生成することができます。これをモデルプーリングと呼びます。2つ目の完了の品質をさらに向上させるために、`温度`を調整するなど、異なる生成引数を使用することができます。最後に、異なるプロンプトテンプレートやシステムプロンプトを使用して2つ目の完了を生成し、特定の特性に基づいて多様性を確保することができます。理論的には、異なる品質の2つのモデルを使用し、より良いものを`選ばれた`完了として使用することができます。
-
-まず、[Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)と[HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct)モデルを`transformers`統合の`distilabel`ライブラリを使用してロードします。これらのモデルを使用して、与えられた`プロンプト`に対して2つの合成`応答`を作成します。`LoadDataFromDicts`、`TextGeneration`、および`GroupColumns`を使用して別のパイプラインを作成します。最初にデータをロードし、次に2つの生成ステップを使用し、最後に結果をグループ化します。ステップを接続し、`>>`演算子と`[]`を使用してデータをパイプラインに流します。これは、前のステップの出力を次のステップの入力として使用することを意味します。
-
-```python
-from distilabel.llms import TransformersLLM
-from distilabel.pipeline import Pipeline
-from distilabel.steps import GroupColumns, LoadDataFromDicts
-from distilabel.steps.tasks import TextGeneration
-
-with Pipeline() as pipeline:
-    data = LoadDataFromDicts(data=[{"instruction": "合成データとは何ですか？"}])
-    llm_a = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
-    gen_a = TextGeneration(llm=llm_a)
-    llm_b = TransformersLLM(model="Qwen/Qwen2.5-1.5B-Instruct")
-    gen_b = TextGeneration(llm=llm_b)
-    group = GroupColumns(columns=["generation"])
-    data >> [gen_a, gen_b] >> group
-
-if __name__ == "__main__":
-    distiset = pipeline.run()
-    print(distiset["default"]["train"]["grouped_generation"][0])
-# {[
-#   '合成データは、実際の使用状況を模倣する人工的に生成されたデータです。',
-#   '合成データとは、人工的に生成されたデータを指します。'
-# ]}
-```
-
-ご覧のとおり、与えられた`プロンプト`に対して2つの合成`完了`があります。生成ステップを特定の`システムプロンプト`で初期化するか、`TransformersLLM`に生成引数を渡すことで、多様性をさらに向上させることができます。次に、EvolQualityを使用して`完了`の品質を向上させる方法を見てみましょう。
-
-### EvolQuality
-
-EvolQualityは、[EvolInstruct](./instruction_datasets.md#evolinstruct)に似ていますが、入力`プロンプト`の代わりに`完了`を進化させます。このタスクは、`プロンプト`と`完了`の両方を受け取り、一連の基準に基づいて`完了`をより良いバージョンに進化させます。このより良いバージョンは、役立ち度、関連性、深化、創造性、または詳細の基準に基づいて定義されます。これにより、データセットに追加の`完了`を自動的に生成できます。理論的には、進化したバージョンが元の完了よりも優れていると仮定し、それを`選ばれた`完了として使用することができます。
-
-プロンプトは[distilabelで実装されています](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/evol_quality)が、簡略化されたバージョンは以下の通りです：
-
-```bash
-応答リライターとして行動してください。
-与えられたプロンプトと応答を、より良いバージョンに書き換えてください。
-以下の基準に基づいてプロンプトを複雑化してください：
-{{ criteria }}
-
-# プロンプト
-{{ input }}
-
-# 応答
-{{ output }}
-
-# 改善された応答
-```
-
-これを使用するには、[EvolQualityクラス](https://distilabel.argilla.io/dev/components-gallery/tasks/evolquality/)に`llm`を渡す必要があります。[モデルプーリングセクション](#model-pooling)の合成`プロンプト`と`完了`を使用して、より良いバージョンに進化させてみましょう。この例では、1世代だけ進化させます。
-
-```python
-from distilabel.llms import TransformersLLM
-from distilabel.steps.tasks import EvolQuality
-
-llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
-evol_quality = EvolQuality(llm=llm, num_evolutions=1)
-evol_quality.load()
-
-instruction = "合成データとは何ですか？"
-completion = "合成データは、実際の使用状況を模倣する人工的に生成されたデータです。"
-
-next(evol_quality.process([{
-    "instruction": instruction,
-    "response": completion
-}]))
-# 手動プロンプトを使用して合成データを生成するプロセスは何ですか？
-```
-
-`応答`はより複雑で、`インストラクション`に特化しています。これは良いスタートですが、EvolInstructで見たように、進化した生成物が常に優れているわけではありません。したがって、データセットの品質を確保するために追加の評価技術を使用することが重要です。次のセクションでこれを探ります。
-
-## スコアの作成
-
-スコアは、ある応答が他の応答よりもどれだけ好まれるかを測定するものです。一般的に、これらのスコアは絶対的、主観的、または相対的です。このコースでは、最初の2つに焦点を当てます。これらは嗜好データセットの作成に最も価値があるためです。このスコアリングは、評価技術と重なる部分があり、[評価の章](../3_evaluation/README.md)で見たように、通常はより大きなモデルが必要です。
-
-### UltraFeedback
-
-UltraFeedbackは、与えられた`プロンプト`とその`完了`に対してスコアと批評を生成する技術です。
-
-スコアは、一連の基準に基づいて`完了`の品質を評価します。基準には、`役立ち度`、`関連性`、`深化`、`創造性`の4つの細かい基準があります。これらは便利ですが、一般的には全体的な基準を使用するのが良いスタートです。これにより、スコアの生成プロセスが簡略化されます。スコアは、どの`完了`が`選ばれた`もので、どれが`拒否された`ものであるかを決定するために使用できます。絶対的なスコアであるため、データセットの外れ値を見つけるための興味深いフィルターとしても使用できます。たとえば、最悪の完了や差が大きいペアを見つけることができます。
-
-批評はスコアの理由を提供するために追加されます。これにより、スコアの違いを理解するための追加のコンテキストが提供されます。言語モデルは広範な批評を生成しますが、これは非常に便利ですが、スコアを表す単一のトークンを生成するよりもコストと複雑さが増します。
-
-プロンプトは[distilabelで実装されています](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/templates/ultrafeedback)が、簡略化されたバージョンは以下の通りです：
-
-```bash
-モデルの出力をさまざまな基準に基づいて評価してください：役立ち度、関連性、深化、創造性
-上記の要因に基づいて全体的な評価を提供する役割を果たします。
-出力の全体的な品質を1から5のスコアで評価してください。
-
-以下の形式で回答してください：スコア - 理由
-
-# 入力
-{{ input }}
-
-# 応答
-{{ output }}
-
-# スコア - 理由
-```
-
-これを使用するには、[UltraFeedbackクラス](https://distilabel.argilla.io/dev/components-gallery/tasks/ultrafeedback/)に`llm`を渡す必要があります。[モデルプーリングセクション](#model-pooling)の合成`プロンプト`と`完了`を評価してみましょう。
-
-```python
-from distilabel.llms import TransformersLLM
-from distilabel.steps.tasks import UltraFeedback
-
-llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
-ultrafeedback = UltraFeedback(llm=llm)
-ultrafeedback.load()
-
-instruction = "合成データとは何ですか？"
-completion_a = "合成データは、実際の使用状況を模倣する人工的に生成されたデータです。"
-completion_b = "合成データとは、人工的に生成されたデータを指します。"
-
-next(ultrafeedback.process([{
-    "instruction": instruction,
-    "generations": [completion_a, completion_b]
-}]))
-# [
-#     {
-#         'ratings': [4, 5],
-#         'rationales': ['could have been more specific', 'good definition'],
-#     }
-# ]
-```
-
-## ベストプラクティス
-
-- 全体的なスコアは、批評や特定のスコアよりも安価で生成が容易です
-- スコアや批評を生成するために大きなモデルを使用する
-- スコアや批評を生成するために多様なモデルセットを使用する
-- `system_prompt`やモデルの構成を繰り返し改善する
-
-## 次のステップ
-
-👨🏽‍💻 コード -[演習ノートブック](./notebooks/preference_dpo_dataset.ipynb)でインストラクションチューニングのためのデータセットを生成する
-
-## 参考文献
-
-- [Distilabel Documentation](https://distilabel.argilla.io/latest/)
-- [Deita](https://arxiv.org/abs/2312.15685)
-- [UltraFeedback](https://arxiv.org/abs/2310.01377)
diff --git a/ja/README.md b/ja/README.md
deleted file mode 100644
index b7f0e6e8..00000000
--- a/ja/README.md
+++ /dev/null
@@ -1,90 +0,0 @@
-![smolcourse image](../banner.png)
-
-# スモールコース
-
-これは、特定のユースケースに合わせて言語モデルを調整するための実践的なコースです。ほとんどのローカルマシンで実行できるため、言語モデルの調整を始めるのに便利です。GPUの要件は最小限で、有料サービスは必要ありません。このコースは[SmolLM2](https://github.com/huggingface/smollm/tree/main)シリーズのモデルに基づいていますが、ここで学んだスキルを大規模なモデルや他の小型言語モデルに転用することができます。
-
-<a href="http://hf.co/join/discord">
-<img src="https://img.shields.io/badge/Discord-7289DA?&logo=discord&logoColor=white"/>
-</a>
-
-<div style="background: linear-gradient(to right, #e0f7fa, #e1bee7, orange); padding: 20px; border-radius: 5px; margin-bottom: 20px; color: purple;">
-    <h2>参加は無料で、今すぐ始められます！</h2>
-    <p>このコースはオープンでピアレビューされています。コースに参加するには、<strong>プルリクエストを開く</strong>ことで、あなたの作業をレビューに提出してください。以下の手順に従ってください：</p>
-    <ol>
-        <li>リポジトリをフォークします <a href="https://github.com/huggingface/smol-course/fork">こちら</a></li>
-        <li>資料を読み、変更を加え、演習を行い、自分の例を追加します。</li>
-        <li>december_2024ブランチでプルリクエストを開きます</li>
-        <li>レビューを受けてマージされます</li>
-    </ol>
-    <p>これにより、学習を助け、常に改善されるコミュニティ主導のコースを構築することができます。</p>
-</div>
-
-このプロセスについては、この[ディスカッションスレッド](https://github.com/huggingface/smol-course/discussions/2#discussion-7602932)で議論できます。
-
-## コース概要
-
-このコースは、小型言語モデルを使用した実践的なアプローチを提供し、初期のトレーニングから本番展開までをカバーします。
-
-| モジュール | 説明 | ステータス | リリース日 |
-|--------|-------------|---------|--------------|
-| [インストラクションチューニング](./1_instruction_tuning) | 教師あり微調整、チャットテンプレート、および基本的な指示に従う方法を学びます | ✅ 準備完了 | 2024年12月3日 |
-| [選好整合](./2_preference_alignment) | DPOおよびORPO技術を探求し、人間の選好にモデルを整合させる方法を学びます | ✅ 準備完了  | 2024年12月6日 |
-| [パラメータ効率の良い微調整](./3_parameter_efficient_finetuning) | LoRA、プロンプトチューニング、および効率的な適応方法を学びます | ✅ 準備完了 | 2024年12月9日 |
-| [評価](./4_evaluation) | 自動ベンチマークを使用し、カスタムドメイン評価を作成する方法を学びます | ✅ 準備完了 | 2024年12月13日 |
-| [ビジョン言語モデル](./5_vision_language_models) | マルチモーダルモデルをビジョン言語タスクに適応させる方法を学びます | ✅ 準備完了 | 2024年12月16日 |
-| [合成データセット](./6_synthetic_datasets) | トレーニング用の合成データセットを作成し、検証する方法を学びます | ✅ 準備完了 | 2024年12月20日 |
-| [推論](./7_inference) | モデルを効率的に推論する方法を学びます | [🚧 作業中](https://github.com/huggingface/smol-course/pull/150) | 2025年1月8日 |
-| [エージェント](./8_agents) | 自分のエージェントAIを構築する方法を学びます | ✅ 準備完了 | 2025年1月13日 ||
-| キャップストーンプロジェクト | 学んだことを使ってリーダーボードを登りましょう！ | [🚧 作業中](https://github.com/huggingface/smol-course/pull/97) | 2025年1月10日 |
-
-## なぜ小型言語モデルなのか？
-
-大規模な言語モデルは印象的な能力を示していますが、しばしば多くの計算リソースを必要とし、特定のアプリケーションには過剰な場合があります。小型言語モデルは、ドメイン固有のアプリケーションに対していくつかの利点を提供します：
-
-- **効率性**：トレーニングと展開に必要な計算リソースが大幅に少ない
-- **カスタマイズ**：特定のドメインに簡単に微調整および適応可能
-- **制御**：モデルの動作をよりよく理解し、制御できる
-- **コスト**：トレーニングと推論の運用コストが低い
-- **プライバシー**：データを外部APIに送信せずにローカルで実行可能
-- **グリーンテクノロジー**：リソースの効率的な使用を推進し、炭素排出量を削減
-- **学術研究の容易さ**：最先端のLLMを使用した学術研究のための簡単なスターターを提供し、物流の制約を減らす
-
-## 前提条件
-
-開始する前に、以下を確認してください：
-- 機械学習と自然言語処理の基本的な理解
-- Python、PyTorch、および`transformers`ライブラリに精通していること
-- 事前学習された言語モデルとラベル付きデータセットへのアクセス
-
-## インストール
-
-コースをパッケージとして維持しているため、パッケージマネージャーを使用して依存関係を簡単にインストールできます。`uv`をお勧めしますが、`pip`や`pdm`などの代替手段も使用できます。
-
-### `uv`を使用する場合
-
-`uv`がインストールされている場合、次のようにしてコースをインストールできます：
-
-```bash
-uv venv --python 3.11.0
-uv sync
-```
-
-### `pip`を使用する場合
-
-すべての例は**python 3.11**環境で実行されるため、次のように環境を作成し、依存関係をインストールします：
-
-```bash
-# python -m venv .venv
-# source .venv/bin/activate
-pip install -r requirements.txt
-```
-
-### Google Colab
-
-**Google Colabから**は、使用するハードウェアに基づいて柔軟に依存関係をインストールする必要があります。次のようにします：
-
-```bash
-pip install transformers trl datasets huggingface_hub
-```
-
diff --git a/ko/1_instruction_tuning/README.md b/ko/1_instruction_tuning/README.md
deleted file mode 100644
index f2afebbe..00000000
--- a/ko/1_instruction_tuning/README.md
+++ /dev/null
@@ -1,28 +0,0 @@
-# 지시 조정(Instruction Tuning)
-
-이 모듈에서는 언어 모델의 지시 조정(instruction tuning) 방법을 설명합니다. 지시 조정이란, 사전 학습된 모델을 특정 태스크에 맞게 조정하기 위해 해당 태스크와 관련된 데이터셋으로 추가 학습시키는 과정을 의미합니다. 이를 통해 목표로 하는 작업에서 모델이 더 나은 성능을 발휘할 수 있습니다.
-
-이 모듈에서는 다음 두 가지 주제를 다룰 예정입니다: 1) 대화 템플릿(Chat Templates) 2) 지도 학습 기반 미세 조정(Supervised Fine-Tuning)
-
-## 1️⃣ 대화 템플릿
-
-채팅 템플릿(Chat Templates)은 사용자와 AI 모델 간의 상호작용을 구조화하여 모델의 일관되고 적절한 맥락의 응답을 보장합니다. 템플릿에는 시스템 프롬프트와 역할 기반 메시지와 같은 구성 요소가 포함됩니다. 더 자세한 내용은 [대화 템플릿](./chat_templates.md) 섹션을 참고하세요.
-
-## 2️⃣ 지도 학습 기반 미세 조정
-
-지도 학습 기반 미세 조정(SFT)은 사전 학습된 언어 모델이 특정 작업에 적합하도록 조정하는 데 핵심적인 과정입니다. 이 과정에서는 레이블이 포함된 태스크별 데이터셋을 사용해 모델을 학습시킵니다. SFT의 주요 단계와 모범 사례를 포함한 자세한 가이드는 [지도 학습 기반 미세 조정](./supervised_fine_tuning.md) 섹션을 참고하세요.
-
-## 실습 노트북
-| 파일명 | 설명 | 실습 내용 | 링크 | Colab |
-|-------|-------------|----------|------|-------|
-| Chat Templates | SmolLM2를 활용한 대화 템플릿 사용법과 데이터셋을 ChatML 형식으로 변환하는 과정 학습 | 🐢 `HuggingFaceTB/smoltalk` 데이터셋을 chatml 형식으로 변환해보기 <br> 🐕 `openai/gsm8k` 데이터셋을 chatml 형식으로 변환해보기 | [Notebook](./notebooks/chat_templates_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/1_instruction_tuning/notebooks/chat_templates_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-| Supervised Fine-Tuning | SFTTrainer를 이용해 SmolLM2를 파인튜닝하는 방법 학습 | 🐢 `HuggingFaceTB/smoltalk` 데이터셋 활용해보기 <br>🐕 `bigcode/the-stack-smol` 데이터셋 활용해보기 <br>🦁 실제 사용 사례에 맞는 데이터셋 선택해보기 | [Notebook](./notebooks/sft_finetuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-## 참고
-
-- [Transformers documentation on chat templates](https://huggingface.co/docs/transformers/main/en/chat_templating)
-- [Script for Supervised Fine-Tuning in TRL](https://github.com/huggingface/trl/blob/main/examples/scripts/sft.py)
-- [`SFTTrainer` in TRL](https://huggingface.co/docs/trl/main/en/sft_trainer)
-- [Direct Preference Optimization Paper](https://arxiv.org/abs/2305.18290)
-- [Supervised Fine-Tuning with TRL](https://huggingface.co/docs/trl/main/en/tutorials/supervised_finetuning)
-- [How to fine-tune Google Gemma with ChatML and Hugging Face TRL](https://www.philschmid.de/fine-tune-google-gemma)
-- [Fine-tuning LLM to Generate Persian Product Catalogs in JSON Format](https://huggingface.co/learn/cookbook/en/fine_tuning_llm_to_generate_persian_product_catalogs_in_json_format)
\ No newline at end of file
diff --git a/ko/1_instruction_tuning/chat_templates.md b/ko/1_instruction_tuning/chat_templates.md
deleted file mode 100644
index 444478f2..00000000
--- a/ko/1_instruction_tuning/chat_templates.md
+++ /dev/null
@@ -1,114 +0,0 @@
-# 대화 템플릿(Chat Templates)
-
-대화 템플릿은 언어 모델과 사용자 간 상호작용을 구조화하는 데 필수적입니다. 이 템플릿은 대화에 일관된 형식을 제공하여, 모델이 각 메시지의 맥락과 역할을 이해하고 적절한 응답 패턴을 유지할 수 있도록 합니다.
-
-## 기본 모델 vs 지시 모델
-
-기본 모델은 다음 토큰을 예측하기 위해 대량의 원시 텍스트 데이터로 학습되는 반면, 지시 모델은 특정 지시를 따르고 대화를 나눌 수 있도록 미세 조정된 모델입니다. 예를 들어, `SmolLM2-135M`은 기본 모델이고, `SmolLM2-135M-Instruct`는 이를 지시 조정한 변형 모델입니다.
-
-기본 모델이 지시 모델처럼 동작하도록 만들기 위해서는 모델이 이해할 수 있는 방식으로 프롬프트를 일관되게 만들어야 합니다. 이때 대화 템플릿이 유용합니다. ChatML은 이러한 템플릿 형식 중 하나로 대화를 구조화하고 명확한 역할 지시자(시스템, 사용자, 어시스턴트)를 제공합니다.
-
-기본 모델은 서로 다른 대화 템플릿으로 미세 조정될 수 있으므로, 지시 모델을 사용할 때는 반드시 해당 모델에 적합한 대화 템플릿을 사용하는 것이 중요합니다.
-
-## 대화 템플릿 이해하기
-
-대화 템플릿의 핵심은 언어 모델과 소통할 때 대화가 어떤 형식으로 이루어져야 하는지 정의하는 것입니다. 템플릿에는 시스템 수준의 지침, 사용자 메시지, 어시스턴트의 응답이 포함되어 있으며, 모델이 이해할 수 있는 구조화된 형식을 제공합니다. 이러한 구조는 상호작용의 일관성을 유지하고, 모델이 다양한 유형의 입력에 적절히 응답하도록 돕습니다. 아래는 채팅 템플릿의 예시입니다:
-
-```sh
-<|im_start|>user
-안녕하세요!<|im_end|>
-<|im_start|>assistant
-만나서 반갑습니다!<|im_end|>
-<|im_start|>user
-질문을 해도 될까요?<|im_end|>
-<|im_start|>assistant
-```
-
-`transformers` 라이브러리는 모델의 토크나이저와 관련하여 대화 템플릿을 자동으로 처리해줍니다. 대화 템플릿이 transformers에서 어떻게 구성되는지 자세히 알아보려면 [여기](https://huggingface.co/docs/transformers/en/chat_templating#how-do-i-use-chat-templates)를 참고하세요. 우리가 메시지를 올바른 형식으로 구조화하기만 하면 나머지는 토크나이저가 처리합니다. 아래는 기본적인 대화 예시입니다:
-```python
-messages = [
-    {"role": "system", "content": "You are a helpful assistant focused on technical topics."},
-    {"role": "user", "content": "Can you explain what a chat template is?"},
-    {"role": "assistant", "content": "A chat template structures conversations between users and AI models..."}
-]
-```
-
-위 예시를 자세히 보면서 대화 템플릿 형식에 어떻게 매핑되는지 살펴봅시다.
-
-## 시스템 메시지
-
-시스템 메시지는 모델의 기본 동작 방식을 설정합니다. 이는 이후 모든 상호작용에 영향을 미치는 지속적인 지침이 됩니다.
-
-```python
-system_message = {
-    "role": "system",
-    "content": "You are a professional customer service agent. Always be polite, clear, and helpful."
-}
-```
-
-## 대화
-
-대화 템플릿은 대화 기록을 통해 맥락을 유지하며, 사용자와 어시스턴트 간의 이전 대화를 저장합니다. 이를 통해 더욱 일관된 멀티 턴 대화가 가능해집니다:
-
-```python
-conversation = [
-    {"role": "user", "content": "I need help with my order"},
-    {"role": "assistant", "content": "I'd be happy to help. Could you provide your order number?"},
-    {"role": "user", "content": "It's ORDER-123"},
-]
-```
-
-## Transformers 기반 구현
-
-transformers 라이브러리는 대화 템플릿을 위한 내장 기능을 지원합니다.사용 방법은 다음과 같습니다:
-
-```python
-from transformers import AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-135M-Instruct")
-
-messages = [
-    {"role": "system", "content": "You are a helpful coding assistant."},
-    {"role": "user", "content": "Write a Python function to sort a list"},
-]
-
-# 대화 템플릿 적용
-formatted_chat = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-```
-
-## 사용자 정의 형식
-
-메시지 유형별로 원하는 형식을 설정할 수 있습니다. 예를 들어, 역할에 따라 특수 토큰을 추가하거나 형식을 지정할 수 있습니다:
-
-```python
-template = """
-<|system|>{system_message}
-<|user|>{user_message}
-<|assistant|>{assistant_message}
-""".lstrip()
-```
-
-## 멀티 턴 지원
-
-템플릿은 문맥을 유지하면서 복잡한 멀티 턴 대화도 처리할 수 있습니다:
-
-```python
-messages = [
-    {"role": "system", "content": "You are a math tutor."},
-    {"role": "user", "content": "What is calculus?"},
-    {"role": "assistant", "content": "Calculus is a branch of mathematics..."},
-    {"role": "user", "content": "Can you give me an example?"},
-]
-```
-
-⏭️ [다음: Supervised Fine-Tuning](./supervised_fine_tuning.md)
-
-## 참고
-
-- [Hugging Face Chat Templating Guide](https://huggingface.co/docs/transformers/main/en/chat_templating)
-- [Transformers Documentation](https://huggingface.co/docs/transformers)
-- [Chat Templates Examples Repository](https://github.com/chujiezheng/chat_templates) 
diff --git a/ko/1_instruction_tuning/notebooks/.env.example b/ko/1_instruction_tuning/notebooks/.env.example
deleted file mode 100644
index f0e16a46..00000000
--- a/ko/1_instruction_tuning/notebooks/.env.example
+++ /dev/null
@@ -1,2 +0,0 @@
-# Duplicate this file to .env and fill in secret values
-HF_TOKEN=
diff --git a/ko/1_instruction_tuning/notebooks/chat_templates_example.ipynb b/ko/1_instruction_tuning/notebooks/chat_templates_example.ipynb
deleted file mode 100644
index 7c4106d0..00000000
--- a/ko/1_instruction_tuning/notebooks/chat_templates_example.ipynb
+++ /dev/null
@@ -1,5725 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "vZAvFVIAtFlq"
-   },
-   "source": [
-    "# SmolLM2와 함께 대화 템플릿 탐색하기\n",
-    "\n",
-    "이 노트북은 `SmolLM2` 모델과 함께 대화 템플릿을 사용하는 방법을 설명합니다. 대화 템플릿은 사용자와 AI 모델 간의 상호작용을 구조화하여, 일관되고 맥락에 맞는 응답을 보장합니다."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "K-lZu8JvtwUN",
-    "outputId": "c3871418-15bc-4265-ae8d-6d6036036d0e"
-   },
-   "outputs": [],
-   "source": [
-    "# Google Colab에서 requirements 설치\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "# Hugging Face 인증\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()\n",
-    "\n",
-    "# 허브 토큰을 HF_TOKEN 환경 변수로 설정해두면 편하게 사용할 수 있습니다."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "tnHzBR7vtFlr"
-   },
-   "outputs": [],
-   "source": [
-    "# 주요 라이브러리 불러오기\n",
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "from trl import setup_chat_format\n",
-    "import torch"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "XTVOqbuetFlr"
-   },
-   "source": [
-    "## SmolLM2 대화 템플릿\n",
-    "\n",
-    "`SmolLM2` 모델에서 대화 템플릿을 사용하는 법을 알아봅시다. 간단한 대화를 정의하고 대화 템플릿을 적용해보겠습니다."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 397,
-     "referenced_widgets": [
-      "b922b90106414644bc0e933f28dea1bf",
-      "e0a40f83ae2e4ab29376a1d48b53aa6e",
-      "547eeb64ffd34e509c0b8b8ba6d657e2",
-      "45675fb5f5c94f8cae575582f7ae41a7",
-      "016d5e929f1240cea067372b2191d107",
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-      "17023310de9b4c3ebd8cc03758d59ef9",
-      "f3e23f781bce4429954d76bfea97aff4",
-      "530fc4c2bf1244628af7dea3e4b35cdf",
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-      "76d306c21214412ab44e542d82e547aa",
-      "b9e41ef9e9c54fa7b71bc333604af74e"
-     ]
-    },
-    "id": "Nrxh0oX6tFls",
-    "outputId": "953e1527-8168-4346-9338-6e188ca31a1a"
-   },
-   "outputs": [],
-   "source": [
-    "# 동적으로 디바이스 설정\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=model_name\n",
-    ").to(device)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
-    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "zkJwILrbtFls"
-   },
-   "outputs": [],
-   "source": [
-    "# SmolLM2를 위한 메시지 정의\n",
-    "messages = [\n",
-    "    {\"role\": \"user\", \"content\": \"Hello, how are you?\"},\n",
-    "    {\n",
-    "        \"role\": \"assistant\",\n",
-    "        \"content\": \"I'm doing well, thank you! How can I assist you today?\",\n",
-    "    },\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Ve4dgtjstFls"
-   },
-   "source": [
-    "# 토큰화 없이 대화 템플릿 적용하기\n",
-    "\n",
-    "토크나이저는 사용자와 어시스턴트의 역할을 설명하기 위해 특수 토큰이 포함된 문자열로 대화를 표현합니다."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "pbAg-5x-tFls",
-    "outputId": "5f9482db-1fcf-4c13-ccaa-ef3f6eff7f76"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Conversation with template: <|im_start|>user\n",
-      "Hello, how are you?<|im_end|>\n",
-      "<|im_start|>assistant\n",
-      "I'm doing well, thank you! How can I assist you today?<|im_end|>\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "input_text = tokenizer.apply_chat_template(messages, tokenize=False)\n",
-    "\n",
-    "print(\"Conversation with template:\", input_text)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "sfvdglOqtFls"
-   },
-   "source": [
-    "# 대화 디코딩\n",
-    "\n",
-    "대화 내용은 위와 같지만 추가적인 어시스턴트 메시지가 표시됩니다."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "mXUVdPeytFls",
-    "outputId": "80870e53-7bc1-426e-ac33-ba6748e030fc"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Conversation decoded: <|im_start|>user\n",
-      "Hello, how are you?<|im_end|>\n",
-      "<|im_start|>assistant\n",
-      "I'm doing well, thank you! How can I assist you today?<|im_end|>\n",
-      "<|im_start|>assistant\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "input_text = tokenizer.apply_chat_template(\n",
-    "    messages, tokenize=True, add_generation_prompt=True\n",
-    ")\n",
-    "\n",
-    "print(\"Conversation decoded:\", tokenizer.decode(token_ids=input_text))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "UcZQpspEtFlt"
-   },
-   "source": [
-    "# 대화 토크나이징\n",
-    "\n",
-    "물론, 토크나이저는 대화와 특수 토큰을 모델의 단어 사전(vocabulary)과 관련된 아이디로 토큰화 하기도 합니다.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "jc2PLxAMtFlt",
-    "outputId": "d2098780-b3f4-41ec-a1f3-b6da2b593c62"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Conversation tokenized: [1, 4093, 198, 19556, 28, 638, 359, 346, 47, 2, 198, 1, 520, 9531, 198, 57, 5248, 2567, 876, 28, 9984, 346, 17, 1073, 416, 339, 4237, 346, 1834, 47, 2, 198, 1, 520, 9531, 198]\n"
-     ]
-    }
-   ],
-   "source": [
-    "input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)\n",
-    "\n",
-    "print(\"Conversation tokenized:\", input_text)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "m3eNp9a0tFlt"
-   },
-   "source": [
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>연습: SFT를 위한 데이터 가공하기</h2>\n",
-    "    <p>Hugging Face 허브에서 데이터셋을 가져온 후 SFT를 적용할 수 있게 가공해보세요.</p>\n",
-    "    <p><b>난이도</b></p>\n",
-    "    <p>🐢 `HuggingFaceTB/smoltalk` 데이터셋을 chatml 형식으로 변환하기</p>\n",
-    "    <p>🐕 `openai/gsm8k` 데이터셋을 chatml 형식으로 변환하기</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 381
-    },
-    "id": "qbkXV2_ItFlt",
-    "outputId": "06deadc3-2c63-4660-d2bd-05096ef07c9f"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<iframe\n",
-       "  src=\"https://huggingface.co/datasets/HuggingFaceTB/smoltalk/embed/viewer/all/train?row=0\"\n",
-       "  frameborder=\"0\"\n",
-       "  width=\"100%\"\n",
-       "  height=\"360px\"\n",
-       "></iframe>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from IPython.core.display import display, HTML\n",
-    "\n",
-    "display(\n",
-    "    HTML(\n",
-    "        \"\"\"<iframe\n",
-    "  src=\"https://huggingface.co/datasets/HuggingFaceTB/smoltalk/embed/viewer/all/train?row=0\"\n",
-    "  frameborder=\"0\"\n",
-    "  width=\"100%\"\n",
-    "  height=\"360px\"\n",
-    "></iframe>\n",
-    "\"\"\"\n",
-    "    )\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 241,
-     "referenced_widgets": [
-      "c2d74a42fb574b8892d0a288fd92f0a6",
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-      "951f60cddcb84dfdbbdf2058369f0541",
-      "646484cf7a36444daebe1dfe4a0e4150",
-      "e2f0c39ce1c046e8acb150dfbfaf5aa8",
-      "7eb12d70d2b542a7b651c7680f590279",
-      "ea1f9cb22abf4e7d9f6e76fc86c03387",
-      "00c9f5ca71b84df4b26acee72c97fefb",
-      "505f96bc0c7843bcb1498ba1c1ba5f06",
-      "635cc2881a1e4b8788bb26c356740e04",
-      "a6ee323c13904525a99c6f092ba96e18",
-      "67fffe7d6f8c4963972b408529e05532",
-      "0055b6b628934affaf88bc58a1572bb6",
-      "aafbbb9fc5164fa3a88193bfd33d2f79",
-      "606e39d53ed64967a60337418c71c595",
-      "26b15fa18b1b4963a1ba76a76675e7ee",
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-      "d0ea49d1d90f4d34bf2ae70efa96946e",
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-      "269920491c134501873e0110367bc984",
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-      "3bc8f6339f4e4a3b961d810255c5573e",
-      "4780ad263ec04b1a97525d985e102049",
-      "488feef55878426bbf1c753c6d58735b",
-      "560ba45d70ca431dadeb327d234c330a",
-      "04d0a6f74af346f7bc696951949063c8",
-      "2a18ce941b0f4cef8307988ef898b47f",
-      "194e3fda3635466b998f96e3dc22746a",
-      "e2ab3cb38b5a41f68d18ed5f0e6ae22c",
-      "f0b271bcac6c43a9aaddac54259bb514",
-      "0dc93d50a283472f9ca64fd0a4c6ff15",
-      "dd1a50d4497144388a1809b78bb38f58",
-      "6b72a856e5bd4812a5e0dd0c3bfb8455",
-      "4e21a567d1f6461985727823b37166e1",
-      "ec1efb7598fd496bb170673ae1b8a1df",
-      "84f393468aa74baa903243d238b2d387",
-      "a54ce365be104d27aaa15cf8c63b5ebe",
-      "1791220377d141ac9b307246177d0712",
-      "fa330d4f0fb241aebd065f6ef4a6892c",
-      "cfa1cc6eed8a4f7791a7959308456b6b",
-      "b50c9c4433854cf7a6b2593e946b7faa",
-      "7557cd24ba9b4aa3955866d59db94519",
-      "cc608dfb880c49d4bc5acf2d691b8ec6",
-      "cb838c5bed994a9a8e6fcf5c98b76d17",
-      "76bbe8c2beba4c0594085d32a68d2ee7",
-      "c9836c952b07472880649b82e2347e8d",
-      "383db57f997140d482b82b123080837a",
-      "182abc7ec4d944d9bb2ec1281c98b4c8",
-      "6934c6d1cbac44dbb08f3fffe3056edb",
-      "05fa0f6eb78b4c56b219b0e57521bd2e",
-      "012aa94e3cf24e32833c6bbca23c52f7",
-      "76c1a1cdc9054bbe90d0d3b662cf0ed1",
-      "e453f1672772400a851735ba64f42c8b",
-      "d1358f6b16644cb3a2328ca639a4a77a",
-      "c19f60d4028045399c62004027eaafd9",
-      "8055588a1fa940239c801ef66f3ecf3b",
-      "7468a9bc8bda44e5b44574c64fdc6803",
-      "a13a8f8b702e44ed88c7d358a0a8b4b4",
-      "13367fbb763747fa8de94cde40ffae32",
-      "b1fcf477db664ccdade4096fb79de327",
-      "9d1c06ac6b774d82adca58773f389161",
-      "31910159cf30463b8246ec47ffd8ab5b",
-      "72220420f9d340eabec13a01caebc92c",
-      "55b14c03a41c495aacf8ac2d0f96ba0b"
-     ]
-    },
-    "id": "4p3atw4_tFlu",
-    "outputId": "62ee9812-3819-4a9c-9e24-5687368ffcd8"
-   },
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "ds = load_dataset(\"HuggingFaceTB/smoltalk\", \"everyday-conversations\")\n",
-    "\n",
-    "\n",
-    "def process_dataset(sample):\n",
-    "    # TODO: 🐢 예시를 대화 형식으로 변환하기\n",
-    "    # 대화 템플릿을 적용하기 위해 토크나이저 메서드를 사용하세요.\n",
-    "    return sample\n",
-    "\n",
-    "\n",
-    "ds = ds.map(process_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 381
-    },
-    "id": "81fQeazltFlu",
-    "outputId": "36cf7148-9881-4f13-d0ce-76c82c4ab219"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<iframe\n",
-       "  src=\"https://huggingface.co/datasets/openai/gsm8k/embed/viewer/main/train\"\n",
-       "  frameborder=\"0\"\n",
-       "  width=\"100%\"\n",
-       "  height=\"360px\"\n",
-       "></iframe>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "display(\n",
-    "    HTML(\n",
-    "        \"\"\"<iframe\n",
-    "  src=\"https://huggingface.co/datasets/openai/gsm8k/embed/viewer/main/train\"\n",
-    "  frameborder=\"0\"\n",
-    "  width=\"100%\"\n",
-    "  height=\"360px\"\n",
-    "></iframe>\n",
-    "\"\"\"\n",
-    "    )\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true,
-    "id": "bWUSv7NMtFlu"
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-    "ds = load_dataset(\"openai/gsm8k\", \"main\")\n",
-    "\n",
-    "\n",
-    "def process_dataset(sample):\n",
-    "    # TODO: 🐕 예시를 대화 형식으로 변환하기\n",
-    "\n",
-    "    # 1. role과 content를 이용해 메시지 형식을 만들어보세요.\n",
-    "\n",
-    "    # 2. 토크나이저 메서드를 활용해 예시에 대화 템플릿을 적용해보세요.\n",
-    "\n",
-    "    return sample\n",
-    "\n",
-    "\n",
-    "ds = ds.map(process_dataset)"
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-    "## 결론\n",
-    "\n",
-    "이 노트북에서는 `SmolLM2`와 같은 다양한 모델에 대화 템플릿을 적용하는 방법을 보여주었습니다. 대화 템플릿을 통해 사용자와 AI 모델 사이 상호작용을 구조화함으로써 모델이 일관되고 맥락에 맞는 응답을 제공하도록 할 수 있습니다.\n",
-    "\n",
-    "실습을 통해 데이터셋을 ChatML 형식으로 변환해보았습니다. TRL이 이를 자동으로 처리해주지만, 내부 작동 방식을 이해해두는 것도 유용합니다."
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diff --git a/ko/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb b/ko/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb
deleted file mode 100644
index 5f22e58c..00000000
--- a/ko/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb
+++ /dev/null
@@ -1,271 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# SFTTrainer를 활용한 지도 학습 기반 미세 조정\n",
-    "\n",
-    "이 노트북은 `trl` 라이브러리에서 제공하는 `SFTTrainer`를 이용해 `HuggingFaceTB/SmolLM2-135M` 모델을 미세 조정하는 법을 설명합니다. 노트북 셀을 실행하면 모델이 미세 조정됩니다. 다른 데이터셋을 사용해 난이도를 조절할 수 있습니다.\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>연습: SFTTrainer를 활용한 SmolLM2 미세 조정</h2>\n",
-    "    <p>Hugging Face 허브에서 가져온 데이터셋으로 모델은 미세 조정해보세요. </p> \n",
-    "    <p><b>난이도</b></p>\n",
-    "    <p>🐢 `HuggingFaceTB/smoltalk` 데이터셋 사용해보기</p>\n",
-    "    <p>🐕 `bigcode/the-stack-smol` 데이터셋의 하위 집합인 `data/python`을 활용해 코드 생성 모델 미세 조정해보기</p>\n",
-    "    <p>🦁 실제 사용 사례와 관련된 관심 있는 데이터셋 사용해보기</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Google Colab에서 requirements 설치\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "# Hugging Face 인증\n",
-    "\n",
-    "from huggingface_hub import login\n",
-    "login()\n",
-    "\n",
-    "# 허브 토큰을 HF_TOKEN 환경 변수로 설정해두면 편하게 사용할 수 있습니다."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 주요 라이브러리 불러오기\n",
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "from datasets import load_dataset\n",
-    "from trl import SFTConfig, SFTTrainer, setup_chat_format\n",
-    "import torch\n",
-    "\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "# 모델과 토크나이저 불러오기\n",
-    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=model_name\n",
-    ").to(device)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
-    "\n",
-    "# 대화 형식 설정\n",
-    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n",
-    "\n",
-    "# 미세 조정 결과를 저장하고 업로드하기 위한 이름 설정\n",
-    "finetune_name = \"SmolLM2-FT-MyDataset\"\n",
-    "finetune_tags = [\"smol-course\", \"module_1\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 기본 모델을 활용한 답변 생성\n",
-    "\n",
-    "여기서는 대화 템플릿이 없는 기본 모델을 사용해보겠습니다."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 학습 전 기본 모델 테스트 진행\n",
-    "prompt = \"Write a haiku about programming\"\n",
-    "\n",
-    "# 템플릿으로 메시지 형식 지정\n",
-    "messages = [{\"role\": \"user\", \"content\": prompt}]\n",
-    "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n",
-    "\n",
-    "# 응답 생성\n",
-    "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
-    "outputs = model.generate(**inputs, max_new_tokens=100)\n",
-    "print(\"Before training:\")\n",
-    "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 데이터셋 준비\n",
-    "\n",
-    "샘플 데이터셋을 불러와서 학습을 위한 형식을 지정합니다. 데이터셋은 입력-출력 쌍으로 구성되어야 하며, 각 입력은 프롬프트이고 출력은 모델에서 예상되는 응답입니다. **TRL은 모델의 대화 템플릿에 맞게 입력 메시지 형식을 맞춥니다.** 메시지는 `role`과 `content` 키를 가진 딕셔너리 리스트로 표현되어야 합니다."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 샘플 데이터셋 불러오기\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "# TODO: path와 name 파라미터를 이용해 데이터셋과 configuration 정의하기\n",
-    "ds = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO: 🦁 데이터셋이 TRL에서 대화 템플릿으로 변환할 수 있는 형식이 아니라면, 해당 데이터를 처리해야 합니다. 자세한 내용은 [모듈](../chat_templates.md)을 참고하세요."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## SFTTrainer 설정\n",
-    "\n",
-    "`SFTTrainer`는 학습 단계 수, 배치 크기, 학습률, 평가 방식과 같이 학습 과정을 제어하는 다양한 파라미터로 구성됩니다. 특정 요구 사항과 연산 자원에 맞춰 파라미터를 조정하세요. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# SFTTrainer를 위한 SFT configuration 설정\n",
-    "sft_config = SFTConfig(\n",
-    "    output_dir=\"./sft_output\",\n",
-    "    max_steps=1000,  # 원하는 학습 시간과 데이터셋 크기에 따라 조정\n",
-    "    per_device_train_batch_size=4,  # GPU 메모리 용량에 따라 설정\n",
-    "    learning_rate=5e-5,  # 미세 조정을 위해 일반적으로 쓰이는 값\n",
-    "    logging_steps=10,  # 학습 지표 로깅 빈도\n",
-    "    save_steps=100,  # 모델 체크포인트 저장 빈도\n",
-    "    evaluation_strategy=\"steps\",  # 주기적인 모델 평가 설정\n",
-    "    eval_steps=50,  # 평가 빈도\n",
-    "    use_mps_device=(\n",
-    "        True if device == \"mps\" else False\n",
-    "    ),  # 혼합 정밀도 학습(mixed precision training)을 위한 MPS 사용\n",
-    "    hub_model_id=finetune_name,  # 모델 이름 설정\n",
-    ")\n",
-    "\n",
-    "# SFTTrainer 초기화\n",
-    "trainer = SFTTrainer(\n",
-    "    model=model,\n",
-    "    args=sft_config,\n",
-    "    train_dataset=ds[\"train\"],\n",
-    "    tokenizer=tokenizer,\n",
-    "    eval_dataset=ds[\"test\"],\n",
-    ")\n",
-    "\n",
-    "# TODO: 🦁 🐕 선택한 데이터셋에 맞게 SFTTrainer 파라미터를 조절하세요. 예를 들어, `bigcode/the-stack-smol` 데이터셋을 사용하는 경우 `content` 열을 선택해야 합니다."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 모델 학습\n",
-    "\n",
-    "트레이너가 구성되었기 때문에 이제 모델을 학습시킬 수 있습니다. 학습 과정은 데이터셋을 반복적으로 처리하며 손실을 계산하고, 이 손실을 최소화하기 위해 모델의 파라미터를 업데이트하는 과정을 포함합니다."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 모델 학습\n",
-    "trainer.train()\n",
-    "\n",
-    "# 모델 저장\n",
-    "trainer.save_model(f\"./{finetune_name}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.push_to_hub(tags=finetune_tags)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>추가 연습: 미세 조정 모델로 응답 생성하기</h2>\n",
-    "    <p>🐕 기본 예제에서와 마찬가지로 미세 조정된 모델을 사용하여 응답을 생성해보세요.</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 동일한 프롬프트로 미세 조정된 모델을 테스트해보세요.\n",
-    "\n",
-    "# 학습 전 기본 모델 테스트 진행\n",
-    "prompt = \"Write a haiku about programming\"\n",
-    "\n",
-    "# 템플릿으로 메시지 형식 지정\n",
-    "messages = [{\"role\": \"user\", \"content\": prompt}]\n",
-    "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n",
-    "\n",
-    "# 응답 생성\n",
-    "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
-    "\n",
-    "# TODO: 기본 예제에서 했던 것처럼 미세 조정된 모델을 사용해 응답을 생성해보세요."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 💐 완료!\n",
-    "\n",
-    "이 노트북은 `SFTTrainer`를 사용하여 `HuggingFaceTB/SmolLM2-135M` 모델을 미세 조정하는 단계별 가이드를 제공합니다. 이 단계를 따라 특정 작업을 보다 효과적으로 수행하도록 모델을 조정할 수 있습니다. 이 과정을 계속 진행하려면 다음 단계를 시도해 보세요:\n",
-    "\n",
-    "- 더 어려운 방법으로 노트북 실습해보기\n",
-    "- 동료의 PR 검토하기\n",
-    "- 이슈 또는 PR을 통해 자료 개선하기"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py310",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.15"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/ko/1_instruction_tuning/supervised_fine_tuning.md b/ko/1_instruction_tuning/supervised_fine_tuning.md
deleted file mode 100644
index c59c64b8..00000000
--- a/ko/1_instruction_tuning/supervised_fine_tuning.md
+++ /dev/null
@@ -1,43 +0,0 @@
-# 지도 학습 기반 미세 조정(Supervised Fine-Tuning)
-
-지도 학습 기반 미세 조정(SFT)은 사전 학습된 언어 모델을 특정 작업이나 도메인에 맞게 조정하는 데 핵심적인 과정입니다. 사전 학습된 모델의 능력은 일반적으로 뛰어나지만, 특정 사용 사례에서 우수한 성능을 발휘하려면 맞춤화가 필요할 때가 많습니다. SFT는 사람이 검증한 예제를 포함하여 신중하게 선별된 데이터셋으로 모델을 추가 학습시켜 이러한 격차를 해소합니다.
-
-## SFT 이해하기
-
-SFT의 핵심은 레이블이 있는 토큰을 통해 사전 학습된 모델이 특정 작업을 수행하도록 학습시키는 것입니다. 이 과정에서 원하는 입력-출력 동작 예제를 모델에 많이 보여줌으로써, 해당 사용 사례에 특화된 패턴을 학습하도록 합니다.
-
-SFT가 효과적인 이유는 사전 학습 과정에서 습득한 기초 지식을 활용하면서도 사용자의 특정 요구에 맞게 모델의 동작을 조정할 수 있기 때문입니다.
-
-## SFT를 언제 사용해야 하나요?
-
-SFT 사용 여부는 주로 모델이 현재 가지고 있는 능력과 특정 요구 사항 간의 차이에 따라 결정됩니다. SFT는 특히 모델 출력에 대한 정교한 제어가 필요하거나 전문적인 도메인에서 작업할 때 매우 유용합니다.
-
-예를 들어, 고객 서비스 애플리케이션을 개발하는 경우 모델이 회사 지침을 일관되게 따르고 기술적인 질문을 표준화된 방식으로 처리하도록 만들고 싶을 것입니다. 마찬가지로 의료나 법률 애플리케이션에서는 정확성과 도메인 특화 용어를 사용하는 것이 중요합니다. SFT는 이러한 경우에서 모델의 응답이 전문적인 표준을 따르고 모델 응답을 도메인 전문 지식에 맞게 조정하는 데 도움을 줄 수 있습니다.
-
-## 미세 조정 과정
-
-지도 학습 기반 미세 조정은 특정 태스크에 맞춘 데이터셋으로 모델 가중치를 학습시키는 과정을 포함합니다.
-
-먼저, 목표 태스크를 대표할 수 있는 데이터셋을 준비하거나 선택해야 합니다. 이 데이터셋은 모델이 마주하게 될 다양한 시나리오를 포괄할 수 있는 예제를 포함하고 있어야 합니다. 데이터 품질이 매우 중요하며 각 예제는 모델이 생성하길 원하는 출력 유형을 명확히 보여줘야 합니다. 이후 Hugging Face의 `transformers` 및 `trl`과 같은 프레임워크를 사용하여 데이터셋을 기반으로 모델을 학습시키는 실제 미세 조정 단계가 이어집니다.
-
-이 과정 전반에서 지속적인 평가가 필수적입니다. 모델이 원하는 동작을 학습하면서도 일반적인 능력을 잃지 않도록 검증 데이터셋을 사용해 성능을 모니터링해야 합니다. 모델을 평가하는 방법에 대해서는 [모듈 4](../4_evaluation)에서 다룰 예정입니다.
-
-## 선호도 조정에서 SFT의 역할
-
-SFT는 언어 모델을 인간의 선호도에 맞추는 데 기본적인 역할을 합니다. 인간 피드백을 통한 강화 학습(Reinforcement Learning from Human Feedback, RLHF) 및 직접 선호도 최적화(Direct Preference Optimization, DPO)와 같은 기술은 SFT를 사용하여 기본적인 수준으로 태스크를 이해하게 한 후 모델의 응답을 원하는 결과에 맞게 조정합니다. 사전 학습된 모델은 일반적인 언어 능력을 가지고 있음에도 불구하고 항상 사람의 선호도와 일치하는 결과를 생성하지 못할 수 있습니다. SFT는 도메인별 데이터와 지침을 도입하여 이러한 격차를 해소함으로써 모델이 인간의 기대에 부합하는 응답을 생성할 수 있도록 합니다.
-
-## TRL을 활용한 SFT
-
-지도 학습 기반 미세 조정에서 중요한 소프트웨어 패키지 중 하나는 Transformer 강화 학습(Transformer Reinforcement Learning, TRL)입니다. TRL은 강화 학습(Reinforcement Learning, RL)을 사용하여 트랜스포머 언어 모델을 학습시키기 위한 도구입니다.
-
-Hugging Face Transformers 라이브러리를 기반으로 구축된 TRL은 사용자가 사전 학습된 언어 모델을 직접 로드할 수 있게 하며, 대부분의 디코더 및 인코더-디코더 아키텍처를 지원합니다. 이 라이브러리는 언어 모델링에 사용되는 주요 RL 프로세스를 지원하고 지도 학습 기반 미세 조정(SFT), 보상 모델링(Reward Modeling, RM), 근접 정책 최적화(Proximal Policy Optimization, PPO), 직접 선호 최적화(Direct Preference Optimization, DPO)와 같은 과정을 용이하게 만듭니다.
-
-이 레포지토리의 여러 모듈에서 TRL을 활용할 예정입니다.
-
-# 다음 단계
-
-다음 튜토리얼을 통해 TRL을 활용한 SFT 실습 경험을 쌓아 보세요: 
-
-⏭️ [Chat Templates 튜토리얼](./notebooks/chat_templates_example.ipynb)
-
-⏭️ [Supervised Fine-Tuning 튜토리얼](./notebooks/supervised_fine_tuning_tutorial.ipynb)
\ No newline at end of file
diff --git a/ko/3_parameter_efficient_finetuning/README.md b/ko/3_parameter_efficient_finetuning/README.md
deleted file mode 100644
index eca5d888..00000000
--- a/ko/3_parameter_efficient_finetuning/README.md
+++ /dev/null
@@ -1,39 +0,0 @@
-# Parameter-Efficient Fine-Tuning, PEFT
-
-언어 모델이 커지면서 전통적인 미세 조정 방식을 적용하는 것이 점점 어려워지고 있습니다. 1.7B 모델조차도 전체 미세 조정을 수행하려면 상당한 GPU 메모리가 필요하며, 모델 사본을 별도로 저장하기 위한 비용이 많이 들고, 모델의 원래 능력을 상실하는 위험이 존재합니다. Parmeter-Efficient Fine-Tuning(PEFT) 방법은 대부분의 모델 파라미터가 고정된 상태에서 모델 파라미터의 일부만 수정하여 전체 미세 조정 과정에서 발생하는 문제를 해결헙니다.
-
-학습 과정에서 모델의 모든 파라미터를 업데이트하는 전통적인 미세 조정 방법을 대형 언어 모델에 적용하는 것은 현실적으로 어렵습니다. PEFT는 원래 모델 크기의 1% 미만에 해당하는 파라미터만 학습시켜 모델을 조정하는 방법입니다. 학습 가능한 파라미터를 크게 줄이는 것은 다음과 같은 이점을 제공합니다:
-
-- 제한된 GPU 메모리를 가진 하드웨어에서도 미세 조정 가능
-- 효율적인 태스크별 적응 모델 저장
-- 데이터가 적은 상황에서도 뛰어난 일반화 성능 제공
-- 더 빠른 학습 및 반복 가능
-
-## 사용 가능한 방법
-
-이 모듈에서는 많이 사용되는 두 가지 PEFT 방법을 다룹니다:
-
-### 1️⃣ LoRA (Low-Rank Adaptation)
-
-LoRA는 효율적인 모델 적응을 위한 멋진 솔루션을 제공하면서 가장 많이 사용되는 PEFT 방법으로 자리 잡았습니다. LoRA는 전체 모델을 수정하는 대신 **학습 가능한 파라미터를 모델의 어텐션 레이어에 주입**합니다. 이 접근법은 전체 미세 조정과 비슷한 성능을 유지하면서 학습 가능한 파라미터를 약 90%까지 줄입니다. [LoRA (Low-Rank Adaptation)](./lora_adapters.md) 섹션에서 LoRA에 대해 자세히 알아보겠습니다.
- 
-### 2️⃣ 프롬프트 튜닝
-
-프롬프트 튜닝은 모델 가중치를 수정하는 대신 **입력에 학습 가능한 토큰을 추가**하여 **더 경량화된** 접근법을 제공합니다. 프롬프트 튜닝은 LoRA만큼 유명하지는 않지만, 모델을 새로운 태스크나 도메인에 빠르게 적용할 때 유용하게 쓰일 수 있는 기술입니다. [프롬프트 튜닝](./prompt_tuning.md) 섹션에서 프롬프트 튜닝에 대해 탐구해볼 예정입니다.
-
-## 실습 노트북
-
-| 파일명 | 설명 | 실습 내용 | 링크 | Colab |
-|-------|-------------|----------|------|-------|
-| LoRA Fine-tuning | LoRA 어댑터를 사용해 모델을 미세 조정하는 방법 학습 | 🐢 LoRA를 사용해 모델 학습해보기<br>🐕 다양한 랭크 값으로 실험해보기<br>🦁 전체 미세 조정과 성능 비교해보기 | [Notebook](./notebooks/finetune_sft_peft.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-| Load LoRA Adapters | LoRA 어댑터를 불러오고 학습시키는 방법 배우기 | 🐢 사전 학습된 어댑터 불러오기<br>🐕 기본 모델과 어댑터 합치기<br>🦁 여러 어댑터 간 전환해보기 | [Notebook](./notebooks/load_lora_adapter_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/load_lora_adapter_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-<!-- | Prompt Tuning | Learn how to implement prompt tuning | 🐢 Train soft prompts<br>🐕 Compare different initialization strategies<br>🦁 Evaluate on multiple tasks | [Notebook](./notebooks/prompt_tuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/prompt_tuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | -->
-
-## 참고
-- [Hugging Face PEFT 문서](https://huggingface.co/docs/peft)
-- [LoRA 논문](https://arxiv.org/abs/2106.09685)
-- [QLoRA 논문](https://arxiv.org/abs/2305.14314)
-- [프롬프트 튜닝 논문](https://arxiv.org/abs/2104.08691)
-- [Hugging Face PEFT 가이드](https://huggingface.co/blog/peft)
-- [How to Fine-Tune LLMs in 2024 with Hugging Face](https://www.philschmid.de/fine-tune-llms-in-2024-with-trl) 
-- [TRL](https://huggingface.co/docs/trl/index)
\ No newline at end of file
diff --git a/ko/3_parameter_efficient_finetuning/images/lora_adapter.png b/ko/3_parameter_efficient_finetuning/images/lora_adapter.png
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diff --git a/ko/3_parameter_efficient_finetuning/lora_adapters.md b/ko/3_parameter_efficient_finetuning/lora_adapters.md
deleted file mode 100644
index 7a40baec..00000000
--- a/ko/3_parameter_efficient_finetuning/lora_adapters.md
+++ /dev/null
@@ -1,124 +0,0 @@
-# LoRA (Low-Rank Adaptation)
-
-LoRA는 널리 쓰이는 PEFT 방법으로 자리 잡았습니다. 어텐션 가중치에 작은 랭크 분해 행렬을 추가하는 방식으로 동작작하며 일반적으로 학습 가능한 파라미터를 약 90% 줄여줍니다.
-
-## LoRA 이해하기
-
-LoRA(Low-Rank Adaptation)는 사전 학습된 모델 가중치를 고정한 상태에서 학습 가능한 랭크 분해 행렬을 모델 레이어에 주입하는 파라미터 효율적인 미세 조정 기법입니다. 미세 조정 과정에서 모든 모델 파라미터를 학습시키는 대신, LoRA는 저랭크 분해를 통해 가중치 업데이트를 더 작은 행렬로 나눠 모델 성능은 유지하면서 학습 가능한 파라미터 수를 크게 줄입니다. 예를 들어, GPT-3 175B에 LoRA를 적용했을 때 전체 미세 조정 대비 학습 가능한 파라미터 수는 10,000배, GPU 메모리 요구 사항은 3배 감소했습니다. [LoRA 논문](https://arxiv.org/pdf/2106.09685)에서 LoRA에 관한 자세한 내용을 확인할 수 있습니다.
-
-LoRA는 일반적으로 트랜스포머 레이어 중 어텐션 가중치에 랭크 분해 행렬 쌍을 추가하는 방식으로 동작합니다. 어댑터 가중치는 추론 과정에서 기본 모델과 병합될 수 있고 추가적인 지연 시간이 발생하지 않습니다. LoRA는 자원 요구 사항을 적절한 수준으로 유지하면서 대형 언어 모델을 특정 태스크나 도메인에 맞게 조정하는 데 특히 유용합니다.
-
-## LoRA 어댑터 불러오기
-
-load_adapter()를 사용하여 사전 학습된 모델에 어댑터를 불러올 수 있으며 가중치가 병합되지 않은 다른 어댑터를 사용해 볼 때 유용합니다. set_adapter() 함수로 활성 어댑터 가중치를 설정합니다. 기본 모델을 반환하려면 unload()를 사용하여 불러온 모든 LoRA 모듈을 내릴 수 있습니다. 이렇게 하면 태스크별 가중치를 쉽게 전환할 수 있습니다.
-
-```python
-from transformers import AutoModelForCausalLM
-from peft import PeftModel
-
-base_model = AutoModelForCausalLM.from_pretrained("<base_model_name>")
-peft_model_id = "<peft_adapter_id>"
-model = PeftModel.from_pretrained(base_model, peft_model_id)
-```
-
-![lora_load_adapter](./images/lora_adapter.png)
-
-## LoRA 어댑터 병합
-
-LoRA로 학습한 후에는 더 쉬운 배포를 위해 어댑터 가중치를 기본 모델에 다시 병합할 수 있습니다. 이를 통해 결합된 가중치를 가진 단일 모델을 생성할 수 있기 때문에 추론 과정에서 별도로 어댑터를 불러올 필요가 없습니다.
-
-병합 과정에서는 메모리 관리와 정밀도에 주의해야 합니다. 기본 모델과 어댑터 가중치를 동시에 불러와야 하므로 GPU/CPU 메모리가 충분해야 합니다. `transformers`의 `device_map="auto"`를 사용하면 메모리를 자동으로 관리할 수 있습니다. 학습 중 사용한 정밀도(예: float16)를 병합 과정에서도 일관되게 유지하고, 병합된 모델을 같은 형식으로 저장하여 배포하세요. 배포 전에 반드시 병합된 모델의 출력 결과와 성능 지표를 어댑터 기반 버전과 비교하여 검증해야 합니다.
-
-어댑터는 서로 다른 태스크나 도메인 간 전환도 간편하게 만듭니다. 기본 모델과 어댑터 가중치를 별도로 불러오면 빠르게 태스크별 가중치를 전환할 수 있습니다.
-
-## 구현 가이드
-
-`notebooks/` 디렉토리에는 다양한 PEFT 방법을 구현하기 위한 실용적인 튜토리얼과 예제가 포함되어 있습니다. `load_lora_adapter_example.ipynb`에서 기본 소개를 살펴본 다음, `lora_finetuning.ipynb`를 통해 LoRA와 SFT를 사용한 모델 미세 조정 과정을 더 자세히 탐구해 보세요.
-
-PEFT 방법을 구현할 때는 LoRA의 랭크를 4~8 정도의 작은 값으로 설정하고 학습 손실을 지속적으로 모니터링하는 것이 좋습니다. 과적합을 방지하기 위해 검증 세트를 활용하고 가능하다면 전체 미세 조정 기준선과 결과를 비교하세요. 다양한 태스크에서 각 방법의 효과는 다를 수 있으므로 실험을 통해 최적의 방법을 찾는 것이 중요합니다.
-
-## OLoRA
-
-[OLoRA](https://arxiv.org/abs/2406.01775)는 LoRA 어댑터 초기화를 위해 QR 분해를 활용합니다. OLoRA는 모델의 기본 가중치를 QR 분해 계수에 따라 변환합니다. 즉, 모델 학습 전에 가중치를 변경합니다. 이 접근 방식은 안정성을 크게 향상시키고 수렴 속도를 빠르게 하여, 궁극적으로 더 우수한 성능을 달성합니다.
-
-## PEFT와 함께 TRL 사용하기
-
-효율적인 미세 조정을 위해 PEFT 방법을 TRL(Transformers Reinforcements Learning)과 결합할 수 있습니다. 이러한 통합은 메모리 요구사항을 줄여주기 때문에 RLHF (Reinforcement Learning from Human Feedback)에 특히 유용합니다.
-
-```python
-from peft import LoraConfig
-from transformers import AutoModelForCausalLM
-
-# PEFT configuration 설정
-lora_config = LoraConfig(
-    r=16,
-    lora_alpha=32,
-    lora_dropout=0.05,
-    bias="none",
-    task_type="CAUSAL_LM"
-)
-
-# 특정 디바이스에서 모델 불러오기
-model = AutoModelForCausalLM.from_pretrained(
-    "your-model-name",
-    load_in_8bit=True,  # 선택 사항: 8비트 정밀도 사용
-    device_map="auto",
-    peft_config=lora_config
-)
-```
-
-위 코드에서 `device_map="auto"`를 사용해 모델을 적절한 디바이스에 자동으로 할당했습니다. `device_map={"": device_index}`를 써서 모델을 특정 디바이스에 직접 할당할 수도 있습니다. 또한, 메모리 사용량을 효율적으로 유지하면서 여러 GPU에 걸쳐 학습을 확장할 수도 있습니다.
-
-## 기본적인 병합 구현
-
-LoRA 어댑터 학습이 끝나면 어댑터 가중치를 기본 모델에 합칠 수 있습니다. 합치는 방법은 다음과 같습니다:
-
-```python
-import torch
-from transformers import AutoModelForCausalLM
-from peft import PeftModel
-
-# 1. 기본 모델 불러오기
-base_model = AutoModelForCausalLM.from_pretrained(
-    "base_model_name",
-    torch_dtype=torch.float16,
-    device_map="auto"
-)
-
-# 2. 어댑터가 있는 PEFT 모델 불러오기
-peft_model = PeftModel.from_pretrained(
-    base_model,
-    "path/to/adapter",
-    torch_dtype=torch.float16
-)
-
-# 3. 어댑터 가중치를 기본 모델에 병합하기
-try:
-    merged_model = peft_model.merge_and_unload()
-except RuntimeError as e:
-    print(f"Merging failed: {e}")
-    # fallback 전략 또는 메모리 최적화 구현
-
-# 4. 병합된 모델 저장
-merged_model.save_pretrained("path/to/save/merged_model")
-```
-
-저장된 모델의 크기가 일치하지 않으면 토크나이저도 함께 저장했는지 확인하세요:
-
-```python
-# 모델과 토크나이저를 모두 저장
-tokenizer = AutoTokenizer.from_pretrained("base_model_name")
-merged_model.save_pretrained("path/to/save/merged_model")
-tokenizer.save_pretrained("path/to/save/merged_model")
-```
-
-## 다음 단계
-
-⏩ [프롬프트 튜닝](prompt_tuning.md) 가이드로 이동해 프롬프트 튜닝으로 미세 조정하는 법을 배워보세요.
-⏩ [LoRA 어댑터 튜토리얼](./notebooks/load_lora_adapter.ipynb)에서 LoRA 어댑터를 불러오는 방법을 배워보세요.
-
-# 참고
-
-- [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/pdf/2106.09685)
-- [Hugging Face PEFT 문서](https://huggingface.co/docs/peft)
-- [Hugging Face blog post on PEFT](https://huggingface.co/blog/peft)
diff --git a/ko/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb b/ko/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
deleted file mode 100644
index 189ead0e..00000000
--- a/ko/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
+++ /dev/null
@@ -1,515 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "z-6LLOPZouLg"
-   },
-   "source": [
-    "# Hugging Face TRL과 LoRA 어댑터를 활용해 LLM을 미세 조정하는 방법\n",
-    "\n",
-    "이 노트북에서는 LoRA(Low-Rank Adaptation) 어댑터를 사용하여 대형 언어 모델을 효율적으로 파인튜닝하는 방법을 설명합니다. LoRA는 다음과 같은 특징을 가지는 파라미터 효율적인 미세 조정 기법입니다:\n",
-    "- 사전 학습 모델 가중치 고정\n",
-    "- 어텐션 레이어에 학습 가능한 저랭크 분해 행렬 추가\n",
-    "- 학습 가능한 파라미터 수가 최대 90%까지 감소\n",
-    "- 메모리를 효율적으로 사용하면서 모델 성능 유지\n",
-    "\n",
-    "이 노트북에서 다루는 내용입니다:\n",
-    "1. 개발 환경 및 LoRA configuration 설정\n",
-    "2. 어댑터 학습을 위한 데이터셋 생성 및 준비\n",
-    "3. `trl`과 `SFTTrainer`를 활용한 LoRA 어댑터 기반 미세 조정\n",
-    "4. 모델 테스트 및 어댑터 병합(선택 사항)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "fXqd9BXgouLi"
-   },
-   "source": [
-    "## 1. 개발 환경 설정\n",
-    "\n",
-    "첫 번째 단계는 trl, transformers, datasets을 포함한 Hugging Face 라이브러리와 Pytorch를 설치하는 것입니다. trl을 들어본 적이 없더라도 걱정하지 마세요. trl은 transformers와 datasets 위에 구축된 새로운 라이브러리로, 오픈 소스 LLM의 미세 조정과 RLHF, 정렬 작업을 쉽게 만들어줍니다.  \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "tKvGVxImouLi"
-   },
-   "outputs": [],
-   "source": [
-    "# Google Colab에서 requirements 설치\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "# Hugging Face 인증\n",
-    "\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()\n",
-    "\n",
-    "# 허브 토큰을 HF_TOKEN 환경 변수로 설정해두면 편하게 사용할 수 있습니다."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "XHUzfwpKouLk"
-   },
-   "source": [
-    "## 2. 데이터셋 불러오기"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "id": "z4p6Bvo7ouLk"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['full_topic', 'messages'],\n",
-       "        num_rows: 2260\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['full_topic', 'messages'],\n",
-       "        num_rows: 119\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 예시 데이터셋 불러오기\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "# TODO: path와 name 파라미터를 이용해 원하는 데이터셋 정의하기\n",
-    "dataset = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")\n",
-    "dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "9TOhJdtsouLk"
-   },
-   "source": [
-    "## 3. LoRA와 함께 `trl`과 `SFTTrainer`를 사용한 LLM 미세 조정\n",
-    "\n",
-    "`trl`에서 제공하는 [SFTTrainer](https://huggingface.co/docs/trl/sft_trainer)는 [PEFT](https://huggingface.co/docs/peft/en/index) 라이브러리를 통해 LoRA 어댑터와의 통합을 제공합니다. 이 설정의 주요 장점은 다음과 같습니다:\n",
-    "\n",
-    "1. **메모리 효율성**: \n",
-    "   - GPU 메모리에 어댑터 파라미터만 저장\n",
-    "   - 기본 모델 가중치는 고정되며 더 낮은 정밀도로 불러오기 가능\n",
-    "   - 서버가 아닌 소비자용 GPU에서 대형 모델 미세 조정 가능\n",
-    "\n",
-    "2. **학습 기능**:\n",
-    "   - 최소한의 설정으로 기본 PEFT/LoRA 통합\n",
-    "   - 더 나은 메모리 효율을 위한 QLoRA(양자화된 LoRA) 지원\n",
-    "\n",
-    "3. **어댑터 관리**:\n",
-    "   - 체크포인트 저장 시 어댑터 가중치도 저장\n",
-    "   - 어댑터를 기본 모델에 병합하는 기능 제공\n",
-    "\n",
-    "이번 예제에서는 성능 저하 없이 메모리 사용량을 더욱 줄이기 위해 4비트 양자화를 결합한 LoRA를 사용하겠습니다. 설정은 몇 가지 단계만 거치면 됩니다:\n",
-    "\n",
-    "1. LoRA configuration 설정(랭크, 알파, 드롭아웃)\n",
-    "2. PEFT configuration을 추가한 SFTTrainer 생성\n",
-    "3. 어댑터 가중치 학습 및 저장\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 주요 라이브러리 불러오기\n",
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "from datasets import load_dataset\n",
-    "from trl import SFTConfig, SFTTrainer, setup_chat_format\n",
-    "import torch\n",
-    "\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "# 모델과 토크나이저 불러오기\n",
-    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
-    "\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=model_name\n",
-    ").to(device)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
-    "\n",
-    "# 대화 형식 설정\n",
-    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n",
-    "\n",
-    "# 미세 조정 결과를 저장하고 업로드하기 위한 이름 설정\n",
-    "finetune_name = \"SmolLM2-FT-MyDataset\"\n",
-    "finetune_tags = [\"smol-course\", \"module_1\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ZbuVArTHouLk"
-   },
-   "source": [
-    "`SFTTrainer`는 `peft`와의 기본 통합을 지원하기 때문에 LoRA와 같은 방법으로 LLM 미세 조정 작업을 매우 쉽고 효율적으로 진행할 수 있습니다. 우리는 `LoraConfig`를 생성하고 트레이너에게 제공하기만 하면 됩니다.\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>연습: 미세 조정을 위한 LoRA 파라미터 정의하기</h2>\n",
-    "    <p>Hugging Face 허브에서 가져온 데이터셋으로 모델을 미세 조정 해보세요.</p> \n",
-    "    <p><b>난이도</b></p>\n",
-    "    <p>🐢 일반적인 파라미터 값을 사용하여 임의로 미세 조정 진행하기</p>\n",
-    "    <p>🐕 파라미터를 조정하고 가중치와 편향 검토해보기</p>\n",
-    "    <p>🦁 파라미터를 조정하고 추론 결과 변화 확인하기</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "blDSs9swouLk"
-   },
-   "outputs": [],
-   "source": [
-    "from peft import LoraConfig\n",
-    "\n",
-    "# TODO: LoRA 파라미터를 구성해보세요.\n",
-    "# r: LoRA 업데이트 행렬을 위한 랭크 차원원 (작을수록 압축률이 높아짐)\n",
-    "rank_dimension = 6\n",
-    "# lora_alpha: LoRA 레이어를 위한 스케일링 계수 (높을수록 모델이 새로운 데이터에 잘 적응함)\n",
-    "lora_alpha = 8\n",
-    "# lora_dropout: LoRA 레이어의 드롭아웃 확률 (과적합 방지)\n",
-    "lora_dropout = 0.05\n",
-    "\n",
-    "peft_config = LoraConfig(\n",
-    "    r=rank_dimension,  # 랭크 차원 - 일반적으로 4-32 사이의 값 사용\n",
-    "    lora_alpha=lora_alpha,  # LoRA 스케일링 계수 - 일반적으로 랭크 차원의 2배\n",
-    "    lora_dropout=lora_dropout,  # LoRA 레이어의의 드롭아웃 확률\n",
-    "    bias=\"none\",  # LoRA 편향 유형 - 해당 편향은 학습 중 업데이트됨됨\n",
-    "    target_modules=\"all-linear\",  # LoRA를 적용할 모듈듈\n",
-    "    task_type=\"CAUSAL_LM\",  # 모델 구조에 맞는 태스크 유형\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "l5NUDPcaouLl"
-   },
-   "source": [
-    "학습을 시작하기 전 사용하고자 하는 하이퍼파라미터(`TrainingArguments`) 값을 설정해야 합니다."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "NqT28VZlouLl"
-   },
-   "outputs": [],
-   "source": [
-    "# 학습을 위한 configuration\n",
-    "# QLoRA 논문에 기반한 하이퍼파라미터 설정\n",
-    "args = SFTConfig(\n",
-    "    # 출력 설정\n",
-    "    output_dir=finetune_name,  # 모델 체크포인트 저장 디렉토리\n",
-    "    # 학습 기간\n",
-    "    num_train_epochs=1,  # 학습 에포크 수\n",
-    "    # 배치 크기 설정\n",
-    "    per_device_train_batch_size=2,  # GPU당 배치 크기\n",
-    "    gradient_accumulation_steps=2,  # 더 큰 배치 크기 효과를 위한 경사 누적\n",
-    "    # 메모리 최적화\n",
-    "    gradient_checkpointing=True,  # 학습 속도는 느려지지만 메모리 절약 가능\n",
-    "    # 옵티마이저 설정\n",
-    "    optim=\"adamw_torch_fused\",  # 효율성을 위해 fused AdamW 사용\n",
-    "    learning_rate=2e-4,  # 학습률 (QLoRA 논문 참고)\n",
-    "    max_grad_norm=0.3,  # 경사 클리핑 임곗값\n",
-    "    # 학습률 스케줄링\n",
-    "    warmup_ratio=0.03,  # 학습률을 0에서 목표 값까지 선형적으로 증가시키는 웜업을 위한 전체 학습 스텝의 비율\n",
-    "    lr_scheduler_type=\"constant\",  # 웜업 후 일정한 학습률 유지\n",
-    "    # 로깅 및 저장\n",
-    "    logging_steps=10,  # 매 N 스텝마다 지표 기록\n",
-    "    save_strategy=\"epoch\",  # 매 에포크마다 체크포인트 저장\n",
-    "    # 정밀도 설정\n",
-    "    bf16=True,  # bfloat16 정밀도 사용\n",
-    "    # 통합 설정\n",
-    "    push_to_hub=False,  # Hugging Face 허브로 내보내지 않음\n",
-    "    report_to=\"none\",  # 외부 로깅 비활성화\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "cGhR7uFBouLl"
-   },
-   "source": [
-    "이제 모델 학습을 위한 `SFTTrainer` 생성에 필요한 요소를 모두 갖췄습니다."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "M00Har2douLl"
-   },
-   "outputs": [],
-   "source": [
-    "max_seq_length = 1512  # 모델과 데이터셋 패킹을 위한 최대 시퀀스 길이\n",
-    "\n",
-    "# LoRA configuration으로 SFTTrainer 생성\n",
-    "trainer = SFTTrainer(\n",
-    "    model=model,\n",
-    "    args=args,\n",
-    "    train_dataset=dataset[\"train\"],\n",
-    "    peft_config=peft_config,  # LoRA configuration\n",
-    "    max_seq_length=max_seq_length,  # 최대 시퀀스 길이\n",
-    "    tokenizer=tokenizer,\n",
-    "    packing=True,  # 효율성을 위해 입력 패킹 활성화\n",
-    "    dataset_kwargs={\n",
-    "        \"add_special_tokens\": False,  # 템플릿에서 추가 토큰 처리\n",
-    "        \"append_concat_token\": False,  # 추가 구분자 없음\n",
-    "    },\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "zQ_kRN24ouLl"
-   },
-   "source": [
-    "`Trainer` 객체의 `train()` 메서드를 호출해 학습을 시작합니다. 학습 루프가 시작되고 3 에포크 동안 모델이 학습됩니다. 지금은 PEFT 방법을 쓰고 있기 때문에 전체 모델을 저장하지 않고 적응된 모델 가중치만 저장합니다."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Tq4nIYqKouLl"
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "300e5dfbb4b54750b77324345c7591f9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/72 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "TrainOutput(global_step=72, training_loss=1.6402628521124523, metrics={'train_runtime': 195.2398, 'train_samples_per_second': 1.485, 'train_steps_per_second': 0.369, 'total_flos': 282267289092096.0, 'train_loss': 1.6402628521124523, 'epoch': 0.993103448275862})"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 학습이 시작되면 허브와 출력 디렉토리에 모델 자동 저장\n",
-    "trainer.train()\n",
-    "\n",
-    "# 모델 저장\n",
-    "trainer.save_model()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "y4HHSYYzouLl"
-   },
-   "source": [
-    "`g5.2xlarge` 인스턴스에서 플래시 어텐션을 적용해 15,000개의 샘플을 3 에포크 동안 학습하는 데 걸린 시간은 4시간 14분 36초였습니다. 해당 인스턴스 비용은 시간당 1.21달러로, 총 비용은 약 `5.3달러`에 불과합니다."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "C309KsXjouLl"
-   },
-   "source": [
-    "### 기존 모델과 LoRA 어댑터 병합\n",
-    "\n",
-    "LoRA를 사용할 때 기본 모델은 고정된 상태로 두고 어댑터 가중치만 학습합니다. 학습 과정에서는 전체 모델이 아닌 2-10MB 크기의 가벼운 어댑터 가중치만 저장합니다. 하지만 배포할 경우 다음과 같은 이유로 어댑터를 기본 모델과 병합하는 것이 더 나을 수 있습니다:\n",
-    "\n",
-    "1. **간소화된 배포**: 기본 모델과 어댑터를 따로 관리하지 않고 단일 모델로 처리 가능\n",
-    "2. **추론 속도**: 어댑터 계산에 따른 추가적인 오버헤드 없음\n",
-    "3. **프레임워크 호환성**: 서빙 프레임워크와의 호환성 향상\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from peft import AutoPeftModelForCausalLM\n",
-    "\n",
-    "\n",
-    "# CPU에서 PEFT 모델 불러오기\n",
-    "model = AutoPeftModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=args.output_dir,\n",
-    "    torch_dtype=torch.float16,\n",
-    "    low_cpu_mem_usage=True,\n",
-    ")\n",
-    "\n",
-    "# LoRA와 기본 모델 병합 후 저장\n",
-    "merged_model = model.merge_and_unload()\n",
-    "merged_model.save_pretrained(\n",
-    "    args.output_dir, safe_serialization=True, max_shard_size=\"2GB\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "-yO6E9quouLl"
-   },
-   "source": [
-    "## 3. 모델 테스트 및 추론 수행\n",
-    "\n",
-    "학습이 끝난 후 모델을 테스트 할 것입니다. 원본 데이터셋에서 다양한 예제를 불러온 뒤, 해당 예제를 활용한 간단한 루프에서 정확도를 평가 지표로 삼아 모델을 평가합니다."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>추가 연습: LoRA 어댑터 불러오기</h2>\n",
-    "    <p>예제 노트북에서 배운 내용을 활용하여 학습된 LoRA 어댑터를 불러오고 추론에 사용하세요.</p> \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "id": "I5B494OdouLl"
-   },
-   "outputs": [],
-   "source": [
-    "# 메모리 확보\n",
-    "del model\n",
-    "del trainer\n",
-    "torch.cuda.empty_cache()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "P1UhohVdouLl"
-   },
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from peft import AutoPeftModelForCausalLM\n",
-    "from transformers import AutoTokenizer, pipeline\n",
-    "\n",
-    "# PEFT 어댑터가 있는 모델 불러오기\n",
-    "tokenizer = AutoTokenizer.from_pretrained(finetune_name)\n",
-    "model = AutoPeftModelForCausalLM.from_pretrained(\n",
-    "    finetune_name, device_map=\"auto\", torch_dtype=torch.float16\n",
-    ")\n",
-    "pipe = pipeline(\n",
-    "    \"text-generation\", model=merged_model, tokenizer=tokenizer, device=device\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "99uFDAuuouLl"
-   },
-   "source": [
-    "몇 가지 프롬프트 예제로 테스트해보고 모델이 어떻게 작동하는지 확인해봅시다."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "id": "-shSmUbvouLl",
-    "outputId": "16d97c61-3b31-4040-c780-3c4de75c3824"
-   },
-   "outputs": [],
-   "source": [
-    "prompts = [\n",
-    "    \"What is the capital of Germany? Explain why thats the case and if it was different in the past?\",\n",
-    "    \"Write a Python function to calculate the factorial of a number.\",\n",
-    "    \"A rectangular garden has a length of 25 feet and a width of 15 feet. If you want to build a fence around the entire garden, how many feet of fencing will you need?\",\n",
-    "    \"What is the difference between a fruit and a vegetable? Give examples of each.\",\n",
-    "]\n",
-    "\n",
-    "\n",
-    "def test_inference(prompt):\n",
-    "    prompt = pipe.tokenizer.apply_chat_template(\n",
-    "        [{\"role\": \"user\", \"content\": prompt}],\n",
-    "        tokenize=False,\n",
-    "        add_generation_prompt=True,\n",
-    "    )\n",
-    "    outputs = pipe(\n",
-    "        prompt,\n",
-    "    )\n",
-    "    return outputs[0][\"generated_text\"][len(prompt) :].strip()\n",
-    "\n",
-    "\n",
-    "for prompt in prompts:\n",
-    "    print(f\"    prompt:\\n{prompt}\")\n",
-    "    print(f\"    response:\\n{test_inference(prompt)}\")\n",
-    "    print(\"-\" * 50)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.10"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/ko/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb b/ko/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
deleted file mode 100644
index d9cb26da..00000000
--- a/ko/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
+++ /dev/null
@@ -1,5061 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "otj46qRbtpnd",
-        "outputId": "0fa81296-44b7-479d-e03b-7ef196cc89e4"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install -q bitsandbytes datasets accelerate loralib\n",
-        "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 446,
-          "referenced_widgets": [
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-            "4454423035964544a7b6799ae7cb8cba",
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-            "27069e667ea24c1896ad413d1fbec939",
-            "903aa7919320406bab7750fbe4dbf8cc",
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-            "768c6d8f7a8a4409a30dcbadd2c2396b",
-            "8e2b70c864bd432fa0a7e426ab81744c",
-            "4f1b47e2d7d64765b4f12dcc16878f1f",
-            "993fcaf75d47402d9f9f7c2b094295f1",
-            "e6787d84d8234d368dc48f8d43bdb2a1",
-            "5ecb188fa26a499ea67954aa90108f6e",
-            "1d3184ff92b7465da9f36eeb5726f021",
-            "74af354462ec4d4a83654c5b8ba7a2f9"
-          ]
-        },
-        "id": "cg3fiQOvmI3Q",
-        "outputId": "92fcd520-ad2c-4488-8f66-664ffc566b89"
-      },
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
-        "import torch\n",
-        "import torch.nn as nn\n",
-        "import bitsandbytes as bnb\n",
-        "from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "S65GcxNGA9kz"
-      },
-      "source": [
-        "## Hub에서 어댑터 불러오기\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 517,
-          "referenced_widgets": [
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-        "outputId": "83ecbf31-3f84-4b8e-d667-10317f7adcec"
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-      "source": [
-        "import torch\n",
-        "import transformers\n",
-        "from datasets import load_dataset\n",
-        "from peft import LoraConfig, get_peft_model\n",
-        "from peft import PeftModel, PeftConfig\n",
-        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-        "\n",
-        "peft_model_id = \"ybelkada/opt-6.7b-lora\"\n",
-        "config = PeftConfig.from_pretrained(peft_model_id)\n",
-        "model = AutoModelForCausalLM.from_pretrained(\n",
-        "    config.base_model_name_or_path,\n",
-        "    return_dict=True,\n",
-        "    load_in_8bit=True,\n",
-        "    device_map=\"auto\",\n",
-        ")\n",
-        "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
-        "\n",
-        "# LoRA 모델 불러오기\n",
-        "model = PeftModel.from_pretrained(model, peft_model_id)"
-      ]
-    },
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-        "batch = tokenizer(\"Machine learning is: \", return_tensors='pt')\n",
-        "\n",
-        "with torch.cuda.amp.autocast():\n",
-        "  output_tokens = model.generate(**batch, max_new_tokens=50)\n",
-        "\n",
-        "print('\\n\\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True))"
-      ]
-    }
-  ],
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diff --git a/ko/3_parameter_efficient_finetuning/prompt_tuning.md b/ko/3_parameter_efficient_finetuning/prompt_tuning.md
deleted file mode 100644
index b160767a..00000000
--- a/ko/3_parameter_efficient_finetuning/prompt_tuning.md
+++ /dev/null
@@ -1,74 +0,0 @@
-# 프롬프트 튜닝
-
-프롬프트 튜닝은 모델 가중치 대신 입력 표현을 변경하는 파라미터 효율적인 접근법입니다. 모델의 모든 파라미터를 업데이트하는 전통적인 미세 조정과 다르게, 프롬프트 튜닝은 기본 모델의 파라미터가 고정된 상태를 유지하면서 소수의 학습 가능한 토큰을 추가하고 최적화합니다. 
-
-## 프롬프트 튜닝 이해하기
-
-프롬프트 튜닝은 모델 미세 조정의 대안으로, 학습 가능한 연속 벡터(소프트 프롬프트)를 입력 텍스트에 추가하는 파라미터 효율적인 방식입니다. 소프트 프롬프트는 이산적인 텍스트 프롬프트와 다르게 모델 파라미터가 고정된 상태에서 역전파를 통해 학습됩니다. 이 방식은 ["The Power of Scale for Parameter-Efficient Prompt Tuning"](https://arxiv.org/abs/2104.08691) (Lester et al., 2021)에서 처음 소개되었으며, 이 논문은 모델 크기가 커질수록 미세 조정과 비교했을 때 프롬프트 튜닝이 경쟁력을 가진다는 것을 보였습니다. 논문에 따르면, 약 100억 개의 파라미터를 가진 모델에서 프롬프트 튜닝의 성능은 태스크당 수백 개의 매개변수 수정만으로 모델 미세 조정의 성능과 일치하는 것으로 나타났습니다.
-
-모델의 임베딩 공간에 존재하는 연속적인 벡터인 소프트 프롬프트는 학습 과정에서 최적화됩니다. 자연어 토큰을 사용하는 전통적인 이산 프롬프트와 달리 소프트 프롬프트는 고유한 의미를 가지고 있지는 않지만 경사 하강법을 통해 파라미터가 고정된 모델에서 원하는 동작을 이끌어내는 방법을 학습합니다. 이 기법은 모델 전체를 복사하지 않고 적은 수(일반적으로 몇백 개의 파라미터)에 해당하는 프롬프트 벡터만을 저장하기 때문에 멀티 태스크 시나리오에 더욱 효과적입니다. 이 접근 방식은 최소한의 메모리 사용량을 유지할 뿐만 아니라, 프롬프트 벡터만 교체하여 모델을 다시 로드하지 않고도 신속하게 작업을 전환할 수 있게 합니다.
-
-## 학습 과정
-
-소프트 프롬프트는 일반적으로 8~32개의 토큰으로 구성되며 무작위 또는 기존 텍스트로 초기화할 수 있습니다. 초기화 방법은 학습 과정에서 중요한 역할을 하며 텍스트 기반 초기화가 무작위 초기화보다 좋은 성능을 보이는 경우가 많습니다.
-
-학습이 진행되는 동안 기본 모델의 파라미터는 고정되어 있고 프롬프트 파라미터만 업데이트 됩니다. 프롬프트 파라미터에 초점을 맞춘 이 접근 방식은 일반적인 학습 목표를 따르지만 학습률과 프롬프트 토큰의 경사 동작에 세심한 주의가 필요합니다.
-
-## PEFT 라이브러리를 활용한 프롬프트 튜닝 구현
-
-PEFT 라이브러리를 이용해 프롬프트 튜닝을 직관적으로 구현할 수 있습니다. 기본적인 예제는 다음과 같습니다:
-
-```python
-from peft import PromptTuningConfig, TaskType, get_peft_model
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-# 기본 모델 불러오기
-model = AutoModelForCausalLM.from_pretrained("your-base-model")
-tokenizer = AutoTokenizer.from_pretrained("your-base-model")
-
-# 프롬프트 튜닝 configuration 설정
-peft_config = PromptTuningConfig(
-    task_type=TaskType.CAUSAL_LM,
-    num_virtual_tokens=8,  # 학습 가능한 토큰 수
-    prompt_tuning_init="TEXT",  # 텍스트 기반 초기화
-    prompt_tuning_init_text="Classify if this text is positive or negative:",
-    tokenizer_name_or_path="your-base-model",
-)
-
-# 프롬프트 튜닝 가능한 모델 생성
-model = get_peft_model(model, peft_config)
-```
-
-## 프롬프트 튜닝과 다른 PEFT 방법 비교
-
-다른 PEFT 접근 방식과 비교했을 때 프롬프트 튜닝은 효율성이 뛰어납니다. LoRA는 파라미터 수와 메모리 사용량이 적지만 태스크 전환을 위해 어댑터를 불러와야 하는 반면 프롬프트 튜닝은 자원을 더 적게 사용하면서 즉각적인 태스크 전환이 가능합니다. 반면에 전체 미세 조정은 상당한 양의 자원을 필요로 하고 서로 다른 태스크를 수행하기 위해 태스크에 맞는 각각의 모델을 필요로 합니다.
-
-| 방법 | 파라미터 수 | 메모리 사용량 | 태스크 전환 |
-|--------|------------|---------|----------------|
-| 프롬프트 튜닝 | 매우 적음 | 메모리 사용량 최소화 | 쉬움 |
-| LoRA | 적음 | 적음 | 모델을 다시 불러와야 함 |
-| 전체 미세 조정 | 많음 | 많음 | 새로운 모델 복사 필요 |
-
-프롬프트 튜닝을 구현할 때는 적은 수(8~16개)의 가상 토큰으로 시작한 후 태스크의 복잡성에 따라 토큰 수를 점차 늘려가세요. 태스크와 관련된 텍스트로 초기화를 수행하는 것이 무작위 초기화보다 일반적으로 더 나은 결과를 가져옵니다. 초기화 전략은 목표 태스크의 복잡성을 반영해야 합니다.
-
-학습 과정은 전체 미세 조정과 약간의 차이가 있습니다. 보통 높은 학습률이 효과적이지만 프롬프트 토큰 경사를 신중하게 모니터링해야 합니다. 다양한 예제를 활용한 주기적인 검증은 서로 다른 시나리오에서 강력한 성능을 보장하는 데 도움이 됩니다.
-
-## 응용
-
-프롬프트 튜닝은 다음과 같은 시나리오에서 특히 뛰어납니다:
-
-1. 멀티 태스크 배포
-2. 자원이 제한적인 환경
-3. 신속한 태스크 적응
-4. 프라이버시에 민감한 애플리케이션
-
-그러나 모델 크기가 작아질수록 전체 미세 조정과 비교했을 때 프롬프트 튜닝의 경쟁력이 떨어집니다. 예를 들어, SmolLM2 크기의 모델은 프롬프트 튜닝보다 전체 미세 조정를 수행하는 것이 좀 더 의미있습니다.
-
-## 다음 단계
-
-⏭️ [LoRA 어댑터 튜토리얼](./notebooks/finetune_sft_peft.ipynb)에서 LoRA 어댑터를 활용해 모델을 미세 조정하는 법을 배워봅시다.
-
-## 참고
-- [Hugging Face PEFT 문서](https://huggingface.co/docs/peft)
-- [프롬프트 튜닝 논문](https://arxiv.org/abs/2104.08691)
-- [Hugging Face Cookbook](https://huggingface.co/learn/cookbook/prompt_tuning_peft)
diff --git a/ko/README.md b/ko/README.md
deleted file mode 100644
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--- a/ko/README.md
+++ /dev/null
@@ -1,93 +0,0 @@
-![smolcourse image](../banner.png)
-
-# 소형 언어 모델 과정
-
-이 과정에서는 특정 사용 사례에 맞게 언어 모델을 정렬하는 법을 다룹니다. 모든 자료는 대부분의 로컬 컴퓨터에서 실행되므로 간편하게 언어 모델 정렬을 시작해볼 수 있습니다. 이 과정을 위해 필요한 최소한의 GPU 요구 사항이나 유료 서비스가 없습니다. [SmolLM2](https://github.com/huggingface/smollm/tree/main) 시리즈 모델을 기반으로 하는 과정이지만, 여기서 배운 기술을 더 큰 모델이나 다른 작은 언어 모델로 옮길 수 있습니다.
-
-<a href="http://hf.co/join/discord">
-<img src="https://img.shields.io/badge/Discord-7289DA?&logo=discord&logoColor=white"/>
-</a>
-
-<div style="background: linear-gradient(to right, #e0f7fa, #e1bee7, orange); padding: 20px; border-radius: 5px; margin-bottom: 20px; color: purple;">
-    <h2>지금 바로 참여하세요!</h2>
-    <p>이 과정은 열려 있으며 다른 사용자와의 상호 검토를 진행할 수 있습니다. 이 과정에 참여하려면 <strong>pull request(PR)</strong>를 열고 검토 받을 수 있도록 결과물을 제출하세요. 다음 단계를 따르면 됩니다:</p>
-    <ol>
-        <li><a href="https://github.com/huggingface/smol-course/fork">여기</a>에서 레포지토리를 fork하세요.</li>
-        <li>자료를 읽고, 바꿔 보고, 실습해보고, 나만의 예제를 추가해보세요.</li>
-        <li>december-2024 브랜치에 PR을 보내세요.</li>
-        <li>검토가 끝나면 december-2024 브랜치에 병합됩니다.</li>
-    </ol>
-    <p>이 과정은 학습에 도움이 될 뿐만 아니라 지속적으로 발전하는 커뮤니티 기반 코스를 형성하는 데에도 기여할 것입니다.</p>
-</div>
-
-[discussion thread](https://github.com/huggingface/smol-course/discussions/2#discussion-7602932)에서 과정에 대해 토론할 수도 있습니다.
-
-## 과정 개요
-
-이 과정은 소형 언어 모델의 초기 학습부터 결과물 배포까지 실습할 수 있는 실용적인 내용을 제공합니다.
-
-| 모듈 | 설명 | 상태 | 공개일 |
-|--------|-------------|---------|--------------|
-| [Instruction Tuning](./1_instruction_tuning) | 지도 학습 기반 미세 조정, 대화 템플릿 작성, 기본적인 지시를 따르게 하는 방법 학습 | ✅ 학습 가능 | 2024. 12. 3 |
-| [Preference Alignment](./2_preference_alignment) | 모델을 인간 선호도에 맞게 정렬하기 위한 DPO와 ORPO 기법 학습 | ✅ 학습 가능 | 2024. 12. 6 |
-| [Parameter-efficient Fine-tuning](./3_parameter_efficient_finetuning) | LoRA, 프롬프트 튜닝을 포함한 효율적인 적응 방법 학습 | ✅ 학습 가능 | 2024. 12. 9 |
-| [Evaluation](./4_evaluation) | 자동 벤치마크 사용법 및 사용자 정의 도메인 평가 수행 방법 학습 | ✅ 학습 가능 | 2024. 12. 13 |
-| [Vision-language Models](./5_vision_language_models) | 비전-언어 태스크를 위한 멀티모달 모델 적용 방법 학습 | [🚧 준비중](https://github.com/huggingface/smol-course/issues/49) | 2024. 12. 16 |
-| [Synthetic Datasets](./6_synthetic_datasets) | 모델 학습을 위한 합성 데이터셋 생성 및 검증 | [🚧 준비중](https://github.com/huggingface/smol-course/issues/83) | 2024. 12. 20 |
-| [Inference](./7_inference) | 모델의 효율적인 추론 방법 학습 | 📝 작성 예정 | 2024. 12. 23 |
-
-## 왜 소형 언어 모델을 사용하나요?
-
-대형 언어 모델은 뛰어난 능력을 보여주지만, 상당한 연산 자원을 필요로 하며 특정 기능에 초점을 맞춘 애플리케이션에 대해서는 대형 언어 모델이 과한 경우도 있습니다. 소형 언어 모델은 도메인 특화 애플리케이션에 있어서 몇 가지 이점을 제공합니다: 
-
-- **효율성**: 대형 언어 모델보다 훨씬 적은 연산 자원으로 학습 및 배포 가능
-- **맞춤화**: 특정 도메인에 대한 미세 조정 및 적응 용이
-- **제어**: 모델 동작 과정을 잘 이해할 수 있고 모델의 동작을 쉽게 제어 가능
-- **비용**: 학습과 추론 과정에서 필요한 비용 감소
-- **프라이버시**: 데이터를 외부 API로 보내지 않고 로컬에서 실행 가능
-- **친환경**: 탄소 발자국을 줄이는 효율적인 자원 사용
-- **쉬운 학술 연구 및 개발**: 최신 LLM을 활용해 물리적 제약 없이 학술 연구를 쉽게 시작할 수 있도록 지원
-
-## 사전 준비 사항
-
-시작하기 전에 아래 사항이 준비되어 있는지 확인하세요:
-- 머신러닝과 자연어처리에 대한 기본적인 이해가 필요합니다.
-- Python, PyTorch 및 `transformers` 라이브러리에 익숙해야 합니다.
-- 사전 학습된 언어 모델과 레이블이 있는 액세스할 수 있어야 합니다.
-
-## 설치
-
-이 과정은 패키지 형태로 관리되기 때문에 패키지 매니저를 이용해 의존성 설치를 쉽게 진행할 수 있습니다. 이를 위해 [uv](https://github.com/astral-sh/uv) 사용을 권장하지만 `pip`나 `pdm`을 사용할 수도 있습니다.
-
-### `uv` 사용
-
-`uv`를 설치한 후, 아래 명령어로 소형 언어 모델 과정을 설치할 수 있습니다:
-
-```bash
-uv venv --python 3.11.0
-uv sync
-```
-
-### `pip` 사용
-
-모든 예제는 동일한 **python 3.11** 환경에서 실행되기 때문에 아래처럼 환경을 생성하고 의존성을 설치해야 합니다:
-
-```bash
-# python -m venv .venv
-# source .venv/bin/activate
-pip install -r requirements.txt
-```
-
-### Google Colab
-
-**Google Colab**에서는 사용하는 하드웨어에 따라 유연하게 의존성을 설치해야 합니다. 아래와 같이 진행하세요:
-
-```bash
-pip install transformers trl datasets huggingface_hub
-```
-
-## 참여
-
-많은 사람이 고가의 장비 없이 LLM을 미세 조정하는 법을 배울 수 있도록 이 자료를 공유해 봅시다!
-
-[![Star History Chart](https://api.star-history.com/svg?repos=huggingface/smol-course&type=Date)](https://star-history.com/#huggingface/smol-course&Date)
diff --git a/pt-br/1_instruction_tuning/README.md b/pt-br/1_instruction_tuning/README.md
deleted file mode 100644
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-# Instruction Tuning (Ajuste de Instrução)
-
-Este módulo o guiará através de modelos de linguagem de ajuste de instrução. O ajuste de instrução envolve a adaptação de modelos pré-treinados a tarefas específicas, treinando-os ainda mais em conjuntos de dados específicos de tarefas. Esse processo ajuda os modelos a melhorar seu desempenho em certas tarefas específicas.
-
-Neste módulo, exploraremos dois tópicos: 1) Modelos de bate-papo e 2) Ajuste fino supervisionado.
-
-## 1️⃣ Modelos de Bate-Papo
-
-Modelos de bate-papo estruturam interações entre usuários e modelos de IA, garantindo respostas consistentes e contextualmente apropriadas. Eles incluem componentes como avisos de sistema e mensagens baseadas em funções. Para informações mais detalhadas, Consulte a seção [Chat Templates (Modelos de Bate-Papo)](./chat_templates.md).
-
-## 2️⃣ Ajuste Fino Supervisionado
-
-Ajuste fino supervisionado (em inglês, SFT - Supervised Fine-Tuning) é um processo crítico para adaptar modelos de linguagem pré-treinados a tarefas específicas. O ajuste envolve treinar o modelo em um conjunto de dados de uma tarefa específica com exemplos rotulados. Para um guia detalhado sobre SFT, incluindo etapas importantes e práticas recomendadas, veja a página [Supervised Fine-Tuning (Ajuste Fino Supervisionado)](./supervised_fine_tuning.md).
-
-## Cadernos de Exercícios
-
-| Título | Descrição | Exercício | Link | Colab |
-|-------|-------------|----------|------|-------|
-| Modelos de Bate-Papo | Aprenda a usar modelos de bate-papo com SmolLM2 and a processar conjunto de dados para o formato chatml | 🐢 Converta o conjunto de dados `HuggingFaceTB/smoltalk` para o formato chatml<br> 🐕 Converta o conjunto de dados `openai/gsm8k` para o formato chatml | [Exercício](./notebooks/chat_templates_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/1_instruction_tuning/notebooks/chat_templates_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-| Ajuste Fino Supervisionado | Aprenda como fazer o ajuste fino no modelo SmolLM2 usando o SFTTrainer | 🐢 Use o conjunto de dados `HuggingFaceTB/smoltalk`<br>🐕 Experimente o conjunto de dados `bigcode/the-stack-smol`<br>🦁 Selecione um conjunto de dados para um caso de uso do mundo real | [Exercício](./notebooks/sft_finetuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-
-## Referências
-
-- [Documentação dos transformadores em modelos de bate-papo](https://huggingface.co/docs/transformers/main/en/chat_templating)
-- [Script para ajuste fino supervisionado em TRL](https://github.com/huggingface/trl/blob/main/examples/scripts/sft.py)
-- [`SFTTrainer` em TRL](https://huggingface.co/docs/trl/main/en/sft_trainer)
-- [Artigo de otimização de preferência direta](https://arxiv.org/abs/2305.18290)
-- [Ajuste fino supervisionado com TRL](https://huggingface.co/docs/trl/main/en/tutorials/supervised_finetuning)
-- [Como ajustar o Google Gemma com ChatML e Hugging Face TRL](https://www.philschmid.de/fine-tune-google-gemma)
-- [Fazendo o ajuste fino em uma LLM para gerar catálogos de produtos persas em formato JSON](https://huggingface.co/learn/cookbook/en/fine_tuning_llm_to_generate_persian_product_catalogs_in_json_format)
diff --git a/pt-br/1_instruction_tuning/chat_templates.md b/pt-br/1_instruction_tuning/chat_templates.md
deleted file mode 100644
index 48480809..00000000
--- a/pt-br/1_instruction_tuning/chat_templates.md
+++ /dev/null
@@ -1,115 +0,0 @@
-# Modelos de bate-papo
-
-Os modelos de bate-papo são essenciais para estruturar interações entre modelos de linguagem e usuários. Eles fornecem um formato consistente para conversas, garantindo que os modelos entendam o contexto e o papel de cada mensagem, enquanto mantêm os padrões de resposta apropriados.
-
-## Modelos Base vs Modelos de Instrução
-
-Um modelo base é treinado em dados de texto bruto para prever o próximo token, enquanto um modelo de instrução é ajustado especificamente para seguir as instruções e se envolver em conversas. Por exemplo, `SmolLM2-135M` é um modelo base, enquanto o `SmolLM2-135M-Instruct` é sua variante ajustada por instrução.
-
-Para fazer um modelo base se comportar como um modelo de instrução, precisamos formatar nossos prompts de uma maneira consistente que o modelo possa entender. É aqui que entram os modelos de bate-papo. ChatML é um desses formatos de modelo que estrutura conversas com indicadores claros de papéis (sistema, usuário, assistente).
-
-É importante notar que o modelo base pode ser ajustado finamente em diferentes modelos de bate-papo, então, quando estamos usando um modelo de instrução, precisamos garantir que estamos usando o modelo de bate-papo correto.
-
-## Entendendo os Modelos de Bate-papo
-
-Na sua essência, os modelos de bate-papo definem como as conversas devem ser formatadas ao se comunicar com um modelo de linguagem. Eles incluem instruções no nível do sistema, mensagens do usuário e respostas assistentes em um formato estruturado que o modelo pode entender. Essa estrutura ajuda a manter a consistência entre as interações e garante que o modelo responda adequadamente a diferentes tipos de input de dados. Abaixo está um exemplo de modelo de bate-papo:
-
-```sh
-<|im_start|>user
-Hi there!<|im_end|>
-<|im_start|>assistant
-Nice to meet you!<|im_end|>
-<|im_start|>user
-Can I ask a question?<|im_end|>
-<|im_start|>assistant
-```
-
-O módulo `transformers` cuidará dos modelos de bate-papo para você em relação ao tokenizador do modelo. Leia mais sobre como os transformadores criam modelos de bate-papo [aqui](https://huggingface.co/docs/transformers/en/chat_templating#how-do-i-use-chat-templates). Tudo o que precisamos fazer é estruturar nossas mensagens da maneira correta e o tokenizador cuidará do resto. Abaixo, você verá um exemplo básico de uma conversa:
-
-```python
-messages = [
-    {"role": "system", "content": "You are a helpful assistant focused on technical topics."},
-    {"role": "user", "content": "Can you explain what a chat template is?"},
-    {"role": "assistant", "content": "A chat template structures conversations between users and AI models..."}
-]
-```
-
-Vamos ir passo-a-passo no exemplo acima e ver como ele mapeia o formato do modelo de bate-papo.
-
-## Mensagens do Sistema
-
-As mensagens do sistema definem a base de como o modelo deve se comportar. Elas agem como instruções persistentes que influenciam todas as interações subsequentes. Por exemplo:
-
-```python
-system_message = {
-    "role": "system",
-    "content": "You are a professional customer service agent. Always be polite, clear, and helpful."
-}
-```
-
-## Conversas
-
-Os modelos de bate-papo mantêm o contexto através do histórico de conversas, armazenando conversas anteriores entre os usuários e o assistente. Isso permite que as conversas de multi-turno sejam mais coerentes:
-
-```python
-conversation = [
-    {"role": "user", "content": "I need help with my order"},
-    {"role": "assistant", "content": "I'd be happy to help. Could you provide your order number?"},
-    {"role": "user", "content": "It's ORDER-123"},
-]
-```
-
-## Implementação com Transformadores
-
-O módulo transformers fornece suporte interno para modelos de bate-papo. Veja como usá-los:
-
-```python
-from transformers import AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-135M-Instruct")
-
-messages = [
-    {"role": "system", "content": "You are a helpful coding assistant."},
-    {"role": "user", "content": "Write a Python function to sort a list"},
-]
-
-# Apply the chat template
-formatted_chat = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-```
-
-## Formatação Personalizada
-
-Você pode personalizar como tipos diferentes de mensagens são formatadas. Por exemplo, adicionando tokens especiais ou formatando para diferentes funções:
-
-```python
-template = """
-<|system|>{system_message}
-<|user|>{user_message}
-<|assistant|>{assistant_message}
-""".lstrip()
-```
-
-## Suporte do Multi-turno
-
-Os modelos podem lidar com conversas multi-turno complexas enquanto mantêm o contexto:
-
-```python
-messages = [
-    {"role": "system", "content": "You are a math tutor."},
-    {"role": "user", "content": "What is calculus?"},
-    {"role": "assistant", "content": "Calculus is a branch of mathematics..."},
-    {"role": "user", "content": "Can you give me an example?"},
-]
-```
-
-⏭️ [Próxima: Supervised Fine-Tuning (Ajuste Fino Supervisionado)](./supervised_fine_tuning.md)
-
-## Resources
-
-- [Guia de modelos de bate-papo - Hugging Face](https://huggingface.co/docs/transformers/main/en/chat_templating)
-- [Documentação do módulo Transformers](https://huggingface.co/docs/transformers)
-- [Repositório de exemplos de modelos de bate-papo](https://github.com/chujiezheng/chat_templates) 
diff --git a/pt-br/1_instruction_tuning/notebooks/.env.example b/pt-br/1_instruction_tuning/notebooks/.env.example
deleted file mode 100644
index f0e16a46..00000000
--- a/pt-br/1_instruction_tuning/notebooks/.env.example
+++ /dev/null
@@ -1,2 +0,0 @@
-# Duplicate this file to .env and fill in secret values
-HF_TOKEN=
diff --git a/pt-br/1_instruction_tuning/notebooks/chat_templates_example.ipynb b/pt-br/1_instruction_tuning/notebooks/chat_templates_example.ipynb
deleted file mode 100644
index 65b56e3e..00000000
--- a/pt-br/1_instruction_tuning/notebooks/chat_templates_example.ipynb
+++ /dev/null
@@ -1,5725 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "vZAvFVIAtFlq"
-   },
-   "source": [
-    "# Explorando Modelos de Bate-papo com SmolLM2\n",
-    "\n",
-    "Este caderno demonstra como usar modelos de bate-papo com o modelo `SmolLM2`. Modelos de bate-papo ajudam a estruturar as interações entre os usuários e os modelos de IA, garantindo respostas consistentes e contextualmente apropriadas."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "K-lZu8JvtwUN",
-    "outputId": "c3871418-15bc-4265-ae8d-6d6036036d0e"
-   },
-   "outputs": [],
-   "source": [
-    "# Instale os requisitos no Google Colab\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "# Authenticate to Hugging Face\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()\n",
-    "\n",
-    "# Por conveniência, você pode criar uma variável de ambiente que contém seu token do hub como HF_TOKEN"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "tnHzBR7vtFlr"
-   },
-   "outputs": [],
-   "source": [
-    "# Importe módulos necessários\n",
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "from trl import setup_chat_format\n",
-    "import torch"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "XTVOqbuetFlr"
-   },
-   "source": [
-    "## Modelo de bate-papo SmolLM2\n",
-    "\n",
-    "Vamos explorar como usar um modelo de bate-papo com o modelo `SmolLM2`. Vamos definir uma conversa simples e aplicar o modelo de bate-papo."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 397,
-     "referenced_widgets": [
-      "b922b90106414644bc0e933f28dea1bf",
-      "e0a40f83ae2e4ab29376a1d48b53aa6e",
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-      "76d306c21214412ab44e542d82e547aa",
-      "b9e41ef9e9c54fa7b71bc333604af74e"
-     ]
-    },
-    "id": "Nrxh0oX6tFls",
-    "outputId": "953e1527-8168-4346-9338-6e188ca31a1a"
-   },
-   "outputs": [],
-   "source": [
-    "# Defina dinamicamente o dispositivo\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=model_name\n",
-    ").to(device)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
-    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "zkJwILrbtFls"
-   },
-   "outputs": [],
-   "source": [
-    "# Defina mensagens para SmolLM2\n",
-    "messages = [\n",
-    "    {\"role\": \"user\", \"content\": \"Hello, how are you?\"},\n",
-    "    {\n",
-    "        \"role\": \"assistant\",\n",
-    "        \"content\": \"I'm doing well, thank you! How can I assist you today?\",\n",
-    "    },\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Ve4dgtjstFls"
-   },
-   "source": [
-    "# Aplique o modelo de bate-papo sem tokenização\n",
-    "\n",
-    "O tokenizador representa a conversa como uma string com tokens especiais para descrever o papel do usuário e do assistente.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "pbAg-5x-tFls",
-    "outputId": "5f9482db-1fcf-4c13-ccaa-ef3f6eff7f76"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Conversation with template: <|im_start|>user\n",
-      "Hello, how are you?<|im_end|>\n",
-      "<|im_start|>assistant\n",
-      "I'm doing well, thank you! How can I assist you today?<|im_end|>\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "input_text = tokenizer.apply_chat_template(messages, tokenize=False)\n",
-    "\n",
-    "print(\"Conversation with template:\", input_text)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "sfvdglOqtFls"
-   },
-   "source": [
-    "# Decodifique a conversação\n",
-    "\n",
-    "Observe que a conversação é representada como acima, mas com uma mensagem adicional do assistente.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "mXUVdPeytFls",
-    "outputId": "80870e53-7bc1-426e-ac33-ba6748e030fc"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Conversation decoded: <|im_start|>user\n",
-      "Hello, how are you?<|im_end|>\n",
-      "<|im_start|>assistant\n",
-      "I'm doing well, thank you! How can I assist you today?<|im_end|>\n",
-      "<|im_start|>assistant\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "input_text = tokenizer.apply_chat_template(\n",
-    "    messages, tokenize=True, add_generation_prompt=True\n",
-    ")\n",
-    "\n",
-    "print(\"Conversation decoded:\", tokenizer.decode(token_ids=input_text))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "UcZQpspEtFlt"
-   },
-   "source": [
-    "# Tokenize a conversação\n",
-    "\n",
-    "Obviamente, o tokenizador também tokeniza a conversa e o token especial como IDs relacionados ao vocabulário do modelo."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "jc2PLxAMtFlt",
-    "outputId": "d2098780-b3f4-41ec-a1f3-b6da2b593c62"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Conversation tokenized: [1, 4093, 198, 19556, 28, 638, 359, 346, 47, 2, 198, 1, 520, 9531, 198, 57, 5248, 2567, 876, 28, 9984, 346, 17, 1073, 416, 339, 4237, 346, 1834, 47, 2, 198, 1, 520, 9531, 198]\n"
-     ]
-    }
-   ],
-   "source": [
-    "input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)\n",
-    "\n",
-    "print(\"Conversation tokenized:\", input_text)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "m3eNp9a0tFlt"
-   },
-   "source": [
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Exercício: Processe um conjunto de dados para SFT</h2>\n",
-    "    <p>Pegue um conjunto de dados do hub do Hugging Face e processe-o para SFT. </p>\n",
-    "    <p><b>Níveis de Dificuldade</b></p>\n",
-    "    <p>🐢 Converta o conjunto de dados `HuggingFaceTB/smoltalk` para o formato chatml.</p>\n",
-    "    <p>🐕 Converta o conjunto de dados `openai/gsm8k` para o formato chatml.</p>\n",
-    "</div>"
-   ]
-  },
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-   "cell_type": "code",
-   "execution_count": null,
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-    "id": "qbkXV2_ItFlt",
-    "outputId": "06deadc3-2c63-4660-d2bd-05096ef07c9f"
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-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<iframe\n",
-       "  src=\"https://huggingface.co/datasets/HuggingFaceTB/smoltalk/embed/viewer/all/train?row=0\"\n",
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-       "  width=\"100%\"\n",
-       "  height=\"360px\"\n",
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-      ]
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-   ],
-   "source": [
-    "from IPython.core.display import display, HTML\n",
-    "\n",
-    "display(\n",
-    "    HTML(\n",
-    "        \"\"\"<iframe\n",
-    "  src=\"https://huggingface.co/datasets/HuggingFaceTB/smoltalk/embed/viewer/all/train?row=0\"\n",
-    "  frameborder=\"0\"\n",
-    "  width=\"100%\"\n",
-    "  height=\"360px\"\n",
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-    "\"\"\"\n",
-    "    )\n",
-    ")"
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-    "id": "4p3atw4_tFlu",
-    "outputId": "62ee9812-3819-4a9c-9e24-5687368ffcd8"
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-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "ds = load_dataset(\"HuggingFaceTB/smoltalk\", \"everyday-conversations\")\n",
-    "\n",
-    "\n",
-    "def process_dataset(sample):\n",
-    "    # TODO: 🐢 Converta a amostra em um formato de bate-papo\n",
-    "    # Use o método do tokenizador para aplicar o modelo de bate-papo\n",
-    "    return sample\n",
-    "\n",
-    "\n",
-    "ds = ds.map(process_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
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-    },
-    "id": "81fQeazltFlu",
-    "outputId": "36cf7148-9881-4f13-d0ce-76c82c4ab219"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<iframe\n",
-       "  src=\"https://huggingface.co/datasets/openai/gsm8k/embed/viewer/main/train\"\n",
-       "  frameborder=\"0\"\n",
-       "  width=\"100%\"\n",
-       "  height=\"360px\"\n",
-       "></iframe>\n"
-      ],
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-    "display(\n",
-    "    HTML(\n",
-    "        \"\"\"<iframe\n",
-    "  src=\"https://huggingface.co/datasets/openai/gsm8k/embed/viewer/main/train\"\n",
-    "  frameborder=\"0\"\n",
-    "  width=\"100%\"\n",
-    "  height=\"360px\"\n",
-    "></iframe>\n",
-    "\"\"\"\n",
-    "    )\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true,
-    "id": "bWUSv7NMtFlu"
-   },
-   "outputs": [],
-   "source": [
-    "ds = load_dataset(\"openai/gsm8k\", \"main\")\n",
-    "\n",
-    "\n",
-    "def process_dataset(sample):\n",
-    "    # TODO: 🐕 Converta a amostra em um formato de bate-papo\n",
-    "\n",
-    "    # 1. Crie um formato de mensagem com o papel e o conteúdo\n",
-    "\n",
-    "    # 2. Aplique o modelo de bate-papo nas amostras usando o método do tokenizador\n",
-    "\n",
-    "    return sample\n",
-    "\n",
-    "\n",
-    "ds = ds.map(process_dataset)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "qlXCuRKotFlu"
-   },
-   "source": [
-    "## Conclusão\n",
-    "\n",
-    "Este caderno demonstrou como aplicar modelos de bate-papo a diferentes modelos, `SmolLM2`. Ao estruturar interações com modelos de bate-papo, podemos garantir que os modelos de IA forneçam respostas consistentes e contextualmente relevantes.\n",
-    "\n",
-    "No exercício, você tentou converter um conjunto de dados para o formato chatml. Felizmente, o TRL fará isso por você, mas é útil entender o que está acontecendo nas entre-linhas de tudo."
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diff --git a/pt-br/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb b/pt-br/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb
deleted file mode 100644
index 7b7be0e4..00000000
--- a/pt-br/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb
+++ /dev/null
@@ -1,273 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Ajuste Fino Supervisionado com SFTTrainer\n",
-    "\n",
-    "Este caderno demonstra como ajustar o modelo `HuggingFaceTB/SmolLM2-135M` usando o `SFTTrainer` do móduli `trl`. As células do caderno são executadas e vão ajustar finamente o modelo. Você pode selecionar sua dificuldade experimentando diferentes conjuntos de dados.\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Exercício: Ajuste fino do SmolLM2 com SFTTrainer</h2>\n",
-    "    <p>Pegue um conjunto de dados do hub do Hugging Face e faça um ajuste fino em um modelo. </p> \n",
-    "    <p><b>Níveis de Dificuldade</b></p>\n",
-    "    <p>🐢 Use o conjunto de dados `HuggingFaceTB/smoltalk`.</p>\n",
-    "    <p>🐕 Experimente o conjunto de dados `bigcode/the-stack-smol` e ajuste um modelo de geração de código no subconjunto específico `data/python`.</p>\n",
-    "    <p>🦁 Selecione um conjunto de dados relacionado a um caso de uso do mundo real que seja do seu interesse.</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instale os requisitos no Google Colab\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "# Autentique ao Hugging Face\n",
-    "\n",
-    "from huggingface_hub import login\n",
-    "login()\n",
-    "\n",
-    "# Por conveniência, você pode criar uma variável de ambiente que contém seu token do hub como HF_TOKEN"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Importe módulos necessários\n",
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "from datasets import load_dataset\n",
-    "from trl import SFTConfig, SFTTrainer, setup_chat_format\n",
-    "import torch\n",
-    "\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "# Carregue o modelo e o tokenizador\n",
-    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=model_name\n",
-    ").to(device)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
-    "\n",
-    "# Configure o formato de bate-papo\n",
-    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n",
-    "\n",
-    "# Defina nosso nome para o ajuste fino a ser salvo e/ou carregado em um local\n",
-    "finetune_name = \"SmolLM2-FT-MyDataset\"\n",
-    "finetune_tags = [\"smol-course\", \"module_1\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Gerar com o modelo base\n",
-    "\n",
-    "Aqui vamos experimentar o modelo base que não tem um modelo de bate-papo. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Vamos testar o modelo base antes do treinamento\n",
-    "prompt = \"Write a haiku about programming\"\n",
-    "\n",
-    "# Formato com o modelo de bate-papo\n",
-    "messages = [{\"role\": \"user\", \"content\": prompt}]\n",
-    "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n",
-    "\n",
-    "# Gerando a resposta\n",
-    "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
-    "outputs = model.generate(**inputs, max_new_tokens=100)\n",
-    "print(\"Before training:\")\n",
-    "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Preparação do Conjunto de Dados\n",
-    "\n",
-    "Carregaremos um conjunto de dados de amostra e o formataremos para treinamento. O conjunto de dados deve ser estruturado com pares de input-output, em que cada input é um prompt e o output é a resposta esperada do modelo.\n",
-    "\n",
-    "**TRL formatará as mensagens de entrada com base nos modelos de bate-papo.** Elas precisam ser representadas como uma lista de dicionários com as chaves: `role` e `content`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Carregue um conjunto de dados de amostra\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "# TODO: Defina seu próprio conjunto de dados e configurações usando os parâmetros do caminho e do nome\n",
-    "ds = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO: 🦁 Se o seu conjunto de dados não estiver em um formato que o TRL possa converter para o modelo de chat, você precisará processá-lo. Consulte o [módulo](../chat_templates.md)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Configuração do SFTTrainer\n",
-    "\n",
-    "O `SFTTrainer` é configurado com vários parâmetros que controlam o processo de treinamento. Eles incluem o número de etapas de treinamento, o tamanho do lote, a taxa de aprendizado e a estratégia de avaliação. Ajuste esses parâmetros com base em seus requisitos específicos e recursos computacionais."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure o SFTTrainer\n",
-    "sft_config = SFTConfig(\n",
-    "    output_dir=\"./sft_output\",\n",
-    "    max_steps=1000,  # Adjust based on dataset size and desired training duration\n",
-    "    per_device_train_batch_size=4,  # Set according to your GPU memory capacity\n",
-    "    learning_rate=5e-5,  # Common starting point for fine-tuning\n",
-    "    logging_steps=10,  # Frequency of logging training metrics\n",
-    "    save_steps=100,  # Frequency of saving model checkpoints\n",
-    "    evaluation_strategy=\"steps\",  # Evaluate the model at regular intervals\n",
-    "    eval_steps=50,  # Frequency of evaluation\n",
-    "    use_mps_device=(\n",
-    "        True if device == \"mps\" else False\n",
-    "    ),  # Use MPS for mixed precision training\n",
-    "    hub_model_id=finetune_name,  # Set a unique name for your model\n",
-    ")\n",
-    "\n",
-    "# Inicie o SFTTrainer\n",
-    "trainer = SFTTrainer(\n",
-    "    model=model,\n",
-    "    args=sft_config,\n",
-    "    train_dataset=ds[\"train\"],\n",
-    "    tokenizer=tokenizer,\n",
-    "    eval_dataset=ds[\"test\"],\n",
-    ")\n",
-    "\n",
-    "# TODO: 🦁 🐕 alinhe os parâmetros do SFTTrainer com o conjunto de dados escolhido. Por exemplo, se estiver usando o conjunto de dados `bigcode/the-stack-smol`, será necessário escolher a coluna `content`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Treinando o modelo\n",
-    "\n",
-    "Com o trainer configurado, podemos prosseguir com o treinamento do modelo. O processo de treinamento envolverá a iteração do conjunto de dados, o cálculo da perda e a atualização dos parâmetros do modelo para minimizar essa perda."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Treine o modelo\n",
-    "trainer.train()\n",
-    "\n",
-    "# Salve o model\n",
-    "trainer.save_model(f\"./{finetune_name}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.push_to_hub(tags=finetune_tags)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Exercício bônus: Gerar com o modelo ajustado</h2>\n",
-    "    <p>🐕 Use o ajuste fino para modelar e gerar uma resposta, assim como no exemplo básico.</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Teste o modelo ajustado no mesmo prompt\n",
-    "\n",
-    "# Vamos testar o modelo básico antes do treinamento\n",
-    "prompt = \"Write a haiku about programming\"\n",
-    "\n",
-    "# Formatar com o modelo\n",
-    "messages = [{\"role\": \"user\", \"content\": prompt}]\n",
-    "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n",
-    "\n",
-    "# Gerar resposta\n",
-    "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
-    "\n",
-    "# TODO: use o ajuste fino para modelar e gerar uma resposta, assim como no exemplo básico."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 💐 Você conseguiu!\n",
-    "\n",
-    "Este caderno fornece um guia passo-a-passo para o ajuste fino do modelo `HuggingFaceTB/SmolLM2-135M` usando o `SFTTrainer`. Seguindo essas etapas, você pode adaptar o modelo para executar tarefas específicas com mais eficiência. Se quiser continuar trabalhando neste curso, aqui estão as etapas que você pode experimentar:\n",
-    "\n",
-    "- Experimente este caderno com uma dificuldade maior\n",
-    "- Revisar o PR de um colega\n",
-    "- Melhorar o material do curso por meio de uma Issue ou PR."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py310",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.15"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pt-br/1_instruction_tuning/supervised_fine_tuning.md b/pt-br/1_instruction_tuning/supervised_fine_tuning.md
deleted file mode 100644
index 827f316d..00000000
--- a/pt-br/1_instruction_tuning/supervised_fine_tuning.md
+++ /dev/null
@@ -1,41 +0,0 @@
-# Ajuste Fino Supervisionado
-
-Ajuste fino supervisionado (em inglês, SFT - Supervised Fine-Tuning) é um processo crítico para adaptar modelos de linguagem pré-treinados a tarefas ou domínios específicos. Embora os modelos pré-treinados tenham recursos gerais impressionantes, eles geralmente precisam ser personalizados para se destacar em casos de usos específicos. O SFT preenche essa lacuna treinando ainda mais o modelo com conjuntos de dados cuidadosamente selecionados com exemplos validados por humanos.
-
-## Entendendo o Ajuste Fino Supervisionado
-
-Na sua essência, o ajuste fino supervisionado é sobre ensinar um modelo pré-treinado a executar tarefas específicas por meio de exemplos de tokens rotulados. O processo envolve mostrar muitos exemplos do comportamento desejado de input-output ao modelo, permitindo que ele aprenda os padrões específicos do seu caso de uso.
-
-O SFT é eficaz porque usa o conhecimento fundamental adquirido durante o pré-treinamento, adaptando o comportamento do modelo para atender às suas necessidades específicas.
-
-## Quando Usar o Ajuste Fino Supervisionado
-
-A decisão de usar o SFT geralmente se resume à lacuna entre os recursos atuais do seu modelo e seus requisitos específicos. O SFT se torna particularmente valioso quando você precisa de controle preciso sobre os outputs do modelo ou ao trabalhar em domínios especializados.
-
-Por exemplo, se você estiver desenvolvendo um aplicativo de atendimento ao cliente, você vai querer que seu modelo siga constantemente as diretrizes da empresa e lide com consultas técnicas de maneira padronizada. Da mesma forma, em aplicações médicas ou legais, a precisão e a adesão à terminologia específica do domínio se tornam cruciais. Nesses casos, o SFT pode ajudar a alinhar as respostas do modelo com padrões profissionais e experiência no campo de trabalho.
-
-## O Processo de Ajuste Fino
-
-O processo de ajuste fino supervisionado envolve o treinamento dos pesos do modelo em um conjunto de dados de tarefa específico. 
-
-Primeiro, você precisará preparar ou selecionar um conjunto de dados que represente sua tarefa. Esse conjunto de dados deve incluir diversos exemplos que cobrem a gama de cenários que seu modelo encontrará. A qualidade desses dados é importante - cada exemplo deve demonstrar o tipo de output que você deseja que seu modelo produza. Em seguida, vem a fase real de ajuste fino, onde você usará estruturas como os módulos do Hugging Face, `transformers` e `trl`, para treinar o modelo no seu conjunto de dados. 
-
-Ao longo do processo, a avaliação contínua é essencial. Você vai querer monitorar o desempenho do modelo em um conjunto de validação para garantir que ele esteja aprendendo os comportamentos desejados sem perder suas capacidades gerais. No [Módulo 4](../4_evaluation), abordaremos como avaliar seu modelo.
-
-## O Papel do SFT no Alinhamento de Preferência
-
-O SFT desempenha um papel fundamental no alinhamento de modelos de linguagem com preferências humanas. Técnicas como o aprendizado de reforço com o feedback humano (RLHF - Reinforcement Learning with Human Feedback) e a otimização de preferência direta (DPO - Direct Preference Optimization) dependem do SFT para formar um nível básico de entendimento da tarefa antes de alinhar ainda mais as respostas do modelo com os resultados desejados. Modelos pré-treinados, apesar de sua proficiência em linguagem geral, nem sempre podem gerar resultados que correspondam às preferências humanas. O SFT preenche essa lacuna introduzindo dados e orientações específicos de domínio, o que melhora a capacidade do modelo de gerar respostas que se alinham mais de perto com as expectativas humanas.
-
-## Ajuste Fino Supervisionado com Aprendizado de Reforço de Transformadores
-
-Um pacote de software importante para ajuste fino supervisionado é o aprendizado de reforço de transformadores (TRL - Transformer Reinforcement Learning). TRL é um kit de ferramentas usado para treinar modelos de linguagem de transformação usando o aprendizado de reforço.
-
-Construído em cima do módulo de transformadores do Hugging Face, o TRL permite que os usuários carreguem diretamente modelos de linguagem pré-treinados e suporta a maioria das arquiteturas decodificadoras e codificador-decodificador. O módulo facilita os principais processos de RL usados ​​na modelagem de linguagem, incluindo ajuste fino supervisionado (SFT), modelagem de recompensa (RM - Reward Modeling), otimização de políticas proximais (PPO - Proximal Policy Optimization) e otimização de preferência direta (DPO). Usaremos o TRL em vários módulos ao longo deste repositório.
-
-# Próximos passos
-
-Experimente os seguintes tutoriais para obter experiência com o SFT usando TRL:
-
-⏭️ [Tutorial de modelos de bate-papo](./notebooks/chat_templates_example.ipynb)
-
-⏭️ [Tutorial de ajuste fino supervisionado](./notebooks/sft_finetuning_example.ipynb)
\ No newline at end of file
diff --git a/pt-br/2_preference_alignment/README.md b/pt-br/2_preference_alignment/README.md
deleted file mode 100644
index e5c9ce3b..00000000
--- a/pt-br/2_preference_alignment/README.md
+++ /dev/null
@@ -1,41 +0,0 @@
-# Preference Alignment (Alinhamento de Preferência)
-
-Este módulo abrange técnicas para alinhar modelos de linguagem com preferências humanas. Enquanto o ajuste fino supervisionado ajuda os modelos a aprender tarefas, o alinhamento de preferência incentiva os resultados a corresponder às expectativas e valores humanos.
-
-## Visão Geral
-
-Métodos de alinhamento típicos envolvem vários estágios:
-1. Ajuste fino supervisionado (SFT) para adaptar os modelos a domínios específicos
-2. Alinhamento de preferência (como RLHF ou DPO) para melhorar a qualidade da resposta
-
-Abordagens alternativas como ORPO combinam ajuste de instrução e alinhamento de preferência em um único processo. Aqui, vamos focar nos algoritmos DPO e ORPO.
-
-Se você quer aprender mais sobre as diferentes técnicas de alinhamento, você pode ler mais sobre isso no [Argilla Blog](https://argilla.io/blog/mantisnlp-rlhf-part-8). 
-
-### 1️⃣ Otimização de Preferência Direta (DPO - Direct Preference Optimization)
-
-Otimização de preferência direta (DPO) simplifica o alinhamento de preferência otimizando diretamente os modelos usando dados de preferência. Essa abordagem elimina a necessidade de usar modelos de recompensa que não fazem parte do sistema e aprendizado de reforço complexo, tornando-o mais estável e eficiente do que o tradicional aprendizado de reforço com o feedback humano (RLHF). Para mais detalhes,você pode ler mais em [Documentação sobre otimização de preferência direta (DPO)](./dpo.md).
-
-
-### 2️⃣ Otimização de Preferências de Razão de Chances (ORPO - Odds Ratio Preference Optimization)
-
-ORPO introduz uma abordagem combinada para ajuste de instrução e alinhamento de preferência em um único processo. Ele modifica o objetivo padrão de modelagem de linguagem combinando a perda de log-verossimilhança negativa com um termo de razão de chances em um nível de token. A abordagem apresenta um processo de treinamento unificado de estágio único, arquitetura sem modelo de referência e eficiência computacional aprimorada. O ORPO apresentou resultados impressionantes em vários benchmarks, demonstrando melhor desempenho no AlpacaEval em comparação com os métodos tradicionais. Para obter mais detalhes, consulte a [Documentação sobre Otimização de Preferências de Razão de Chances (ORPO)](./orpo.md)
-
-## Caderno de Exercícios
-
-| Título | Descrição | Exercício | Link | Colab |
-|-------|-------------|----------|------|-------|
-| Treinamento em DPO | Aprenda a treinar modelos usando a Otimização Direta de Preferência | 🐢 Treine um modelo usando o conjunto de dados Anthropic HH-RLHF<br>🐕 Use seu próprio conjunto de dados de preferências<br>🦁 Experimente diferentes conjuntos de dados de preferências e tamanhos de modelos | [Exercício](./notebooks/dpo_finetuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-| Treinamento em ORPO | Aprenda a treinar modelos usando a otimização de preferências de razão de chances | 🐢 Treine um modelo usando instruções e dados de preferências<br>🐕 Experimente com diferentes pesos de perda<br>🦁 Comparar os resultados de ORPO com DPO | [Exercício](./notebooks/orpo_finetuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-
-
-## Referências
-
-- [Documentação do TRL](https://huggingface.co/docs/trl/index) - Documentação do módulo Transformers Reinforcement Learning (TRL), que implementa várias técnicas de alinhamento, inclusive a DPO.
-- [Artigo sobre DPO](https://arxiv.org/abs/2305.18290) - Documento de pesquisa original que apresenta a Otimização Direta de Preferência como uma alternativa mais simples ao RLHF que otimiza diretamente os modelos de linguagem usando dados de preferência.
-- [Artigo sobre ORPO](https://arxiv.org/abs/2403.07691) - Apresenta a Otimização de preferências de razão de chances, uma nova abordagem que combina o ajuste de instruções e o alinhamento de preferências em um único estágio de treinamento.
-- [Guia Argilla RLHF](https://argilla.io/blog/mantisnlp-rlhf-part-8/) - Um guia que explica diferentes técnicas de alinhamento, incluindo RLHF, DPO e suas implementações práticas.
-- [Postagem de blog sobre DPO](https://huggingface.co/blog/dpo-trl) - Guia prático sobre a implementação de DPO usando a biblioteca TRL com exemplos de código e práticas recomendadas.
-- [Exemplo de script TRL no DPO](https://github.com/huggingface/trl/blob/main/examples/scripts/dpo.py) - Script de exemplo completo que demonstra como implementar o treinamento em DPO usando a biblioteca TRL.
-- [Exemplo de script TRL no ORPO](https://github.com/huggingface/trl/blob/main/examples/scripts/orpo.py) - Implementação de referência do treinamento ORPO usando a biblioteca TRL com opções de configuração detalhadas.
-- [Manual de alinhamento do Hugging Face](https://github.com/huggingface/alignment-handbook) - Guias de recursos e base de código para alinhamento de modelos de linguagem usando várias técnicas, incluindo SFT, DPO e RLHF.
\ No newline at end of file
diff --git a/pt-br/2_preference_alignment/dpo.md b/pt-br/2_preference_alignment/dpo.md
deleted file mode 100644
index c75dd280..00000000
--- a/pt-br/2_preference_alignment/dpo.md
+++ /dev/null
@@ -1,72 +0,0 @@
-# Otimização de Preferência Direta (DPO)
-
-Otimização de Preferência Direta (DPO) oferece uma abordagem simplificada para alinhar modelos de linguagem com preferências humanas. Ao contrário dos métodos RLHF tradicionais que requerem modelos de recompensa separados e aprendizado de reforço complexo, o DPO otimiza diretamente o modelo usando dados de preferência.
-
-## Entendendo sobre o DPO
-
-O DPO reformula o alinhamento da preferência como um problema de classificação nos dados de preferência humana. As abordagens tradicionais de RLHF exigem treinamento de um modelo de recompensa separado e usando algoritmos de aprendizado de reforço complexos como o PPO para alinhar os outputs do modelo. O DPO simplifica esse processo, definindo uma função de perda que otimiza diretamente a política do modelo com base em outputs preferidos versus não preferidos.
-
-Esta abordagem se mostrou altamente eficaz quando colocada em prática, sendo usada para treinar modelos como o Llama. Ao eliminar a necessidade de usar modelos de recompensa que não fazem parte do sistema e aprendizado de reforço complexo, DPO faz o alinhamento de preferência muito mais acessível e estável.
-
-## Como o DPO Funciona
-
-O processo do DPO exige que o ajuste fino supervisionado (SFT) adapte o modelo ao domínio-alvo. Isso cria uma base para a aprendizagem de preferências por meio do treinamento em conjuntos de dados padrão que seguem instruções. O modelo aprende a concluir tarefas básicas enquanto mantém suas capacidades gerais.
-
-Em seguida, vem o aprendizado de preferências, em que o modelo é treinado em pares de outputs - um preferido e outro não preferido. Os pares de preferências ajudam o modelo a entender quais respostas se alinham melhor com os valores e as expectativas humanas.
-
-A principal inovação do DPO está em sua abordagem de otimização direta. Em vez de treinar um modelo de recompensa separado, o DPO usa uma perda de entropia cruzada binária para atualizar diretamente os pesos do modelo com base nos dados de preferência. Esse processo simplificado torna o treinamento mais estável e eficiente e, ao mesmo tempo, obtém resultados comparáveis ou melhores do que a RLHF tradicional.
-
-## Conjuntos de Dados para DPO
-
-Os conjuntos de dados para DPO são normalmente criados anotando pares de respostas como preferidas ou não preferidas. Isso pode ser feito manualmente ou usando técnicas de filtragem automatizadas. Abaixo está um exemplo de estrutura de conjunto de dados de preferência de turno único para DPO:
-
-| Prompt | Escolhido | Rejeitado |
-|--------|-----------|-----------|
-| ...    | ...       | ...       |
-| ...    | ...       | ...       |
-| ...    | ...       | ...       |
-
-A coluna `Prompt` contém o prompt usado para gerar as respostas `Escolhido` e `Rejeitado`. As colunas `Escolhido` e `Rejeitado` contêm as respostas preferidas e não preferidas, respectivamente. Há variações nessa estrutura, por exemplo, incluindo uma coluna de prompt do sistema ou uma coluna `Input` que contém material de referência. Os valores de `Escolhido` e `Rejeitado` podem ser representados como cadeias de caracteres para conversas de turno único ou como listas de conversas. 
-
-Você pode encontrar uma coleção de conjuntos de dados DPO em Hugging Face [aqui](https://huggingface.co/collections/argilla/preference-datasets-for-dpo-656f0ce6a00ad2dc33069478).
-
-## Implementação com TRL
-
-O módulo Transformers Reinforcement Learning (TRL) facilita a implementação do DPO. As classes `DPOConfig` e `DPOTrainer` seguem a mesma API no estilo `transformers`.
-Veja a seguir um exemplo básico de configuração de treinamento de DPO:
-
-```python
-from trl import DPOConfig, DPOTrainer
-
-# Define arguments
-training_args = DPOConfig(
-    ...
-)
-
-# Initialize trainer
-trainer = DPOTrainer(
-    model,
-    train_dataset=dataset,
-    tokenizer=tokenizer,
-    ...
-)
-
-# Train model
-trainer.train()
-```
-
-Abordaremos mais detalhes sobre como usar as classes `DPOConfig` e `DPOTrainer` no [Tutotial sobre DPO](./notebooks/dpo_finetuning_example.ipynb).
-
-## Práticas Recomendadas
-
-A qualidade dos dados é fundamental para a implementação bem sucedida do DPO. O conjunto de dados de preferências deve incluir diversos exemplos que abranjam diferentes aspectos do comportamento desejado. Diretrizes claras de anotação garantem a rotulagem consistente das respostas preferidas e não preferidas. Você pode aprimorar o desempenho do modelo melhorando a qualidade do conjunto de dados de preferências. Por exemplo, filtrando conjuntos de dados maiores para incluir apenas exemplos de alta qualidade ou exemplos relacionados ao seu caso de uso.
-
-Durante o treinamento, monitore cuidadosamente a convergência da perda e valide o desempenho em dados retidos. O parâmetro beta pode precisar de ajustes para equilibrar o aprendizado de preferências com a manutenção das capacidades gerais do modelo. A avaliação regular em diversos prompts ajuda a garantir que o modelo esteja aprendendo as preferências pretendidas sem se ajustar demais.
-
-Compare os resultados do modelo com o modelo de referência para verificar a melhoria no alinhamento de preferências. Testar em uma variedade de prompts, incluindo casos extremos, ajuda a garantir um aprendizado de preferências robusto em diferentes cenários.
-
-## Próximos Passos
-
-⏩ Para obter experiência prática com o DPO, experimente fazer o [Tutorial DPO](./notebooks/dpo_finetuning_example.ipynb). Esse guia prático o orientará na implementação do alinhamento de preferências com seu próprio modelo, desde a preparação dos dados até o treinamento e a avaliação. 
-
-⏭️ Depois de concluir o tutorial, você pode explorar a página do [ORPO](./orpo.md) para conhecer outra técnica de alinhamento de preferências.
\ No newline at end of file
diff --git a/pt-br/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb b/pt-br/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
deleted file mode 100644
index f70dc684..00000000
--- a/pt-br/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
+++ /dev/null
@@ -1,5497 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Alinhamento de Preferências com a Otimização Direta de Preferências (DPO)\n",
-    "\n",
-    "Este caderno o guiará pelo processo de ajuste fino de um modelo de linguagem usando a Otimização Direta de Preferências (DPO). Usaremos o modelo SmolLM2-135M-Instruct que já passou por um treinamento SFT, portanto, o qual agora é compatível com o DPO. Você também pode usar o modelo que treinou em [1_instruction_tuning](../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb).\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "     <h2 style='margin: 0;color:blue'>Exercício: Alinhamento do SmolLM2 com o DPOTrainer</h2>\n",
-    "     <p>Pegue um conjunto de dados do hub do Hugging Face e alinhe um modelo a ele. </p> \n",
-    "     <p><b>Níveis de Dificuldade</b></p>\n",
-    "     <p>🐢 Use o conjunto de dados `trl-lib/ultrafeedback_binarized`.</p>\n",
-    "     <p>🐕 Experimente o conjunto de dados `argilla/ultrafeedback-binarized-preferences.</p>\n",
-    "     <p>🦁 Selecione um conjunto de dados relacionado a um caso de uso real no qual você esteja interessado ou use o modelo que você treinou em  \n",
-    "        <a href=\"../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb\">1_instruction_tuning</a>.</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install the requirements in Google Colab\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "# Authenticate to Hugging Face\n",
-    "\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()\n",
-    "\n",
-    "# for convenience you can create an environment variable containing your hub token as HF_TOKEN"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Importe os módulos\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "_zIBL8IssExG",
-    "outputId": "90d70bd2-30e7-49a4-d5bf-20d74deb6cec"
-   },
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "import os\n",
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "from datasets import load_dataset\n",
-    "from trl import DPOTrainer, DPOConfig"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "d8CvUgROUDw-"
-   },
-   "source": [
-    "## Formate o conjunto de dados"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "MCD77GZ60DOT"
-   },
-   "outputs": [],
-   "source": [
-    "# Load dataset\n",
-    "\n",
-    "# TODO: 🦁🐕 change the dataset to one of your choosing\n",
-    "dataset = load_dataset(path=\"trl-lib/ultrafeedback_binarized\", split=\"train\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO: 🐕 If your dataset is not represented as conversation lists, you can use the `process_dataset` function to convert it."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Selecione o modelo\n",
-    "\n",
-    "Usaremos o modelo SmolLM2-135M-Instruct que já passou por um treinamento SFT, que agora é compatível com o DPO. Você também pode usar o modelo que treinou em [1_instruction_tuning](../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb).\n",
-    "\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; width:80%; color:black'>\n",
-    "     <p>🦁 altere o modelo para o caminho ou o ID do repositório do modelo em que você treinou o <a href=\"../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb\">1_instruction_tuning</a>.</p>\n",
-    "</div>\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO: 🦁 change the model to the path or repo id of the model you trained in [1_instruction_tuning](../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb)\n",
-    "\n",
-    "model_name = \"HuggingFaceTB/SmolLM2-135M-Instruct\"\n",
-    "\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "# Model to fine-tune\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=model_name,\n",
-    "    torch_dtype=torch.float16,\n",
-    ").to(device)\n",
-    "model.config.use_cache = False\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
-    "tokenizer.pad_token = tokenizer.eos_token\n",
-    "\n",
-    "# Set our name for the finetune to be saved &/ uploaded to\n",
-    "finetune_name = \"SmolLM2-FT-DPO\"\n",
-    "finetune_tags = [\"smol-course\", \"module_1\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "DeT5eUK_UJgK"
-   },
-   "source": [
-    "## Treine o modelo com DPO"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
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-   "outputs": [],
-   "source": [
-    "# Training arguments\n",
-    "training_args = DPOConfig(\n",
-    "    # Training batch size per GPU\n",
-    "    per_device_train_batch_size=4,\n",
-    "    # Number of updates steps to accumulate before performing a backward/update pass\n",
-    "    # Effective batch size = per_device_train_batch_size * gradient_accumulation_steps\n",
-    "    gradient_accumulation_steps=4,\n",
-    "    # Saves memory by not storing activations during forward pass\n",
-    "    # Instead recomputes them during backward pass\n",
-    "    gradient_checkpointing=True,\n",
-    "    # Base learning rate for training\n",
-    "    learning_rate=5e-5,\n",
-    "    # Learning rate schedule - 'cosine' gradually decreases LR following cosine curve\n",
-    "    lr_scheduler_type=\"cosine\",\n",
-    "    # Total number of training steps\n",
-    "    max_steps=200,\n",
-    "    # Disables model checkpointing during training\n",
-    "    save_strategy=\"no\",\n",
-    "    # How often to log training metrics\n",
-    "    logging_steps=1,\n",
-    "    # Directory to save model outputs\n",
-    "    output_dir=\"smol_dpo_output\",\n",
-    "    # Number of steps for learning rate warmup\n",
-    "    warmup_steps=100,\n",
-    "    # Use bfloat16 precision for faster training\n",
-    "    bf16=True,\n",
-    "    # Disable wandb/tensorboard logging\n",
-    "    report_to=\"none\",\n",
-    "    # Keep all columns in dataset even if not used\n",
-    "    remove_unused_columns=False,\n",
-    "    # Enable MPS (Metal Performance Shaders) for Mac devices\n",
-    "    use_mps_device=device == \"mps\",\n",
-    "    # Model ID for HuggingFace Hub uploads\n",
-    "    hub_model_id=finetune_name,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer = DPOTrainer(\n",
-    "    # The model to be trained\n",
-    "    model=model,\n",
-    "    # Training configuration from above\n",
-    "    args=training_args,\n",
-    "    # Dataset containing preferred/rejected response pairs\n",
-    "    train_dataset=dataset,\n",
-    "    # Tokenizer for processing inputs\n",
-    "    processing_class=tokenizer,\n",
-    "    # DPO-specific temperature parameter that controls the strength of the preference model\n",
-    "    # Lower values (like 0.1) make the model more conservative in following preferences\n",
-    "    beta=0.1,\n",
-    "    # Maximum length of the input prompt in tokens\n",
-    "    max_prompt_length=1024,\n",
-    "    # Maximum combined length of prompt + response in tokens\n",
-    "    max_length=1536,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Train the model\n",
-    "trainer.train()\n",
-    "\n",
-    "# Save the model\n",
-    "trainer.save_model(f\"./{finetune_name}\")\n",
-    "\n",
-    "# Save to the huggingface hub if login (HF_TOKEN is set)\n",
-    "if os.getenv(\"HF_TOKEN\"):\n",
-    "    trainer.push_to_hub(tags=finetune_tags)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 💐 Você terminou!\n",
-    "\n",
-    "Este caderno forneceu um guia passo-a-passo para o ajuste fino do modelo `HuggingFaceTB/SmolLM2-135M` usando o `DPOTrainer`. Seguindo essas etapas, você pode adaptar o modelo para executar tarefas específicas com mais eficiência. Se quiser continuar trabalhando neste curso, aqui estão as etapas que você pode experimentar:\n",
-    "\n",
-    "- Experimente este caderno em uma dificuldade maior\n",
-    "- Revisar o PR de um colega\n",
-    "- Melhorar o material do curso por meio de uma Issue ou PR."
-   ]
-  }
- ],
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diff --git a/pt-br/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb b/pt-br/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
deleted file mode 100644
index ed78ad3b..00000000
--- a/pt-br/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
+++ /dev/null
@@ -1,8643 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Alinhamento de Preferências com Otimização de Preferências de Razão de Chances (ORPO)\n",
-    "\n",
-    "Este caderno o guiará pelo processo de ajuste fino de um modelo de linguagem usando a Otimização de Preferências de Razão de Chances (ORPO). Usaremos o modelo SmolLM2-135M que **não** passou pelo treinamento do SFT, portanto, não é compatível com o DPO. Isso significa que você não pode usar o modelo que treinou em [1_instruction_tuning](../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb).\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "     <h2 style='margin: 0;color:blue'>Exercício: Alinhamento do SmolLM2 com o DPOTrainer</h2>\n",
-    "     <p>Pegue um conjunto de dados do hub do Hugging Face e alinhe um modelo a ele. </p> \n",
-    "     <p><b>Níveis de Dificuldade</b></p>\n",
-    "     <p>🐢 Use o conjunto de dados `trl-lib/ultrafeedback_binarized`.</p>\n",
-    "     <p>🐕 Experimente usar o conjunto de dados `argilla/ultrafeedback-binarized-preferences.</p>\n",
-    "     <p>🦁 Experimente usar um subconjunto do conjunto de dados `orpo-dpo-mix-40k` de mlabonne.</p>\n",
-    "</div>\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Importe os módulos\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "EwNxh0pCQhfI",
-    "outputId": "e4c288e1-2fa3-4717-d9dd-ae3ec0b91e3b"
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/ben/code/smol-course/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "import os\n",
-    "from datasets import load_dataset\n",
-    "from transformers import (\n",
-    "    AutoModelForCausalLM,\n",
-    "    AutoTokenizer,\n",
-    ")\n",
-    "from trl import ORPOConfig, ORPOTrainer, setup_chat_format\n",
-    "\n",
-    "# Authenticate to Hugging Face\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Formate o conjunto de dados"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Generating train split: 100%|██████████| 62135/62135 [00:00<00:00, 450465.27 examples/s]\n",
-      "Generating test split: 100%|██████████| 1000/1000 [00:00<00:00, 382866.64 examples/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Load dataset\n",
-    "\n",
-    "# TODO: 🦁🐕 change the dataset to one of your choosing\n",
-    "dataset = load_dataset(path=\"trl-lib/ultrafeedback_binarized\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO: 🐕 If your dataset is not represented as conversation lists, you can use the `process_dataset` function to convert it."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Defina o modelo"
-   ]
-  },
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-    "outputId": "62246d3e-5e5d-4910-a9d9-dd1d24fd3c01"
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-   "outputs": [],
-   "source": [
-    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
-    "\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "# Model to fine-tune\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=model_name,\n",
-    "    torch_dtype=torch.float16,\n",
-    ").to(device)\n",
-    "model.config.use_cache = False\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
-    "model, tokenizer = setup_chat_format(model, tokenizer)\n",
-    "\n",
-    "# Set our name for the finetune to be saved &/ uploaded to\n",
-    "finetune_name = \"SmolLM2-FT-DPO\"\n",
-    "finetune_tags = [\"smol-course\", \"module_1\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Treine o modelo com ORPO"
-   ]
-  },
-  {
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-    "colab": {
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-   "outputs": [],
-   "source": [
-    "orpo_args = ORPOConfig(\n",
-    "    # Small learning rate to prevent catastrophic forgetting\n",
-    "    learning_rate=8e-6,\n",
-    "    # Linear learning rate decay over training\n",
-    "    lr_scheduler_type=\"linear\",\n",
-    "    # Maximum combined length of prompt + completion\n",
-    "    max_length=1024,\n",
-    "    # Maximum length for input prompts\n",
-    "    max_prompt_length=512,\n",
-    "    # Controls weight of the odds ratio loss (λ in paper)\n",
-    "    beta=0.1,\n",
-    "    # Batch size for training\n",
-    "    per_device_train_batch_size=2,\n",
-    "    per_device_eval_batch_size=2,\n",
-    "    # Helps with training stability by accumulating gradients before updating\n",
-    "    gradient_accumulation_steps=4,\n",
-    "    # Memory-efficient optimizer for CUDA, falls back to adamw_torch for CPU/MPS\n",
-    "    optim=\"paged_adamw_8bit\" if device == \"cuda\" else \"adamw_torch\",\n",
-    "    # Number of training epochs\n",
-    "    num_train_epochs=1,\n",
-    "    # When to run evaluation\n",
-    "    evaluation_strategy=\"steps\",\n",
-    "    # Evaluate every 20% of training\n",
-    "    eval_steps=0.2,\n",
-    "    # Log metrics every step\n",
-    "    logging_steps=1,\n",
-    "    # Gradual learning rate warmup\n",
-    "    warmup_steps=10,\n",
-    "    # Disable external logging\n",
-    "    report_to=\"none\",\n",
-    "    # Where to save model/checkpoints\n",
-    "    output_dir=\"./results/\",\n",
-    "    # Enable MPS (Metal Performance Shaders) if available\n",
-    "    use_mps_device=device == \"mps\",\n",
-    "    hub_model_id=finetune_name,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer = ORPOTrainer(\n",
-    "    model=model,\n",
-    "    args=orpo_args,\n",
-    "    train_dataset=dataset[\"train\"],\n",
-    "    eval_dataset=dataset[\"test\"],\n",
-    "    processing_class=tokenizer,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.train()  # Train the model\n",
-    "\n",
-    "# Save the model\n",
-    "trainer.save_model(f\"./{finetune_name}\")\n",
-    "\n",
-    "# Save to the huggingface hub if login (HF_TOKEN is set)\n",
-    "if os.getenv(\"HF_TOKEN\"):\n",
-    "    trainer.push_to_hub(tags=finetune_tags)"
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-    "## 💐 Você terminou!\n",
-    "\n",
-    "Este caderno forneceu um guia passo-a-passo para o ajuste fino do modelo `HuggingFaceTB/SmolLM2-135M` usando o `ORPOTrainer`. Seguindo essas etapas, você pode adaptar o modelo para executar tarefas específicas com mais eficiência. Se quiser continuar trabalhando neste curso, aqui estão as etapas que você pode experimentar:\n",
-    "\n",
-    "- Experimente este notebook em uma dificuldade maior\n",
-    "- Revisar o PR de um colega\n",
-    "- Melhorar o material do curso por meio de uma Issue ou PR."
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diff --git a/pt-br/2_preference_alignment/orpo.md b/pt-br/2_preference_alignment/orpo.md
deleted file mode 100644
index 752c6d6c..00000000
--- a/pt-br/2_preference_alignment/orpo.md
+++ /dev/null
@@ -1,84 +0,0 @@
-# Otimização de Preferências de Razão de Chances (ORPO)
-
-A ORPO (Odds Ratio Preference Optimization) é uma nova técnica de ajuste fino que combina o ajuste fino e o alinhamento de preferências em um único processo unificado. Essa abordagem combinada oferece vantagens em termos de eficiência e desempenho em comparação com métodos tradicionais como RLHF ou DPO.
-
-## Entendendo sobre o ORPO
-
-O alinhamento com métodos como o DPO normalmente envolve duas etapas separadas: ajuste fino supervisionado para adaptar o modelo a um domínio e formato, seguido pelo alinhamento de preferências para alinhar com as preferências humanas. Embora o SFT adapte efetivamente os modelos aos domínios-alvo, ele pode aumentar inadvertidamente a probabilidade de gerar respostas desejáveis e indesejáveis. O ORPO aborda essa limitação integrando as duas etapas em um único processo, conforme ilustrado na comparação abaixo:
-
-![Comparação das Técnicas de Alinhamento](https://argilla.io/images/blog/mantisnlp-rlhf/part-8-alignments.png)
-*Comparação de diferentes técnicas de alinhamento de modelos*
-
-## Como o ORPO Funciona
-
-O processo de treinamento utiliza um conjunto de dados de preferências semelhante ao que usamos para o DPO, em que cada exemplo de treinamento contém um prompt de entrada com duas respostas: uma que é preferida e outra que é rejeitada. Diferentemente de outros métodos de alinhamento que exigem estágios e modelos de referência separados, o ORPO integra o alinhamento de preferências diretamente ao processo de ajuste fino supervisionado. Essa abordagem monolítica o torna livre de modelos de referência, computacionalmente mais eficiente e eficiente em termos de memória com menos FLOPs.
-
-O ORPO cria um novo objetivo ao combinar dois componentes principais:
-
-1. **Função de perda do SFT**: A perda padrão de log-verossimilhança negativa usada na modelagem de linguagem, que maximiza a probabilidade de gerar tokens de referência. Isso ajuda a manter as capacidades gerais de linguagem do modelo.
-
-2. **Função de perda da Razão de Chances**: Um novo componente que penaliza respostas indesejáveis e recompensa as preferidas. Essa função de perda usa índices de probabilidade (razão de chances) para contrastar efetivamente entre respostas favorecidas e desfavorecidas no nível do token.
-
-Juntos, esses componentes orientam o modelo a se adaptar às gerações desejadas para o domínio específico e, ao mesmo tempo, desencorajam ativamente as gerações do conjunto de respostas rejeitadas. O mecanismo de razão de chances oferece uma maneira natural de medir e otimizar a preferência do modelo entre os resultados escolhidos e rejeitados. Se quiser se aprofundar na matemática, você pode ler o [artigo sobre ORPO](https://arxiv.org/abs/2403.07691). Se quiser saber mais sobre o ORPO do ponto de vista da implementação, confira como a função de perda do ORPO é calculada no [módulo TRL](https://github.com/huggingface/trl/blob/b02189aaa538f3a95f6abb0ab46c0a971bfde57e/trl/trainer/orpo_trainer.py#L660).
-
-## Desempenho e Resultados
-
-O ORPO demonstrou resultados impressionantes em vários benchmarks. No MT-Bench, ele alcança pontuações competitivas em diferentes categorias:
-
-![Resultados do MT-Bench](https://argilla.io/images/blog/mantisnlp-rlhf/part-8-mtbench.png)
-*Resultados do MT-Bench por categoria para os modelos Mistral-ORPO*
-
-Quando comparado a outros métodos de alinhamento, o ORPO apresenta desempenho superior no AlpacaEval 2.0:
-
-![Resultados do AlpacaEval](https://argilla.io/images/blog/mantisnlp-rlhf/part-8-winrate.png)
-*Pontuações do AlpacaEval 2.0 em diferentes métodos de alinhamento*
-
-Em comparação com p SFT+DPO, o ORPO reduz os requisitos de computação, eliminando a necessidade de um modelo de referência e reduzindo pela metade o número de passagens de avanço por lote. Além disso, o processo de treinamento é mais estável em diferentes tamanhos de modelos e conjuntos de dados, exigindo menos hiperparâmetros para ajustar. Em termos de desempenho, o ORPO se iguala aos modelos maiores e mostra melhor alinhamento com as preferências humanas.
-
-## Implementação 
-
-A implementação bem-sucedida do ORPO depende muito de dados de preferência de alta qualidade. Os dados de treinamento devem seguir diretrizes claras de anotação e fornecer uma representação equilibrada das respostas preferidas e rejeitadas em diversos cenários. 
-
-### Implementação com TRL
-
-O ORPO pode ser implementado usando o módulo Transformers Reinforcement Learning (TRL). Aqui está um exemplo básico:
-
-```python
-from trl import ORPOConfig, ORPOTrainer
-
-# Configure ORPO training
-orpo_config = ORPOConfig(
-    learning_rate=1e-5,
-    per_device_train_batch_size=4,
-    gradient_accumulation_steps=4,
-    max_steps=1000,
-    orpo_alpha=1.0,  # Controls strength of preference optimization
-    orpo_beta=0.1,   # Temperature parameter for odds ratio
-)
-
-# Initialize trainer
-trainer = ORPOTrainer(
-    model=model,
-    args=orpo_config,
-    train_dataset=dataset,
-    tokenizer=tokenizer,
-)
-
-# Start training
-trainer.train()
-```
-
-Principais parâmetros a serem considerados:
-- `orpo_alpha`: Controla a força da otimização de preferências
-- `orpo_beta`: Parâmetro de temperatura para o cálculo da razão de chances
-- `learning_rate`: Deve ser relativamente pequeno para evitar o esquecimento catastrófico
-- `gradient_accumulation_steps`: Ajuda na estabilidade do treinamento
-
-## Próximos Passos
-
-⏩ Experimente o [Tutorial do ORPO](./notebooks/orpo_tutorial.ipynb) para implementar essa abordagem unificada ao alinhamento de preferências.
-
-## Referências
-- [Artigo sobre ORPO](https://arxiv.org/abs/2403.07691)
-- [Documentação TRL](https://huggingface.co/docs/trl/index)
-- [Guia Argilla RLHF](https://argilla.io/blog/mantisnlp-rlhf-part-8/) 
\ No newline at end of file
diff --git a/pt-br/3_parameter_efficient_finetuning/README.md b/pt-br/3_parameter_efficient_finetuning/README.md
deleted file mode 100644
index 1d015514..00000000
--- a/pt-br/3_parameter_efficient_finetuning/README.md
+++ /dev/null
@@ -1,40 +0,0 @@
-# Parameter-Efficient Fine-Tuning (PEFT) (Ajuste Fino com Eficiência de Parâmetro)
-
-À medida que os modelos de linguagem aumentam, o ajuste fino tradicional torna-se cada vez mais desafiador. O ajuste fino completo de um modelo com 1,7 bilhão de parâmetros requer uma quantidade considerável de memória da GPU, torna caro o armazenamento de cópias separadas do modelo e apresenta o risco de um esquecimento catastrófico das capacidades originais do modelo. Os métodos de ajuste fino com eficiência de parâmetros (PEFT) abordam esses desafios modificando apenas um pequeno subconjunto de parâmetros do modelo e mantendo a maior parte do modelo congelada.
-
-O ajuste fino tradicional atualiza todos os parâmetros do modelo durante o treinamento, o que se torna impraticável para modelos grandes. Os métodos PEFT introduzem abordagens para adaptar modelos usando menos parâmetros treináveis, geralmente menos de 1% do tamanho do modelo original. Essa redução drástica nos parâmetros treináveis permite:
-
-- Ajuste fino no hardware do consumidor com memória de GPU limitada
-- Armazenamento eficiente de várias adaptações de tarefas específicas
-- Melhor generalização em cenários com poucos dados
-- Ciclos de treinamento e iteração mais rápidos
-
-## Métodos Disponíveis
-
-Neste módulo, abordaremos dois métodos populares de PEFT:
-
-### 1️⃣ LoRA (Low-Rank Adaptation - Adaptação de Baixa Classificação)
-
-O LoRA surgiu como o método PEFT mais amplamente adotado, oferecendo uma solução elegante para a adaptação eficiente do modelo. Em vez de modificar o modelo inteiro, o **LoRA injeta matrizes treináveis nas camadas de atenção do modelo.**  Essa abordagem normalmente reduz os parâmetros treináveis em cerca de 90%, mantendo um desempenho comparável ao ajuste fino completo. Exploraremos o LoRA na seção [LoRA (Adaptação de Baixa Classificação)](./lora_adapters.md).
- 
-### 2️⃣ Ajuste de Prompts
-
-O ajuste de prompts oferece uma abordagem **ainda mais leve** ao **adicionar tokens treináveis à entrada** em vez de modificar os pesos do modelo. O ajuste de prompt é menos popular que o LoRA, mas pode ser uma técnica útil para adaptar rapidamente um modelo a novas tarefas ou domínios. Exploraremos o ajuste de prompt na seção [Ajuste de Prompt](./prompt_tuning.md).
-
-## Cadernos de Exercícios
-
-| Título | Descrição | Exercício | Link | Colab |
-|-------|-------------|----------|------|-------|
-| Ajuste fino do LoRA | Aprenda a fazer o ajuste fino de modelos usando adaptadores do LoRA | 🐢 Treine um modelo usando o LoRA< br>🐕 Experimente com diferentes valores de classificação<br>🦁 Compare o desempenho com o ajuste fino completo | [Exercício](./notebooks/finetune_sft_peft.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | |
-| Carregue adaptadores LoRA | Aprenda como carregar e usar adaptadores LoRA treinados | 🐢 Carregar adaptadores pré-treinados< br>🐕 Mesclar adaptadores com o modelo de base<br>🦁 Alternar entre vários adaptadores | [Exercício](./notebooks/load_lora_adapter.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | 
-<!-- | Ajuste de prompts | Aprenda como implementar o ajuste de prompts | 🐢 Treine prompts flexíveis<br>🐕 Compare diferentes estratégias de inicialização< br>🦁 Avalie em várias tarefas | [Exercício](./notebooks/prompt_tuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/prompt_tuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | -->
-
-## Referências
-
-- [Documentação PEFT](https://huggingface.co/docs/peft)
-- [Artigo sobre LoRA](https://arxiv.org/abs/2106.09685)
-- [Artigo sobre QLoRA](https://arxiv.org/abs/2305.14314)
-- [Artigo sobre Ajuste de Prompts](https://arxiv.org/abs/2104.08691)
-- [Guia PEFT do Hugging Face](https://huggingface.co/blog/peft)
-- [Como ajustar os LLMs em 2024 com o Hugging Face](https://www.philschmid.de/fine-tune-llms-in-2024-with-trl) 
-- [TRL](https://huggingface.co/docs/trl/index)
\ No newline at end of file
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diff --git a/pt-br/3_parameter_efficient_finetuning/lora_adapters.md b/pt-br/3_parameter_efficient_finetuning/lora_adapters.md
deleted file mode 100644
index 411af9af..00000000
--- a/pt-br/3_parameter_efficient_finetuning/lora_adapters.md
+++ /dev/null
@@ -1,124 +0,0 @@
-# LoRA (Low-Rank Adaptation - Adaptação de Baixa Classificação)
-
-LoRA tornou-se o método PEFT mais amplamente adotado. Ele funciona adicionando pequenas matrizes de decomposição de classificação aos pesos de atenção, reduzindo tipicamente os parâmetros treináveis em cerca de 90%.
-
-## Entendendo o LoRA
-
-LoRA (Adaptação de Baixa Classificação) é uma técnica de ajuste fino eficiente em termos de parâmetros que congela os pesos do modelo pré-treinado e injeta matrizes treináveis de decomposição de classificação nas camadas do modelo. Em vez de treinar todos os parâmetros do modelo durante o ajuste fino, o LoRA decompõe as atualizações de peso em matrizes menores por meio da decomposição de baixa classificação, reduzindo significativamente o número de parâmetros treináveis enquanto mantém o desempenho do modelo. Por exemplo, quando aplicado ao GPT-3 175B, o LoRA reduziu os parâmetros treináveis em 10.000x e os requisitos de memória GPU em 3x em comparação ao ajuste fino completo. Você pode saber mais sobre o LoRA no [Artigo sobre LoRA](https://arxiv.org/pdf/2106.09685).
-
-O LoRA funciona adicionando pares de matrizes de decomposição de classificação às camadas do transformer, geralmente focando nos pesos de atenção. Durante a inferência, esses pesos adaptadores podem ser mesclados com o modelo base, resultando em nenhuma sobrecarga adicional de latência. LoRA é particularmente útil para adaptar modelos de linguagem de grande porte a tarefas ou domínios específicos, mantendo os requisitos de recursos gerenciáveis.
-
-## Carregando Adaptadores LoRA
-
-Os adaptadores podem ser carregados em um modelo pré-treinado com load_adapter(), o que é útil para experimentar diferentes adaptadores cujos pesos não estão mesclados. Defina os pesos do adaptador ativo com a função set_adapter(). Para retornar ao modelo base, você pode usar unload() para descarregar todos os módulos LoRA. Isso facilita a alternância entre pesos específicos de tarefas.
-
-```python
-from transformers import AutoModelForCausalLM
-from peft import PeftModel
-
-base_model = AutoModelForCausalLM.from_pretrained("<base_model_name>")
-peft_model_id = "<peft_adapter_id>"
-model = PeftModel.from_pretrained(base_model, peft_model_id)
-```
-
-![lora_load_adapter](./images/lora_adapter.png)
-
-## Mesclando Adaptadores LoRA
-
-Após o treinamento com LoRA, você pode querer mesclar os pesos do adaptador de volta ao modelo base para facilitar a implantação. Isso cria um único modelo com os pesos combinados, eliminando a necessidade de carregar adaptadores separadamente durante a inferência.
-
-processo de mesclagem requer atenção ao gerenciamento de memória e precisão. Como será necessário carregar o modelo base e os pesos do adaptador simultaneamente, garanta memória suficiente na GPU/CPU. Usar `device_map="auto"` no `transformers` ajudará no gerenciamento automático de memória. Mantenha uma precisão consistente (por exemplo, float16) durante todo o processo, correspondendo à precisão usada durante o treinamento e salvando o modelo mesclado no mesmo formato para implantação. Antes de implantar, sempre valide o modelo mesclado comparando suas saídas e métricas de desempenho com a versão baseada em adaptadores.
-
-Os adaptadores também são convenientes para alternar entre diferentes tarefas ou domínios. Você pode carregar o modelo base e os pesos do adaptador separadamente, permitindo alternâncias rápidas entre pesos específicos de tarefas. 
-
-## Guia de Implementação
-
-O diretório `notebooks/` contém tutoriais práticos e exercícios para implementar diferentes métodos PEFT. Comece com `load_lora_adapter_example.ipynb` para uma introdução básica e depois veja `lora_finetuning.ipynb` para um estudo mais detalhado sobre como ajustar um modelo com LoRA e SFT.
-
-Ao implementar métodos PEFT, comece com valores baixos de classificação (4-8) para LoRA e monitore a perda de treinamento. Use conjuntos de validação para evitar overfitting e compare os resultados com as linhas de base de ajuste fino completo sempre que possível. A eficácia de diferentes métodos pode variar conforme a tarefa, então a experimentação é essencial.
-
-## OLoRA
-
-[OLoRA](https://arxiv.org/abs/2406.01775) utiliza decomposição QR para inicializar os adaptadores LoRA. OLoRA traduz os pesos base do modelo por um fator de suas decomposições QR, ou seja, altera os pesos antes de realizar qualquer treinamento sobre eles. Essa abordagem melhora significativamente a estabilidade, acelera a velocidade de convergência e, por fim, alcança um desempenho superior.
-
-## Usando TRL com PEFT
-
-Os métodos PEFT podem ser combinados com TRL (Reinforcement Learning com Transformers) para ajuste fino eficiente. Essa integração é particularmente útil para RLHF (Reinforcement Learning from Human Feedback), pois reduz os requisitos de memória.
-
-```python
-from peft import LoraConfig
-from transformers import AutoModelForCausalLM
-
-# Load model with PEFT config
-lora_config = LoraConfig(
-    r=16,
-    lora_alpha=32,
-    lora_dropout=0.05,
-    bias="none",
-    task_type="CAUSAL_LM"
-)
-
-# Load model on specific device
-model = AutoModelForCausalLM.from_pretrained(
-    "your-model-name",
-    load_in_8bit=True,  # Optional: use 8-bit precision
-    device_map="auto",
-    peft_config=lora_config
-)
-```
-
-No exemplo acima, usamos `device_map="auto"` para atribuir automaticamente o modelo ao dispositivo correto. Você também pode atribuir manualmente o modelo a um dispositivo específico usando `device_map={"": device_index}`. Também é possível escalar o treinamento em várias GPUs enquanto mantém o uso de memória eficiente.
-
-## Implementação Básica de Mesclagem
-
-Após treinar um adaptador LoRA, você pode mesclar os pesos do adaptador de volta ao modelo base. Veja como fazer isso:
-
-```python
-import torch
-from transformers import AutoModelForCausalLM
-from peft import PeftModel
-
-# 1. Load the base model
-base_model = AutoModelForCausalLM.from_pretrained(
-    "base_model_name",
-    torch_dtype=torch.float16,
-    device_map="auto"
-)
-
-# 2. Load the PEFT model with adapter
-peft_model = PeftModel.from_pretrained(
-    base_model,
-    "path/to/adapter",
-    torch_dtype=torch.float16
-)
-
-# 3. Merge adapter weights with base model
-try:
-    merged_model = peft_model.merge_and_unload()
-except RuntimeError as e:
-    print(f"Merging failed: {e}")
-    # Implement fallback strategy or memory optimization
-
-# 4. Save the merged model
-merged_model.save_pretrained("path/to/save/merged_model")
-```
-
-Se você encontrar discrepâncias de tamanho no modelo salvo, garanta que está salvando também o tokenizador:
-
-```python
-# Save both model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained("base_model_name")
-merged_model.save_pretrained("path/to/save/merged_model")
-tokenizer.save_pretrained("path/to/save/merged_model")
-```
-
-## Próximos Passos
-
-⏩ Prossiga para o guia de [Ajuste de Prompts](prompt_tuning.md) para aprender como ajustar um modelo com ajuste de prompts.
-⏩ Veja o [Tutorial de Carregamento de Adaptadores LoRA](./notebooks/load_lora_adapter.ipynb) para aprender como carregar adaptadores LoRA.
-
-# Referências
-
-- [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/pdf/2106.09685)
-- [Documentação PEFT](https://huggingface.co/docs/peft)
-- [Blog do Hugging Face sobre PEFT](https://huggingface.co/blog/peft)
diff --git a/pt-br/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb b/pt-br/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
deleted file mode 100644
index 472a2c34..00000000
--- a/pt-br/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
+++ /dev/null
@@ -1,515 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "z-6LLOPZouLg"
-   },
-   "source": [
-    "# Como Ajustar LLMs com Adaptadores LoRA usando Hugging Face TRL\n",
-    "\n",
-    "Este notebook demonstra como ajustar eficientemente grandes modelos de linguagem usando adaptadores LoRA (Adaptação de Baixa Classificação). LoRA é uma técnica de ajuste fino eficiente em termos de parâmetros que:\n",
-    "- Congela os pesos do modelo pré-treinado\n",
-    "- Adiciona pequenas matrizes treináveis de decomposição de classificação às camadas de atenção\n",
-    "- Normalmente reduz os parâmetros treináveis em ~90%\n",
-    "- Mantém o desempenho do modelo sendo eficiente em termos de memória\n",
-    "\n",
-    "Vamos abordar:\n",
-    "1. Configurar o ambiente de desenvolvimento e a configuração do LoRA\n",
-    "2. Criar e preparar o conjunto de dados para o treinamento do adaptador\n",
-    "3. Ajustar o modelo usando `trl` e `SFTTrainer` com adaptadores LoRA\n",
-    "4. Testar o modelo e mesclar adaptadores (opcional)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "fXqd9BXgouLi"
-   },
-   "source": [
-    "## 1. Configurar o ambiente de desenvolvimento\n",
-    "\n",
-    "Nosso primeiro passo é instalar os módulos do Hugging Face e o PyTorch, incluindo trl, transformers e datasets. Se você ainda não ouviu falar do trl, não se preocupe. É uma nova biblioteca baseada em transformers e datasets, que facilita o ajuste fino, o RLHF (Reinforcement Learning from Human Feedback) e o alinhamento de modelos de linguagem abertos (open LLMs).\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "tKvGVxImouLi"
-   },
-   "outputs": [],
-   "source": [
-    "# Install the requirements in Google Colab\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "# Authenticate to Hugging Face\n",
-    "\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()\n",
-    "\n",
-    "# for convenience you can create an environment variable containing your hub token as HF_TOKEN"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "XHUzfwpKouLk"
-   },
-   "source": [
-    "## 2. Carregue o conjunto de dados"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "id": "z4p6Bvo7ouLk"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['full_topic', 'messages'],\n",
-       "        num_rows: 2260\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['full_topic', 'messages'],\n",
-       "        num_rows: 119\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Load a sample dataset\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "# TODO: define your dataset and config using the path and name parameters\n",
-    "dataset = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")\n",
-    "dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "9TOhJdtsouLk"
-   },
-   "source": [
-    "## 3. Ajustar uma LLM usando `trl` e o `SFTTrainer` com o LoRA\n",
-    "\n",
-    "O [SFTTrainer](https://huggingface.co/docs/trl/sft_trainer) do `trl` oferece integração com adaptadores LoRA através do módulo [PEFT](https://huggingface.co/docs/peft/en/index). As principais vantagens dessa configuração incluem:\n",
-    "\n",
-    "1. **Eficiência de Memória**: \n",
-    "   - Apenas os parâmetros dos adaptadores são armazenados na memória da GPU\n",
-    "   - Os pesos do modelo base permanecem congelados e podem ser carregados em precisão reduzida\n",
-    "\n",
-    "   - Enables fine-tuning of large models on consumer GPUs\n",
-    "\n",
-    "2. **Recursos de Treinamento**:\n",
-    "   - Integração nativa PEFT/LoRA com configuração mínima\n",
-    "   - Suporte ao QLoRA (LoRA Quantizado) para uma eficiência de memória ainda melhor\n",
-    "\n",
-    "3. **Gerenciamento de Adaptadores**:\n",
-    "   - Salvamento de pesos dos adaptadores durante checkpoints\n",
-    "   - Recursos para mesclar adaptadores de volta ao modelo base\n",
-    "\n",
-    "Usaremos LoRA em nosso exemplo, que combina LoRA com quantização de 4 bits para reduzir ainda mais o uso de memória sem sacrificar o desempenho. A configuração requer apenas algumas etapas:\n",
-    "1. Definir a configuração do LoRA (rank, alpha, dropout)\n",
-    "2. Criar o SFTTrainer com configuração PEFT\n",
-    "3. Treinar e salvar os pesos dos adaptadores\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import necessary libraries\n",
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "from datasets import load_dataset\n",
-    "from trl import SFTConfig, SFTTrainer, setup_chat_format\n",
-    "import torch\n",
-    "\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "# Load the model and tokenizer\n",
-    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
-    "\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=model_name\n",
-    ").to(device)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
-    "\n",
-    "# Set up the chat format\n",
-    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n",
-    "\n",
-    "# Set our name for the finetune to be saved &/ uploaded to\n",
-    "finetune_name = \"SmolLM2-FT-MyDataset\"\n",
-    "finetune_tags = [\"smol-course\", \"module_1\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ZbuVArTHouLk"
-   },
-   "source": [
-    "O `SFTTrainer` oferece uma integração nativa com o `peft`, tornando extremamente fácil ajustar eficientemente LLMs, como com o LoRA, por exemplo. Precisamos apenas criar nosso `LoraConfig` e fornecê-lo ao trainer.\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'> \n",
-    "    <h2 style='margin: 0;color:blue'>Exercício: Definir parâmetros LoRA para ajuste fino</h2> \n",
-    "    <p>Escolha um conjunto de dados do hub do Hugging Face e ajuste um modelo com ele.</p> \n",
-    "    <p><b>Níveis de Dificuldade</b></p> \n",
-    "    <p>🐢 Use os parâmetros gerais para um ajuste fino arbitrário.</p> \n",
-    "    <p>🐕 Ajuste os parâmetros e revise em pesos & biases.</p> \n",
-    "    <p>🦁 Ajuste os parâmetros e mostre a mudança nos resultados de inferência.</p> \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "blDSs9swouLk"
-   },
-   "outputs": [],
-   "source": [
-    "from peft import LoraConfig\n",
-    "\n",
-    "# TODO: Configure LoRA parameters\n",
-    "# r: rank dimension for LoRA update matrices (smaller = more compression)\n",
-    "rank_dimension = 6\n",
-    "# lora_alpha: scaling factor for LoRA layers (higher = stronger adaptation)\n",
-    "lora_alpha = 8\n",
-    "# lora_dropout: dropout probability for LoRA layers (helps prevent overfitting)\n",
-    "lora_dropout = 0.05\n",
-    "\n",
-    "peft_config = LoraConfig(\n",
-    "    r=rank_dimension,  # Rank dimension - typically between 4-32\n",
-    "    lora_alpha=lora_alpha,  # LoRA scaling factor - typically 2x rank\n",
-    "    lora_dropout=lora_dropout,  # Dropout probability for LoRA layers\n",
-    "    bias=\"none\",  # Bias type for LoRA. the corresponding biases will be updated during training.\n",
-    "    target_modules=\"all-linear\",  # Which modules to apply LoRA to\n",
-    "    task_type=\"CAUSAL_LM\",  # Task type for model architecture\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "l5NUDPcaouLl"
-   },
-   "source": [
-    "Antes de iniciarmos nosso treinamento, precisamos definir os hiperparâmetros (`TrainingArguments`) que queremos usar.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "NqT28VZlouLl"
-   },
-   "outputs": [],
-   "source": [
-    "# Training configuration\n",
-    "# Hyperparameters based on QLoRA paper recommendations\n",
-    "args = SFTConfig(\n",
-    "    # Output settings\n",
-    "    output_dir=finetune_name,  # Directory to save model checkpoints\n",
-    "    # Training duration\n",
-    "    num_train_epochs=1,  # Number of training epochs\n",
-    "    # Batch size settings\n",
-    "    per_device_train_batch_size=2,  # Batch size per GPU\n",
-    "    gradient_accumulation_steps=2,  # Accumulate gradients for larger effective batch\n",
-    "    # Memory optimization\n",
-    "    gradient_checkpointing=True,  # Trade compute for memory savings\n",
-    "    # Optimizer settings\n",
-    "    optim=\"adamw_torch_fused\",  # Use fused AdamW for efficiency\n",
-    "    learning_rate=2e-4,  # Learning rate (QLoRA paper)\n",
-    "    max_grad_norm=0.3,  # Gradient clipping threshold\n",
-    "    # Learning rate schedule\n",
-    "    warmup_ratio=0.03,  # Portion of steps for warmup\n",
-    "    lr_scheduler_type=\"constant\",  # Keep learning rate constant after warmup\n",
-    "    # Logging and saving\n",
-    "    logging_steps=10,  # Log metrics every N steps\n",
-    "    save_strategy=\"epoch\",  # Save checkpoint every epoch\n",
-    "    # Precision settings\n",
-    "    bf16=True,  # Use bfloat16 precision\n",
-    "    # Integration settings\n",
-    "    push_to_hub=False,  # Don't push to HuggingFace Hub\n",
-    "    report_to=None,  # Disable external logging\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "cGhR7uFBouLl"
-   },
-   "source": [
-    "Agora temos todos os pedaços que precisamos para criar nosso `SFTTrainer` para então começar a treinar nosso modelo."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "M00Har2douLl"
-   },
-   "outputs": [],
-   "source": [
-    "max_seq_length = 1512  # max sequence length for model and packing of the dataset\n",
-    "\n",
-    "# Create SFTTrainer with LoRA configuration\n",
-    "trainer = SFTTrainer(\n",
-    "    model=model,\n",
-    "    args=args,\n",
-    "    train_dataset=dataset[\"train\"],\n",
-    "    peft_config=peft_config,  # LoRA configuration\n",
-    "    max_seq_length=max_seq_length,  # Maximum sequence length\n",
-    "    tokenizer=tokenizer,\n",
-    "    packing=True,  # Enable input packing for efficiency\n",
-    "    dataset_kwargs={\n",
-    "        \"add_special_tokens\": False,  # Special tokens handled by template\n",
-    "        \"append_concat_token\": False,  # No additional separator needed\n",
-    "    },\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "zQ_kRN24ouLl"
-   },
-   "source": [
-    "Comece treinando nosso modelo chamando a função `train ()` em nossa instância de `Trainer`. Isso iniciará o loop de treinamento e treinará nosso modelo por 3 épocas. Como estamos usando um método PEFT, salvaremos apenas os pesos do modelo adaptado e não o do modelo completo."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Tq4nIYqKouLl"
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "300e5dfbb4b54750b77324345c7591f9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/72 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "TrainOutput(global_step=72, training_loss=1.6402628521124523, metrics={'train_runtime': 195.2398, 'train_samples_per_second': 1.485, 'train_steps_per_second': 0.369, 'total_flos': 282267289092096.0, 'train_loss': 1.6402628521124523, 'epoch': 0.993103448275862})"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# start training, the model will be automatically saved to the hub and the output directory\n",
-    "trainer.train()\n",
-    "\n",
-    "# save model\n",
-    "trainer.save_model()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "y4HHSYYzouLl"
-   },
-   "source": [
-    "O treinamento com Flash Attention por 3 épocas com um conjunto de dados de 15 mil amostras levou 4:14:36 ​​em um `g5.2xlarge`. A instância custa `1,21 dólares/h`, o que nos leva a um custo total de apenas ~`5,3 dólares`.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "C309KsXjouLl"
-   },
-   "source": [
-    "### Mesclar Adaptador LoRA no Modelo Original\n",
-    "\n",
-    "Ao usar LoRA, treinamos apenas os pesos do adaptador mantendo o modelo base congelado. Durante o treinamento, salvamos apenas esses pesos de adaptador leves (~2-10MB) em vez de uma cópia completa do modelo. No entanto, para implantação, você talvez queira mesclar os adaptadores de volta ao modelo base para:\n",
-    "\n",
-    "1. **Implantação Simplificada**: Arquivo de modelo único em vez de modelo base + adaptadores\n",
-    "2. **Velocidade de Inferência**: Sem sobrecarga de computação de adaptadores\n",
-    "3. **Compatibilidade de Framework**: Melhor compatibilidade com serving frameworks \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from peft import AutoPeftModelForCausalLM\n",
-    "\n",
-    "\n",
-    "# Load PEFT model on CPU\n",
-    "model = AutoPeftModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=args.output_dir,\n",
-    "    torch_dtype=torch.float16,\n",
-    "    low_cpu_mem_usage=True,\n",
-    ")\n",
-    "\n",
-    "# Merge LoRA and base model and save\n",
-    "merged_model = model.merge_and_unload()\n",
-    "merged_model.save_pretrained(\n",
-    "    args.output_dir, safe_serialization=True, max_shard_size=\"2GB\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "-yO6E9quouLl"
-   },
-   "source": [
-    "## 3. Testar Modelo e Executar Inferência\n",
-    "\n",
-    "Após a conclusão do treinamento, queremos testar nosso modelo. Vamos carregar diferentes amostras do conjunto de dados original e avaliar o modelo baseado nessas amostras, usando um loop simples e a precisão como nossa métrica."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Exercício Bônus: Carregar Adaptador LoRA</h2>\n",
-    "    <p>Use o que você aprendeu no caderno de exemplo para carregar seu adaptador LoRA treinado para inferência.</p> \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "id": "I5B494OdouLl"
-   },
-   "outputs": [],
-   "source": [
-    "# free the memory again\n",
-    "del model\n",
-    "del trainer\n",
-    "torch.cuda.empty_cache()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "P1UhohVdouLl"
-   },
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from peft import AutoPeftModelForCausalLM\n",
-    "from transformers import AutoTokenizer, pipeline\n",
-    "\n",
-    "# Load Model with PEFT adapter\n",
-    "tokenizer = AutoTokenizer.from_pretrained(finetune_name)\n",
-    "model = AutoPeftModelForCausalLM.from_pretrained(\n",
-    "    finetune_name, device_map=\"auto\", torch_dtype=torch.float16\n",
-    ")\n",
-    "pipe = pipeline(\n",
-    "    \"text-generation\", model=merged_model, tokenizer=tokenizer, device=device\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "99uFDAuuouLl"
-   },
-   "source": [
-    "Vamos testar uns exemplos de prompts e ver como o modelo reage."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "id": "-shSmUbvouLl",
-    "outputId": "16d97c61-3b31-4040-c780-3c4de75c3824"
-   },
-   "outputs": [],
-   "source": [
-    "prompts = [\n",
-    "    \"What is the capital of Germany? Explain why thats the case and if it was different in the past?\",\n",
-    "    \"Write a Python function to calculate the factorial of a number.\",\n",
-    "    \"A rectangular garden has a length of 25 feet and a width of 15 feet. If you want to build a fence around the entire garden, how many feet of fencing will you need?\",\n",
-    "    \"What is the difference between a fruit and a vegetable? Give examples of each.\",\n",
-    "]\n",
-    "\n",
-    "\n",
-    "def test_inference(prompt):\n",
-    "    prompt = pipe.tokenizer.apply_chat_template(\n",
-    "        [{\"role\": \"user\", \"content\": prompt}],\n",
-    "        tokenize=False,\n",
-    "        add_generation_prompt=True,\n",
-    "    )\n",
-    "    outputs = pipe(\n",
-    "        prompt,\n",
-    "    )\n",
-    "    return outputs[0][\"generated_text\"][len(prompt) :].strip()\n",
-    "\n",
-    "\n",
-    "for prompt in prompts:\n",
-    "    print(f\"    prompt:\\n{prompt}\")\n",
-    "    print(f\"    response:\\n{test_inference(prompt)}\")\n",
-    "    print(\"-\" * 50)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.10"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/pt-br/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb b/pt-br/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
deleted file mode 100644
index 0a41f846..00000000
--- a/pt-br/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
+++ /dev/null
@@ -1,5061 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "otj46qRbtpnd",
-        "outputId": "0fa81296-44b7-479d-e03b-7ef196cc89e4"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install -q bitsandbytes datasets accelerate loralib\n",
-        "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 446,
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-            "93eb41e45b444ff284ae600f752bc1da",
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-            "4f1b47e2d7d64765b4f12dcc16878f1f",
-            "993fcaf75d47402d9f9f7c2b094295f1",
-            "e6787d84d8234d368dc48f8d43bdb2a1",
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-          ]
-        },
-        "id": "cg3fiQOvmI3Q",
-        "outputId": "92fcd520-ad2c-4488-8f66-664ffc566b89"
-      },
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
-        "import torch\n",
-        "import torch.nn as nn\n",
-        "import bitsandbytes as bnb\n",
-        "from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "S65GcxNGA9kz"
-      },
-      "source": [
-        "## Carregue os adaptadores do Hub\n",
-        "\n"
-      ]
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-        "import torch\n",
-        "import transformers\n",
-        "from datasets import load_dataset\n",
-        "from peft import LoraConfig, get_peft_model\n",
-        "from peft import PeftModel, PeftConfig\n",
-        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-        "\n",
-        "peft_model_id = \"ybelkada/opt-6.7b-lora\"\n",
-        "config = PeftConfig.from_pretrained(peft_model_id)\n",
-        "model = AutoModelForCausalLM.from_pretrained(\n",
-        "    config.base_model_name_or_path,\n",
-        "    return_dict=True,\n",
-        "    load_in_8bit=True,\n",
-        "    device_map=\"auto\",\n",
-        ")\n",
-        "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
-        "\n",
-        "# Load the Lora model\n",
-        "model = PeftModel.from_pretrained(model, peft_model_id)"
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-        "\n",
-        "with torch.cuda.amp.autocast():\n",
-        "  output_tokens = model.generate(**batch, max_new_tokens=50)\n",
-        "\n",
-        "print('\\n\\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True))"
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diff --git a/pt-br/3_parameter_efficient_finetuning/prompt_tuning.md b/pt-br/3_parameter_efficient_finetuning/prompt_tuning.md
deleted file mode 100644
index 7e48dc42..00000000
--- a/pt-br/3_parameter_efficient_finetuning/prompt_tuning.md
+++ /dev/null
@@ -1,74 +0,0 @@
-# Ajuste de Prompts
-
-O ajuste de prompts (prompt tuning) é uma abordagem eficiente em termos de parâmetros que modifica as representações de entrada em vez dos pesos do modelo. Diferente do ajuste fino tradicional, que atualiza todos os parâmetros do modelo, o ajuste de prompts adiciona e otimiza um pequeno conjunto de tokens treináveis, mantendo o modelo base congelado.
-
-## Entendendo o Ajuste de Prompts
-
-O ajuste de prompts é uma alternativa eficiente ao ajuste fino de modelos que adiciona vetores contínuos treináveis (soft prompts) ao texto de entrada. Diferente dos prompts de texto discretos, esses soft prompts são aprendidos através de retropropagação enquanto o modelo de linguagem permanece congelado. O método foi introduzido em ["The Power of Scale for Parameter-Efficient Prompt Tuning"](https://arxiv.org/abs/2104.08691) (Lester et al., 2021), que demonstrou que o ajuste de prompts se torna mais competitivo em relação ao ajuste fino conforme o tamanho do modelo aumenta. No artigo, em torno de 10 bilhões de parâmetros, o ajuste de prompts iguala o desempenho do ajuste fino, modificando apenas algumas centenas de parâmetros por tarefa.
-
-Esses soft prompts são vetores contínuos no espaço de embedding do modelo que são otimizados durante o treinamento. Diferente dos prompts discretos tradicionais que usam tokens de linguagem natural, os soft prompts não possuem significado inerente, mas aprendem a evocar o comportamento desejado do modelo congelado por meio de gradiente descendente. A técnica é particularmente eficaz em cenários multitarefa, pois cada tarefa exige apenas o armazenamento de um pequeno vetor de prompt (normalmente algumas centenas de parâmetros) em vez de uma cópia completa do modelo. Essa abordagem não apenas mantém um uso mínimo de memória, mas também possibilita a troca rápida de tarefas apenas trocando os vetores de prompt sem precisar recarregar o modelo.
-
-## Processo de Treinamento
-
-Os soft prompts geralmente têm entre 8 e 32 tokens e podem ser inicializados aleatoriamente ou a partir de texto existente. O método de inicialização desempenha um papel crucial no processo de treinamento, com inicializações baseadas em texto frequentemente apresentando melhor desempenho do que inicializações aleatórias.
-
-Durante o treinamento, apenas os parâmetros do prompt são atualizados, enquanto o modelo base permanece congelado. Essa abordagem focada utiliza objetivos de treinamento padrão, mas exige atenção cuidadosa à taxa de aprendizado e ao comportamento do gradiente dos tokens do prompt.
-
-## Implementação com PEFT
-
-O módulo PEFT facilita a implementação do ajuste de prompts. Aqui está um exemplo básico:
-
-```python
-from peft import PromptTuningConfig, TaskType, get_peft_model
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-# Load base model
-model = AutoModelForCausalLM.from_pretrained("your-base-model")
-tokenizer = AutoTokenizer.from_pretrained("your-base-model")
-
-# Configure prompt tuning
-peft_config = PromptTuningConfig(
-    task_type=TaskType.CAUSAL_LM,
-    num_virtual_tokens=8,  # Number of trainable tokens
-    prompt_tuning_init="TEXT",  # Initialize from text
-    prompt_tuning_init_text="Classify if this text is positive or negative:",
-    tokenizer_name_or_path="your-base-model",
-)
-
-# Create prompt-tunable model
-model = get_peft_model(model, peft_config)
-```
-
-## Comparação com Outros Métodos
-
-Quando comparado a outras abordagens PEFT, o ajuste de prompts se destaca por sua eficiência. Enquanto o LoRA oferece baixo uso de parâmetros e memória, mas exige o carregamento de adaptadores para troca de tarefas, o ajuste de prompts atinge um uso de recursos ainda menor e possibilita a troca imediata de tarefas. O ajuste fino completo, em contraste, demanda recursos significativos e requer cópias separadas do modelo para diferentes tarefas.
-
-| Método | Parâmetros | Memória | Troca de Tarefas |
-|--------|------------|---------|----------------|
-| Ajuste de Prompts| Muito Baixo | Mínima | Fácil |
-| LoRA | Baixo | Baixa | Requer Carregamento |
-| Ajuste Fino | Alto | Alta | Nova Cópia do Modelo |
-
-Ao implementar o ajuste de prompts, comece com um pequeno número de tokens virtuais (8-16) e aumente apenas se a complexidade da tarefa exigir. A inicialização baseada em texto geralmente apresenta melhores resultados do que a inicialização aleatória, especialmente ao usar texto relevante para a tarefa. A estratégia de inicialização deve refletir a complexidade da tarefa alvo.
-
-O treinamento requer considerações ligeiramente diferentes do ajuste fino completo. Taxas de aprendizado mais altas geralmente funcionam bem, mas o monitoramento cuidadoso dos gradientes dos tokens do prompt é essencial. A validação regular com exemplos diversos ajuda a garantir um desempenho robusto em diferentes cenários.
-
-## Aplicação
-
-O ajuste de prompts se destaca em diversos cenários:
-
-1. Implantação multitarefa
-2. Ambientes com restrição de recursos
-3. Adaptação rápida a tarefas
-4. Aplicações sensíveis à privacidade
-
-Conforme os modelos ficam menores, o ajuste de prompts se torna menos competitivo em comparação ao ajuste fino completo. Por exemplo, em modelos como SmolLM2, o ajuste de prompts é menos relevante do que o ajuste fino completo. 
-
-## Próximos Passos
-
-⏭️ Prossiga para o [Tutorial de Adaptadores LoRA](./notebooks/finetune_sft_peft.ipynb) para aprender como ajustar um modelo com adaptadores LoRA.
-
-## Referências
-- [Documentação PEFT](https://huggingface.co/docs/peft)
-- [Artigo sobre Ajuste de Prompts](https://arxiv.org/abs/2104.08691)
-- [Cookbook do Hugging Face](https://huggingface.co/learn/cookbook/prompt_tuning_peft)
diff --git a/pt-br/4_evaluation/README.md b/pt-br/4_evaluation/README.md
deleted file mode 100644
index a30d76ef..00000000
--- a/pt-br/4_evaluation/README.md
+++ /dev/null
@@ -1,40 +0,0 @@
-# Evaluation (Avaliação)
-
-A avaliação é uma etapa crítica no desenvolvimento e implantação de modelos de linguagem. Ela nos ajuda a entender quão bem nossos modelos desempenham em diferentes capacidades e a identificar áreas para melhorias. Este módulo aborda benchmarks padrão e abordagens de avaliação específicas de domínio para avaliar de forma abrangente o seu modelo smol (miudinho).
-
-Usaremos o [`lighteval`](https://github.com/huggingface/lighteval), uma poderosa biblioteca de avaliação desenvolvida pelo Hugging Face que se integra perfeitamente ao ecossistema Hugging Face. Para um aprendizado mais profundo nos conceitos e práticas recomendadas de avaliação, confira o [guia](https://github.com/huggingface/evaluation-guidebook).
-
-## Visão Geral do Módulo 
-
-Uma estratégia de avaliação completa examina múltiplos aspectos do desempenho do modelo. Avaliamos capacidades específicas de tarefas, como responder a perguntas e sumarização, para entender como o modelo lida com diferentes tipos de problemas. Medimos a qualidade do output através de fatores como coerência e precisão factual. A avaliação de segurança ajuda a identificar outputs potencialmente prejudiciais ou biases. Por fim, os testes de especialização de domínio verificam o conhecimento especializado do modelo no campo-alvo.
-
-## Conteúdo
-
-### 1️⃣ [Benchmarks Automáticos](./automatic_benchmarks.md)
-
-Aprenda a avaliar seu modelo usando benchmarks e métricas padronizados. Exploraremos benchmarks comuns, como MMLU e TruthfulQA, entenderemos as principais métricas e configurações de avaliação e abordaremos as melhores práticas para avaliações reproduzíveis.
-
-### 2️⃣ [Avaliação de Domnínio Personalizado](./custom_evaluation.md)
-
-Descubra como criar pipelines de avaliação adaptados ao seu caso de uso específico. Veremos o design de tarefas de avaliação personalizadas, implementação de métricas especializadas e construção de conjuntos de dados de avaliação que atendam às suas necessidades.
-
-### 3️⃣ [Projeto de Avaliação de Domínio](./project/README.md)
-
-Siga um exemplo completo de construção de um pipeline de avaliação de domínio específico. Você aprenderá a gerar conjuntos de dados de avaliação, usar o Argilla para anotação de dados, criar conjuntos de dados padronizados e avaliar modelos usando o LightEval.
-
-### Cadernos de Exercício
-
-| Título | Descrição | Exercício | Link | Colab |
-|-------|-------------|----------|------|-------|
-| Avalie e Analise Seu LLM | Aprenda a usar o LightEval para avaliar e comparar modelos em domínios específicos | 🐢 Use tarefas do domínio da medicina para avaliar um modelo <br> 🐕 Crie uma nova avaliação de domínio com diferentes tarefas do MMLU <br> 🦁 Crie uma tarefa de avaliação personalizada para o seu domínio | [Notebook](./notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-
-
-## Referências
-
-- [Guia de Avaliação](https://github.com/huggingface/evaluation-guidebook) - Guia abrangente de avaliação de LLMs
-- [Documentação do LightEval](https://github.com/huggingface/lighteval) - Documentação oficial do módulo LightEval
-- [Documentação do Argilla](https://docs.argilla.io) - Saiba mais sobre a plataforma de anotação Argilla
-- [Artigo do MMLU](https://arxiv.org/abs/2009.03300) - Artigo que descreve o benchmark MMLU
-- [Criando uma Tarefa Personalizada](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
-- [Criando uma Métrica Personalizada](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
-- [Usando Métricas Existentes](https://github.com/huggingface/lighteval/wiki/Metric-List)
\ No newline at end of file
diff --git a/pt-br/4_evaluation/automatic_benchmarks.md b/pt-br/4_evaluation/automatic_benchmarks.md
deleted file mode 100644
index 0c2cc55d..00000000
--- a/pt-br/4_evaluation/automatic_benchmarks.md
+++ /dev/null
@@ -1,137 +0,0 @@
-# Benchmarks Automáticos
-
-Os benchmarks automáticos servem como ferramentas padronizadas para avaliar modelos de linguagem em diferentes tarefas e capacidades. Embora forneçam um ponto de partida útil para entender o desempenho do modelo, é importante reconhecer que eles representam apenas uma parte de uma estratégia abrangente de avaliação.
-
-## Entendendo os Benchmarks Automáticos
-
-Os benchmarks automáticos geralmente consistem em conjuntos de dados organizados com tarefas e métricas de avaliação predefinidas. Esses benchmarks têm como objetivo avaliar vários aspectos da capacidade do modelo, desde compreensão básica de linguagem até raciocínio complexo. A principal vantagem de usar benchmarks automáticos é a padronização, eles permitem comparações consistentes entre diferentes modelos e fornecem resultados reproduzíveis.
-
-No entanto, é crucial entender que o desempenho em benchmarks nem sempre se traduz diretamente em eficácia no mundo real. Um modelo que se destaca em benchmarks acadêmicos pode ainda enfrentar dificuldades em aplicações específicas de domínio ou casos práticos.
-
-## Benchmarks e Suas Limitações
-
-### Benchmarks de Conhecimento Geral
-
-O MMLU (Massive Multitask Language Understanding) testa conhecimentos em 57 disciplinas, de ciências a humanidades. Embora abrangente, este benchmark pode não refletir a profundidade de especialização necessária para domínios específicos. O TruthfulQA avalia a tendência de um modelo em reproduzir conceitos equivocados comuns, embora não capture todas as formas de desinformação.
-
-### Benchmarks de Raciocínio
-
-BBH (Big Bench Hard) e GSM8K focam em tarefas de raciocínio complexo. O BBH testa pensamento lógico e planejamento, enquanto o GSM8K visa especificamente a resolução de problemas matemáticos. Esses benchmarks ajudam a avaliar capacidades analíticas, mas podem não captar o raciocínio detalhado exigido em cenários do mundo real.
-
-### Compreensão de Linguagem
-
-HELM fornece uma estrutura de avaliação holística, enquanto o WinoGrande testa o senso comum através da desambiguação de pronomes. Esses benchmarks oferecem insights sobre capacidades de processamento de linguagem, mas podem não representar totalmente a complexidade da conversação natural ou terminologia específica de domínio.
-
-## Abordagens Alternativas de Avaliação
-
-Muitas organizações desenvolveram métodos alternativos de avaliação para lidar com as limitações dos benchmarks padrão:
-
-### LLM como Juiz
-
-Usar um modelo de linguagem para avaliar os outputs de outro tornou-se cada vez mais popular. Essa abordagem pode fornecer feedback mais detalhado do que métricas tradicionais, embora apresente seus próprios vieses e limitações.
-
-### Arenas de Avaliação
-
-Plataformas como a IA Arena Constitucional da Anthropic permitem que modelos interajam e avaliem uns aos outros em ambientes controlados. Isso pode revelar pontos fortes e fracos que podem não ser evidentes em benchmarks tradicionais.
-
-### Conjuntos de Benchmarks Personalizados
-
-As organizações frequentemente desenvolvem conjuntos de benchmarks internos adaptados às suas necessidades e casos de uso específicos. Esses conjuntos podem incluir testes de conhecimento de domínio ou cenários de avaliação que refletem condições reais de implantação.
-
-## Criando Sua Própria Estratégia de Avaliação
-
-Embora o LightEval facilite a execução de benchmarks padrão, você também deve investir tempo no desenvolvimento de métodos de avaliação específicos para o seu caso de uso.
-
-Sabendo que benchmarks padrão fornecem uma linha de base útil, eles não devem ser seu único método de avaliação. Aqui está como desenvolver uma abordagem mais abrangente:
-
-1. Comece com benchmarks padrão relevantes para estabelecer uma linha de base e permitir comparações com outros modelos.
-
-2. Identifique os requisitos específicos e desafios do seu caso de uso. Quais tarefas seu modelo realmente executará? Que tipos de erros seriam mais problemáticos?
-
-3. Desenvolva conjuntos de dados de avaliação personalizados que reflitam seu caso de uso real. Isso pode incluir:
-    - Consultas reais de usuários do seu domínio
-    - Casos extremos comuns que você encontrou
-    - Exemplos de cenários particularmente desafiadores
-
-4. Considere implementar uma estratégia de avaliação em camadas:
-    - Métricas automatizadas para feedback rápido
-    - Avaliação humana para entendimento mais detalhado
-    - Revisão por especialistas no domínio para aplicações especializadas
-    - Testes A/B em ambientes controlados
-
-## Usando LightEval para Benchmarks
-
-As tarefas do LightEval são definidas usando um formato específico:
-
-```
-{suite}|{task}|{num_few_shot}|{auto_reduce}
-```
-
-- **suite**: O conjunto de benchmarks (ex.: 'mmlu', 'truthfulqa')
-- **task**: Tarefa específica dentro do conjunto (ex.: 'abstract_algebra')
-- **num_few_shot**: Número de exemplos a incluir no prompt (0 para zero-shot)
-- **auto_reduce**: Se deve reduzir automaticamente exemplos few-shot caso o prompt seja muito longo (0 ou 1)
-
-Exemplo: `"mmlu|abstract_algebra|0|0"` avalia a tarefa de álgebra abstrata do MMLU com inferência zero-shot.
-
-### Exemplo de Pipeline de Avaliação
-
-Aqui está um exemplo completo de configuração e execução de uma avaliação em benchmarks automáticos relevantes para um domínio específico:
-
-```python
-from lighteval.tasks import Task, Pipeline
-from transformers import AutoModelForCausalLM
-
-# Define tasks to evaluate
-domain_tasks = [
-    "mmlu|anatomy|0|0",
-    "mmlu|high_school_biology|0|0", 
-    "mmlu|high_school_chemistry|0|0",
-    "mmlu|professional_medicine|0|0"
-]
-
-# Configure pipeline parameters
-pipeline_params = {
-    "max_samples": 40,  # Number of samples to evaluate
-    "batch_size": 1,    # Batch size for inference
-    "num_workers": 4    # Number of worker processes
-}
-
-# Create evaluation tracker
-evaluation_tracker = EvaluationTracker(
-    output_path="./results",
-    save_generations=True
-)
-
-# Load model and create pipeline
-model = AutoModelForCausalLM.from_pretrained("your-model-name")
-pipeline = Pipeline(
-    tasks=domain_tasks,
-    pipeline_parameters=pipeline_params,
-    evaluation_tracker=evaluation_tracker,
-    model=model
-)
-
-# Run evaluation
-pipeline.evaluate()
-
-# Get and display results
-results = pipeline.get_results()
-pipeline.show_results()
-```
-
-Os resultados são exibidos em formato tabular, mostrando:
-```
-|                  Task                  |Version|Metric|Value |   |Stderr|
-|----------------------------------------|------:|------|-----:|---|-----:|
-|all                                     |       |acc   |0.3333|±  |0.1169|
-|leaderboard:mmlu:_average:5             |       |acc   |0.3400|±  |0.1121|
-|leaderboard:mmlu:anatomy:5              |      0|acc   |0.4500|±  |0.1141|
-|leaderboard:mmlu:high_school_biology:5  |      0|acc   |0.1500|±  |0.0819|
-```
-
-Você também pode manipular os resultados em um DataFrame do pandas e visualizá-los conforme necessário.
-
-# Próximos Passos
-
-⏩ Veja a [Avaliação Personalizada de Domínio](./custom_evaluation.md) para aprender a criar pipelines de avaliação adaptados às suas necessidades específicas.
diff --git a/pt-br/4_evaluation/custom_evaluation.md b/pt-br/4_evaluation/custom_evaluation.md
deleted file mode 100644
index 1b7bdc40..00000000
--- a/pt-br/4_evaluation/custom_evaluation.md
+++ /dev/null
@@ -1,132 +0,0 @@
-# Avaliação de Domínio Personalizado
-
-Embora benchmarks padrão ofereçam insights valiosos, muitas aplicações requerem abordagens de avaliação especializadas adaptadas a domínios ou casos de uso específicos. Este guia ajudará você a criar pipelines de avaliação personalizados que avaliem com precisão o desempenho do seu modelo no domínio-alvo.
-
-## Planejando Sua Estratégia de Avaliação
-
-Uma estratégia de avaliação personalizada bem-sucedida começa com objetivos claros. Considere quais capacidades específicas seu modelo precisa demonstrar no seu domínio. Isso pode incluir conhecimento técnico, padrões de raciocínio ou formatos específicos de domínio. Documente esses requisitos cuidadosamente, eles guiarão tanto o design da tarefa quanto a seleção de métricas.
-
-Sua avaliação deve testar tanto casos de uso padrão quanto casos extremos. Por exemplo, em um domínio de medicina, você pode avaliar cenários com diagnósticos comuns e condições raras. Em aplicações financeiras, pode-se testar transações rotineiras e casos extremos envolvendo múltiplas moedas ou condições especiais.
-
-## Implementação com LightEval
-
-O LightEval fornece um framework flexível para implementar avaliações personalizadas. Veja como criar uma tarefa personalizada:
-
-```python
-from lighteval.tasks import Task, Doc
-from lighteval.metrics import SampleLevelMetric, MetricCategory, MetricUseCase
-
-class CustomEvalTask(Task):
-    def __init__(self):
-        super().__init__(
-            name="custom_task",
-            version="0.0.1",
-            metrics=["accuracy", "f1"],  # Your chosen metrics
-            description="Description of your custom evaluation task"
-        )
-    
-    def get_prompt(self, sample):
-        # Format your input into a prompt
-        return f"Question: {sample['question']}\nAnswer:"
-    
-    def process_response(self, response, ref):
-        # Process model output and compare to reference
-        return response.strip() == ref.strip()
-```
-
-## Métricas Personalizadas
-
-Tarefas específicas de domínio frequentemente exigem métricas especializadas. O LightEval fornece um framework flexível para criar métricas personalizadas que capturam aspectos relevantes do desempenho no domínio:
-
-```python
-from aenum import extend_enum
-from lighteval.metrics import Metrics, SampleLevelMetric, SampleLevelMetricGrouping
-import numpy as np
-
-# Define a sample-level metric function
-def custom_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> dict:
-    """Example metric that returns multiple scores per sample"""
-    response = predictions[0]
-    return {
-        "accuracy": response == formatted_doc.choices[formatted_doc.gold_index],
-        "length_match": len(response) == len(formatted_doc.reference)
-    }
-
-# Create a metric that returns multiple values per sample
-custom_metric_group = SampleLevelMetricGrouping(
-    metric_name=["accuracy", "length_match"],  # Names of sub-metrics
-    higher_is_better={  # Whether higher values are better for each metric
-        "accuracy": True,
-        "length_match": True
-    },
-    category=MetricCategory.CUSTOM,
-    use_case=MetricUseCase.SCORING,
-    sample_level_fn=custom_metric,
-    corpus_level_fn={  # How to aggregate each metric
-        "accuracy": np.mean,
-        "length_match": np.mean
-    }
-)
-
-# Register the metric with LightEval
-extend_enum(Metrics, "custom_metric_name", custom_metric_group)
-```
-
-Para casos mais simples onde você precisa de apenas um valor por amostra:
-
-```python
-def simple_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> bool:
-    """Example metric that returns a single score per sample"""
-    response = predictions[0]
-    return response == formatted_doc.choices[formatted_doc.gold_index]
-
-simple_metric_obj = SampleLevelMetric(
-    metric_name="simple_accuracy",
-    higher_is_better=True,
-    category=MetricCategory.CUSTOM,
-    use_case=MetricUseCase.SCORING,
-    sample_level_fn=simple_metric,
-    corpus_level_fn=np.mean  # How to aggregate across samples
-)
-
-extend_enum(Metrics, "simple_metric", simple_metric_obj)
-```
-
-Você pode então usar suas métricas personalizadas em tarefas de avaliação referenciando-as na configuração da tarefa. As métricas serão automaticamente calculadas para todas as amostras e agregadas de acordo com as funções especificadas.
-
-Para métricas mais complexas, considere:
-- Usar metadados em seus documentos formatados para ponderar ou ajustar pontuações
-- Implementar funções de agregação personalizadas para estatísticas em nível de corpus
-- Adicionar verificações de validação para as entradas da métrica
-- Documentar casos extremos e comportamentos esperados
-
-Para um exemplo completo de métricas personalizadas em ação, veja nosso [projeto de avaliação de domínio](./project/README.md).
-
-## Criação de Conjuntos de Dados
-
-Uma avaliação de alta qualidade requer conjuntos de dados cuidadosamente selecionados. Considere estas abordagens para criação de conjuntos de dados:
-
-1. Anotação Avançada: Trabalhe com especialistas no domínio para criar e validar exemplos de avaliação. Ferramentas como o [Argilla](https://github.com/argilla-io/argilla) tornam esse processo mais eficiente.
-
-2. Dados do Mundo Real: Colete e anonimize dados de uso real, garantindo que representem cenários reais de implantação.
-
-3. Geração Sintética: Use LLMs para gerar exemplos iniciais e, em seguida, peça para especialistas validarem e refinarem esses exemplos.
-
-## Melhores Práticas
-
-- Documente sua metodologia de avaliação detalhadamente, incluindo quaisquer suposições ou limitações
-- Inclua casos de teste diversificados que cubram diferentes aspectos do seu domínio
-- Considere métricas automatizadas e avaliação humana, quando apropriado
-- Faça o controle de versão dos seus conjuntos de dados e código de avaliação
-- Atualize regularmente seu conjunto de avaliação à medida que descobrir novos casos extremos ou requisitos
-
-## Referências
-
-- [Guia de Tarefas Personalizadas do LightEval](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
-- [Métricas Personalizadas do LightEval](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
-- [Documentação do Argilla](https://docs.argilla.io) para anotação de conjuntos de dados
-- [Guia de Avaliação](https://github.com/huggingface/evaluation-guidebook) para princípios gerais de avaliação
-
-# Próximos Passos
-
-⏩ Para um exemplo completo de implementação desses conceitos, veja nosso [projeto de avaliação de domínio](./project/README.md).
\ No newline at end of file
diff --git a/pt-br/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb b/pt-br/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
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--- a/pt-br/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
+++ /dev/null
@@ -1,19422 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TZVw9f5QYWPL"
-      },
-      "source": [
-        "# lighteval é o seu módulo de avaliação de IA\n",
-        "\n",
-        "Este caderno explora como você pode usar o lighteval para avaliar e comparar LLMs.\n",
-        "\n",
-        "O `lighteval` já existe há algum tempo e é uma ótima ferramenta para obter pontuações de avaliação em benchmarks importantes. Ele foi recentemente reformulado para ser usado como uma biblioteca em Python, o que o torna excelente para comparar modelos em diferentes benchmarks.\n",
-        "\n",
-        "Então, vamos mergulhar nas pontuações de avaliação.\n",
-        "\n",
-        "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-        "    <h2 style='margin: 0;color:blue'>Exercício: Avalie Seu Próprio Modelo</h2>\n",
-        "    <p>Agora que você viu como avaliar modelos em domínios específicos, experimente avaliar um modelo de um domínio que lhe interessa.</p>\n",
-        "    <p><b>Níveis de Dificuldade</b></p>\n",
-        "    <p>🐢 Use as tarefas existentes do domínio médico, mas avalie um modelo diferente do hub do Hugging Face</p>\n",
-        "    <p>🐕 Crie uma nova avaliação de domínio selecionando diferentes tarefas do MMLU (por exemplo, ciência da computação, matemática, física)</p>\n",
-        "    <p>🦁 Crie uma tarefa de avaliação personalizada usando o framework de tarefas do LightEval e avalie modelos no seu domínio específico</p>\n",
-        "</div>\n",
-        "\n",
-        "## Instalar dependências"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "afW_CLJoPCnF",
-        "outputId": "91611cd5-51d6-4f73-9b3d-ac79c918d074"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install -qqq -U \"torch<2.5\" \"torchvision<2.5\" \"torchaudio<2.5\" --index-url https://download.pytorch.org/whl/cu121\n",
-        "!pip list | grep torch"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "8xMnn_cQ1EEi",
-        "outputId": "bcaf8498-19c3-46af-9ae1-0306326f006b"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install -qqq git+https://github.com/huggingface/lighteval.git tiktoken"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TDKs5ShvXw8K"
-      },
-      "source": [
-        "## Configurar a avaliação com `lighteval`\n",
-        "\n",
-        "Precisamos configurar o ambiente e o pipeline de avaliação. Muito disso será desativado porque estamos mantendo as coisas no notebook, mas também podemos usar `push_to_hub` ou `push_to_tensorboard`.\n",
-        "\n",
-        "### `push_to_hub`\n",
-        "\n",
-        "Isso é útil se estivermos avaliando um modelo e quisermos persistir sua avaliação com pesos e configuração no hub do Hugging Face.\n",
-        "\n",
-        "### `push_to_tensorboard`\n",
-        "\n",
-        "Isso é útil se estivermos criando uma ferramenta ou script de avaliação, onde queremos visualizar os resultados no TensorBoard.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 296,
-          "referenced_widgets": [
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-          ]
-        },
-        "id": "3cUebd-z6IWs",
-        "outputId": "4b5ef2ce-04a0-4b2a-f3d7-5ecb7afb662e"
-      },
-      "outputs": [],
-      "source": [
-        "import lighteval\n",
-        "import os\n",
-        "from datetime import timedelta\n",
-        "from transformers import AutoModelForCausalLM\n",
-        "\n",
-        "from lighteval.logging.evaluation_tracker import EvaluationTracker\n",
-        "from lighteval.logging.hierarchical_logger import hlog_warn, htrack\n",
-        "from lighteval.models.model_config import create_model_config\n",
-        "from lighteval.pipeline import EnvConfig, ParallelismManager, Pipeline, PipelineParameters\n",
-        "\n",
-        "TOKEN = os.getenv(\"HF_TOKEN\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "muikmXNQXgFv"
-      },
-      "outputs": [],
-      "source": [
-        "env_config = EnvConfig(token=TOKEN, cache_dir=\"~/tmp\")\n",
-        "\n",
-        "evaluation_tracker = EvaluationTracker(\n",
-        "    output_dir=\"~/tmp\",\n",
-        "    save_details=False,\n",
-        "    push_to_hub=False,\n",
-        "    push_to_tensorboard=False,\n",
-        "    public=False,\n",
-        "    hub_results_org=False,\n",
-        ")\n",
-        "\n",
-        "pipeline_params = PipelineParameters(\n",
-        "    launcher_type=ParallelismManager.ACCELERATE,\n",
-        "    env_config=env_config,\n",
-        "    job_id=1,\n",
-        "    override_batch_size=1,\n",
-        "    num_fewshot_seeds=0,\n",
-        "    max_samples=10,\n",
-        "    use_chat_template=False,\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "nsNjwzCtltkA"
-      },
-      "source": [
-        "# Comparar modelos com `lighteval`\n",
-        "\n",
-        "Vamos comparar dois pequenos LLMs em um domínio. Vamos usar `Qwen2.5-0.5B` e `SmolLM2-360M-Instruct` e vamos avaliá-los no domínio médico.\n",
-        "\n",
-        "Podemos criar uma avaliação de domínio a partir de um subconjunto de avaliações do MMLU, definindo as tarefas de avaliação. No lighteval, as tarefas são descritas como strings.\n",
-        "\n",
-        "`{suite}|{task}:{subtask}|{num_few_shot}|{0 ou 1 para reduzir num_few_shot se o prompt for muito longo}`\n",
-        "\n",
-        "Portanto, passaremos nossa lista de tarefas relacionadas à medicina desta forma:\n",
-        "\n",
-        "```\n",
-        "\"leaderboard|mmlu:anatomy|5|0,leaderboard|mmlu:professional_medicine|5|0,leaderboard|mmlu:high_school_biology|5|0,leaderboard|mmlu:high_school_chemistry|5|0\"\n",
-        "```\n",
-        "\n",
-        "Que pode ser traduzido para:\n",
-        "\n",
-        "| Suite | Task | Num Fewshot Example | Limit Fewshots |\n",
-        "|---|---|---|---|\n",
-        "| leaderboard | mmlu:anatomy | 5 | False |\n",
-        "| leaderboard | mmlu:professional_medicine | 5 | False |\n",
-        "| leaderboard | mmlu:high_school_biology | 5 | False |\n",
-        "| leaderboard | mmlu:high_school_chemistry | 5 | False |\n",
-        "\n",
-        "Para uma lista completa das tarefas suportadas pelo lighteval, confira esta página [na documentação](https://github.com/huggingface/lighteval/wiki/Available-Tasks)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "qTqsizv9mVbO"
-      },
-      "outputs": [],
-      "source": [
-        "domain_tasks = \"leaderboard|mmlu:anatomy|5|0,leaderboard|mmlu:professional_medicine|5|0,leaderboard|mmlu:high_school_biology|5|0,leaderboard|mmlu:high_school_chemistry|5|0\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "XwcJklSFX4H6"
-      },
-      "source": [
-        "# Avaliar Qwen2.5 0.5B"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
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-        "colab": {
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-          ]
-        },
-        "id": "PXJuaXVxUNBO",
-        "outputId": "87aca361-5bd5-4b12-9097-3e97f1165d7b"
-      },
-      "outputs": [],
-      "source": [
-        "qwen_model = AutoModelForCausalLM.from_pretrained(\"Qwen/Qwen2.5-0.5B\")\n",
-        "\n",
-        "pipeline = Pipeline(\n",
-        "    tasks=domain_tasks,\n",
-        "    pipeline_parameters=pipeline_params,\n",
-        "    evaluation_tracker=evaluation_tracker,\n",
-        "    model=qwen_model\n",
-        ")\n",
-        "\n",
-        "pipeline.evaluate()\n",
-        "\n",
-        "qwen_results = pipeline.get_results()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "CIwCaCxJX_hA"
-      },
-      "source": [
-        "# Avaliar SmolLM 360M"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000,
-          "referenced_widgets": [
-            "ede652658bc34897a9afc6ac71cc3910",
-            "a80d51a7ba014574a71d0cf81e0543e4",
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-        "id": "Dxg0RtlNVT4y",
-        "outputId": "ee40393c-bf62-499d-d443-e96da72411e8"
-      },
-      "outputs": [],
-      "source": [
-        "smol_model = AutoModelForCausalLM.from_pretrained(\"HuggingFaceTB/SmolLM2-360M-Instruct\")\n",
-        "\n",
-        "pipeline = Pipeline(\n",
-        "    tasks=domain_tasks,\n",
-        "    pipeline_parameters=pipeline_params,\n",
-        "    evaluation_tracker=evaluation_tracker,\n",
-        "    model=smol_model\n",
-        ")\n",
-        "\n",
-        "pipeline.evaluate()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "pdjyzfKHVt52"
-      },
-      "outputs": [],
-      "source": [
-        "smol_results = pipeline.get_results()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "eugvMFfgV1VD",
-        "outputId": "62956e3f-29c8-4493-f868-4ba63b7d8e96"
-      },
-      "outputs": [],
-      "source": [
-        "pipeline.show_results()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0HD8aFwSYGHu"
-      },
-      "source": [
-        "# Visualizar Resultados\n",
-        "\n",
-        "Agora que temos os resultados dos dois modelos, podemos visualizá-los lado a lado. Manteremos a visualização simples aqui, mas com essa estrutura de dados, você pode representar as pontuações de várias maneiras."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 447
-        },
-        "id": "1sReqrgQUO9r",
-        "outputId": "9a0779e1-c278-48ab-9ecc-5055028ea3fd"
-      },
-      "outputs": [],
-      "source": [
-        "import pandas as pd\n",
-        "\n",
-        "df = pd.DataFrame.from_records(smol_results[\"results\"]).T[\"acc\"].rename(\"SmolLM2-360M-Instruct\")\n",
-        "_df = pd.DataFrame.from_records(qwen_results[\"results\"]).T[\"acc\"].rename(\"Qwen2-0.5B-DPO\")\n",
-        "df = pd.concat([df, _df], axis=1)\n",
-        "df.plot(kind=\"barh\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "qJEbQeYDplKX"
-      },
-      "source": [
-        "# 💐 É isso!\n",
-        "\n",
-        "Você agora tem um caderno prático para visualizar as avaliações de modelos. Você pode usá-lo para:\n",
-        "\n",
-        "- Selecionar o modelo certo para o seu caso de uso em inferência\n",
-        "- Avaliar checkpoints durante o treinamento\n",
-        "- Compartilhar pontuações de modelos"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jWdS38syaipm"
-      },
-      "source": [
-        "🏃 Próximos Passos \n",
-        "\n",
-        "- Se você quiser aprofundar seus resultados de avaliação, confira este [caderno](https://github.com/huggingface/evaluation-guidebook/blob/main/contents/examples/comparing_task_formulations.ipynb)\n"
-      ]
-    }
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diff --git a/pt-br/4_evaluation/project/README.md b/pt-br/4_evaluation/project/README.md
deleted file mode 100644
index de40f0b9..00000000
--- a/pt-br/4_evaluation/project/README.md
+++ /dev/null
@@ -1,85 +0,0 @@
-# Avaliação Específica de Domínio com Argilla, Distilabel e LightEval
-
-Os benchmarks mais populares analisam capacidades muito gerais (raciocínio, matemática, código), mas você já precisou estudar sobre capacidades mais específicas? 
-
-O que fazer se você precisar avaliar um modelo em um **domínio personalizado** relevante para seus casos de uso? (Por exemplo aplicações financeiras, jurídicas ou médicas)
-
-Este tutorial mostra todo o pipeline que você pode seguir, desde a criação de dados relevantes e a anotação de suas amostras até a avaliação de seu modelo com elas, usando ferramentas como [Argilla](https://github.com/argilla-io/argilla), [distilabel](https://github.com/argilla-io/distilabel) e [lighteval](https://github.com/huggingface/lighteval). Para nosso exemplo, focaremos na geração de questões de exame a partir de múltiplos documentos.
-
-## Estrutura do Projeto
-
-Para o nosso processo, seguiremos 4 etapas, com um script para cada uma: gerar um conjunto de dados, anotar dados, extrair amostras relevantes para avaliação e, finalmente, avaliar os modelos.
-
-| Nome do Script | Descrição |
-|-------------|-------------|
-| generate_dataset.py | Gera questões de exame a partir de múltiplos documentos de texto usando um modelo de linguagem especificado.. |
-| annotate_dataset.py | Cria um conjunto de dados no Argilla para anotação manual das questões geradas. |
-| create_dataset.py | Processa os dados anotados no Argilla e cria um conjunto de dados no Hugging Face. |
-| evaluation_task.py | Define uma tarefa personalizada no LightEval para avaliar modelos de linguagem no conjunto de questões de exame. |
-
-## Etapas
-
-### 1. Gerar Conjunto de Dados
-
-O script `generate_dataset.py` usa o módulo distilabel para gerar questões de exame com base em múltiplos documentos de texto. Ele utiliza o modelo especificado (padrão: Meta-Llama-3.1-8B-Instruct) para criar perguntas, respostas corretas e respostas incorretas (chamadas de distrações). Você deve adicionar seus próprios exemplos de dados e talvez até usar um modelo diferente.
-
-Para executar a geração:
-
-```sh
-python generate_dataset.py --input_dir path/to/your/documents --model_id your_model_id --output_path output_directory
-```
-
-Isso criará um [Distiset](https://distilabel.argilla.io/dev/sections/how_to_guides/advanced/distiset/) contendo as questões de exame geradas para todos os documentos no diretório de entrada.
-
-### 2. Anotar Conjunto de Dados
-
-O script `annotate_dataset.py` utiliza as questões geradas e cria um conjunto de dados no Argilla para anotação. Ele configura a estrutura do conjunto de dados e o popula com as perguntas e respostas geradas, randomizando a ordem das respostas para evitar vieses (biases). No Argilla, você ou um especialista no domínio pode validar o conjunto de dados com as respostas corretas.
-
-Você verá as respostas corretas sugeridas pelo LLM em ordem aleatória e poderá aprovar a resposta correta ou selecionar outra. A duração desse processo dependerá da escala do seu conjunto de avaliação, da complexidade dos dados do domínio e da qualidade do seu LLM. Por exemplo, fomos capazes de criar 150 amostras em 1 hora no domínio de transferência de aprendizado, usando o Llama-3.1-70B-Instruct, aprovando principalmente as respostas corretas e descartando as incorretas
-
-Para executar o processo de anotação:
-
-```sh
-python annotate_dataset.py --dataset_path path/to/distiset --output_dataset_name argilla_dataset_name
-```
-
-Isso criará um conjunto de dados no Argilla que pode ser usado para revisão e anotação manual.
-
-![argilla_dataset](./images/domain_eval_argilla_view.png)
-
-Se você não estiver usando o Argilla, implante-o localmente ou no Spaces seguindo este [guia de início rápido](https://docs.argilla.io/latest/getting_started/quickstart/).
-
-### 3. Criar Conjunto de Dados
-
-O script `create_dataset.py` processa os dados anotados no Argilla e cria um conjunto de dados no Hugging Face. Ele manipula tanto as respostas sugeridas quanto as anotadas manualmente. O script criará um conjunto de dados contendo a pergunta, as possíveis respostas e o nome da coluna para a resposta correta. Para criar o conjunto de dados final:
-
-```sh
-huggingface_hub login
-python create_dataset.py --dataset_path argilla_dataset_name --dataset_repo_id your_hf_repo_id
-```
-
-Isso enviará o conjunto de dados para o Hugging Face Hub sob o repositório especificado. Você pode visualizar o conjunto de dados de exemplo no hub [aqui](https://huggingface.co/datasets/burtenshaw/exam_questions/viewer/default/train), e uma pré-visualização do conjunto de dados se parece com isto:
-
-![hf_dataset](./images/domain_eval_dataset_viewer.png)
-
-### 4. Tarefa de Avaliação
-
-O script `evaluation_task.py` define uma tarefa personalizada no LightEval para avaliar modelos de linguagem no conjunto de questões de exame. Ele inclui uma função de prompt, uma métrica de precisão personalizada e a configuração da tarefa. 
-
-Para avaliar um modelo usando o lighteval com a tarefa personalizada de questões de exame:
-
-```sh
-lighteval accelerate \
-    --model_args "pretrained=HuggingFaceH4/zephyr-7b-beta" \
-    --tasks "community|exam_questions|0|0" \
-    --custom_tasks domain-eval/evaluation_task.py \
-    --output_dir "./evals"
-```
-
-Você pode encontrar guias detalhados no wiki do lighteval sobre cada uma dessas etapas: 
-
-- [Criando uma Tarefa Personalizada](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
-- [Criando uma Métrica Personalizada](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
-- [Usando Métricas Existentes](https://github.com/huggingface/lighteval/wiki/Metric-List)
-
-
diff --git a/pt-br/4_evaluation/project/annotate_dataset.py b/pt-br/4_evaluation/project/annotate_dataset.py
deleted file mode 100644
index dadffc10..00000000
--- a/pt-br/4_evaluation/project/annotate_dataset.py
+++ /dev/null
@@ -1,129 +0,0 @@
-import argparse
-import json
-from random import choices, sample
-
-import argilla as rg
-from distilabel.distiset import Distiset
-
-################################################################################
-# Script Parameters
-################################################################################
-
-parser = argparse.ArgumentParser(
-    description="Annotate exam questions dataset using Argilla."
-)
-parser.add_argument(
-    "--argilla_api_key",
-    type=str,
-    default="argilla.apikey",
-    help="API key for Argilla",
-)
-parser.add_argument(
-    "--argilla_api_url",
-    type=str,
-    default="http://localhost:6900",
-    help="API URL for Argilla",
-)
-parser.add_argument(
-    "--dataset_path",
-    type=str,
-    default="exam_questions",
-    help="Path to the exam questions dataset",
-)
-parser.add_argument(
-    "--dataset_config",
-    type=str,
-    default="default",
-    help="Configuration of the dataset",
-)
-parser.add_argument(
-    "--dataset_split",
-    type=str,
-    default="train",
-    help="Split of the dataset to use",
-)
-parser.add_argument(
-    "--output_dataset_name",
-    type=str,
-    default="exam_questions",
-    help="Name of the output Argilla dataset",
-)
-
-args = parser.parse_args()
-
-################################################################################
-# Create Argilla dataset with the feedback task for validation
-################################################################################
-
-client = rg.Argilla(api_key=args.argilla_api_key, api_url=args.argilla_api_url)
-
-if client.datasets(args.output_dataset_name):
-    print(f"Deleting existing dataset '{args.output_dataset_name}'")
-    client.datasets(args.output_dataset_name).delete()
-
-settings = rg.Settings(
-    fields=[
-        rg.TextField("question"),
-        rg.TextField("answer_a"),
-        rg.TextField("answer_b"),
-        rg.TextField("answer_c"),
-        rg.TextField("answer_d"),
-    ],
-    questions=[
-        rg.LabelQuestion(
-            name="correct_answer",
-            labels=["answer_a", "answer_b", "answer_c", "answer_d"],
-        ),
-        rg.TextQuestion(
-            name="improved_question",
-            description="Could you improve the question?",
-        ),
-        rg.TextQuestion(
-            name="improved_answer",
-            description="Could you improve the best answer?",
-        ),
-    ],
-)
-
-dataset = rg.Dataset(settings=settings, name=args.output_dataset_name)
-dataset.create()
-
-################################################################################
-# Load the Distiset and process and add records to Argilla dataset
-# We will validate that questions appear in random order to avoid bias
-# but we will show correct answers in the Argilla UI as suggestions.
-################################################################################
-
-distiset = Distiset.load_from_disk(args.dataset_path)
-answer_names = ["answer_a", "answer_b", "answer_c", "answer_d"]
-dataset_records = []
-
-for exam in distiset[args.dataset_config][args.dataset_split]:
-    exam_json = json.loads(exam["generation"])["exam"]
-
-    for question in exam_json:
-        answer = question["answer"]
-        distractors = question["distractors"]
-        distractors = choices(distractors, k=3)
-        answers = distractors + [answer]
-        answers = sample(answers, len(answers))
-        suggestion_idx = answers.index(answer)
-        fields = dict(zip(answer_names, answers))
-        fields["question"] = question["question"]
-
-        record = rg.Record(
-            fields=fields,
-            suggestions=[
-                rg.Suggestion(
-                    question_name="correct_answer",
-                    value=answer_names[suggestion_idx],
-                )
-            ],
-        )
-        dataset_records.append(record)
-
-dataset.records.log(dataset_records)
-
-print(
-    f"Dataset '{args.output_dataset_name}' has been created and populated in Argilla."
-)
diff --git a/pt-br/4_evaluation/project/create_dataset.py b/pt-br/4_evaluation/project/create_dataset.py
deleted file mode 100644
index 822e268e..00000000
--- a/pt-br/4_evaluation/project/create_dataset.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import argparse
-
-import argilla as rg
-from datasets import Dataset
-
-################################################################################
-# Script Parameters
-################################################################################
-
-parser = argparse.ArgumentParser(
-    description="Create a Hugging Face dataset from annotated Argilla data."
-)
-parser.add_argument(
-    "--argilla_api_key",
-    type=str,
-    default="argilla.apikey",
-    help="API key for Argilla",
-)
-parser.add_argument(
-    "--argilla_api_url",
-    type=str,
-    default="http://localhost:6900",
-    help="API URL for Argilla",
-)
-parser.add_argument(
-    "--dataset_path",
-    type=str,
-    default="exam_questions",
-    help="Path to the Argilla dataset",
-)
-parser.add_argument(
-    "--dataset_repo_id",
-    type=str,
-    default="burtenshaw/exam_questions",
-    help="Hugging Face dataset repository ID",
-)
-
-args = parser.parse_args()
-
-################################################################################
-# Initialize Argilla client and load dataset
-################################################################################
-
-client = rg.Argilla(api_key=args.argilla_api_key, api_url=args.argilla_api_url)
-dataset = client.datasets(args.dataset_path)
-
-################################################################################
-# Process Argilla records
-################################################################################
-
-dataset_rows = []
-
-for record in dataset.records(with_suggestions=True, with_responses=True):
-    row = record.fields
-
-    if len(record.responses) == 0:
-        answer = record.suggestions["correct_answer"].value
-        row["correct_answer"] = answer
-    else:
-        for response in record.responses:
-            if response.question_name == "correct_answer":
-                row["correct_answer"] = response.value
-    dataset_rows.append(row)
-
-################################################################################
-# Create Hugging Face dataset and push to Hub
-################################################################################
-
-hf_dataset = Dataset.from_list(dataset_rows)
-hf_dataset.push_to_hub(repo_id=args.dataset_repo_id)
-
-print(f"Dataset has been successfully pushed to {args.dataset_repo_id}")
diff --git a/pt-br/4_evaluation/project/evaluation_task.py b/pt-br/4_evaluation/project/evaluation_task.py
deleted file mode 100644
index 397c75ad..00000000
--- a/pt-br/4_evaluation/project/evaluation_task.py
+++ /dev/null
@@ -1,86 +0,0 @@
-import numpy as np
-
-from lighteval.tasks.lighteval_task import LightevalTaskConfig
-from lighteval.tasks.requests import Doc
-from lighteval.metrics.utils.metric_utils import (
-    SampleLevelMetric,
-    MetricCategory,
-    MetricUseCase,
-)
-
-################################################################################
-# Define the prompt function based on the structure of the dataset
-################################################################################
-
-
-def prompt_fn(line, task_name: str = None):
-    """Converts a dataset line to a Doc object for evaluation."""
-    instruction = "Choose the correct answer for the following exam question:"
-    return Doc(
-        task_name=task_name,
-        query=f"{instruction} {line['question']}",
-        choices=[
-            f" {line['answer_a']}",
-            f" {line['answer_b']}",
-            f" {line['answer_c']}",
-            f" {line['answer_d']}",
-        ],
-        gold_index=["answer_a", "answer_b", "answer_c", "answer_d"].index(
-            line["correct_answer"]
-        ),
-        instruction=instruction,
-    )
-
-
-################################################################################
-# Define the custom metric based on guide here https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric
-# Or use an existing metric based on the guide here: https://github.com/huggingface/lighteval/wiki/Metric-List
-# Existing metrics can be imported from lighteval.metrics.metrics
-################################################################################
-
-
-def sample_level_fn(formatted_doc: Doc, **kwargs) -> bool:
-    response = np.argmin(kwargs["choices_logprob"])
-    return response == formatted_doc.gold_index
-
-
-custom_metric = SampleLevelMetric(
-    metric_name="exam_question_accuracy",
-    higher_is_better=True,
-    category=MetricCategory.MULTICHOICE,
-    use_case=MetricUseCase.NONE,
-    sample_level_fn=sample_level_fn,
-    corpus_level_fn=np.mean,
-)
-
-################################################################################
-# Define the task based on the prompt function and the custom metric
-# Based on the guide here: https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task
-################################################################################
-
-task = LightevalTaskConfig(
-    name="example",
-    prompt_function=prompt_fn,
-    suite=["community"],
-    hf_repo="burtenshaw/exam_questions",
-    hf_subset="default",
-    hf_avail_splits=["train"],
-    evaluation_splits=["train"],
-    few_shots_split=None,
-    few_shots_select=None,
-    metric=[custom_metric],
-)
-
-# Add the task to TASKS_TABLE
-TASKS_TABLE = [task]
-
-# MODULE LOGIC
-if __name__ == "__main__":
-    print([t.name for t in TASKS_TABLE])
-    print(len(TASKS_TABLE))
-
-# lighteval accelerate \
-# "pretrained=HuggingFaceTB/SmolLM2-135M-Instruct" \
-# "community|example|0|0" \
-# --custom-tasks "submitted_tasks/example.py" \
-# --output-dir "results"
diff --git a/pt-br/4_evaluation/project/generate_dataset.py b/pt-br/4_evaluation/project/generate_dataset.py
deleted file mode 100644
index cb531d3f..00000000
--- a/pt-br/4_evaluation/project/generate_dataset.py
+++ /dev/null
@@ -1,167 +0,0 @@
-import argparse
-import os
-from pydantic import BaseModel, Field
-from datasets import Dataset
-
-from distilabel.llms import InferenceEndpointsLLM
-from distilabel.pipeline import Pipeline
-from distilabel.steps.tasks import TextGeneration
-
-
-################################################################################
-# Script Parameters
-################################################################################
-
-parser = argparse.ArgumentParser(
-    description="Generate exam questions from text files in a directory."
-)
-parser.add_argument(
-    "--model_id",
-    type=str,
-    default="Qwen/Qwen2.5-7B-Instruct",
-    help="Model ID for text generation",
-)
-parser.add_argument(
-    "--tokenizer_id",
-    type=str,
-    default="Qwen/Qwen2.5-7B-Instruct",
-    help="Tokenizer ID for text generation",
-)
-parser.add_argument(
-    "--input_dir",
-    type=str,
-    help="Directory containing input text files",
-    default="data",
-)
-parser.add_argument(
-    "--max_new_tokens",
-    type=int,
-    default=2048,
-    help="Maximum number of new tokens to generate",
-)
-parser.add_argument(
-    "--output_path",
-    type=str,
-    default="exam_questions_output",
-    help="Directory to save the generated datasets",
-)
-
-args = parser.parse_args()
-
-################################################################################
-# Load the documents
-# We assume that the documents are in the input directory, and that each file
-# is a separate document about the same topic.
-################################################################################
-
-# Process all text files in the input directory
-documents = []
-for filename in os.listdir(args.input_dir):
-    if filename.endswith(".txt"):
-        file_path = os.path.join(args.input_dir, filename)
-        with open(file=file_path, mode="r", encoding="utf-8") as file:
-            document_content = file.read()
-            documents.append(document_content)
-
-# Create a single dataset from all document contents
-dataset = Dataset.from_dict({"document": documents})
-
-################################################################################
-# Define the prompts
-# We use a system prompt to guide the model to generate the correct output format.
-# A template is used to insert the document into the prompt.
-################################################################################
-
-SYSTEM_PROMPT = """\
-You are an exam writer specialized in writing exams for students.
-Your goal is to create questions and answers based on the document provided, 
-and a list of distractors, that are incorrect but viable answers to the question.
-Your answer must adhere to the following format:
-```
-[
-    {
-        "question": "Your question",
-        "answer": "The correct answer to the question",
-        "distractors": ["wrong answer 1", "wrong answer 2", "wrong answer 3"]
-    },
-    ... (more questions and answers as required)
-]
-```
-""".strip()
-
-INSTRUCTION_TEMPLATE = """\
-    Generate a list of answers and questions about the document. 
-    Document:\n\n{{ instruction }}"""
-
-################################################################################
-# Define the output structure
-# We define a data model for the output of the pipeline, this is used to ensure
-# that the output is in the correct format for the evaluation task.
-################################################################################
-
-
-class ExamQuestion(BaseModel):
-    question: str = Field(..., description="The question to be answered")
-    answer: str = Field(..., description="The correct answer to the question")
-    distractors: List[str] = Field(
-        ..., description="A list of incorrect but viable answers to the question"
-    )
-
-
-class ExamQuestions(BaseModel):
-    exam: List[ExamQuestion]
-
-
-################################################################################
-# Create the pipeline
-# We create a pipeline with a single task that generates the exam questions
-# based on the document and in the correct format. We will Hugging Face
-# InferenceEndpoints and the model specified in the arguments.
-################################################################################
-
-with Pipeline(
-    name="Domain-Eval-Questions",
-    description="Generate exam questions based on given documents.",
-) as pipeline:
-    # Set up the text generation task
-    text_generation = TextGeneration(
-        name="exam_generation",
-        llm=InferenceEndpointsLLM(
-            model_id=args.model_id,
-            tokenizer_id=args.model_id,
-            api_key=os.environ["HF_TOKEN"],
-            structured_output={
-                "schema": ExamQuestions.model_json_schema(),
-                "format": "json",
-            },
-        ),
-        input_batch_size=8,
-        output_mappings={"model_name": "generation_model"},
-        input_mappings={"instruction": "document"},
-        system_prompt=SYSTEM_PROMPT,
-        template=INSTRUCTION_TEMPLATE,
-    )
-
-
-################################################################################
-# Run the pipeline
-# We run the pipeline for all documents and save the results to the output path.
-################################################################################
-
-if __name__ == "__main__":
-    # Run the pipeline for all documents
-    distiset = pipeline.run(
-        parameters={
-            "exam_generation": {
-                "llm": {
-                    "generation_kwargs": {
-                        "max_new_tokens": args.max_new_tokens,
-                    }
-                }
-            }
-        },
-        use_cache=False,
-        dataset=dataset,
-    )
-
-    distiset.save_to_disk(args.output_path)
diff --git a/pt-br/4_evaluation/project/images/domain_eval_argilla_view.png b/pt-br/4_evaluation/project/images/domain_eval_argilla_view.png
deleted file mode 100644
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diff --git a/pt-br/README.md b/pt-br/README.md
deleted file mode 100644
index ecaf82ac..00000000
--- a/pt-br/README.md
+++ /dev/null
@@ -1,94 +0,0 @@
-![smolcourse image](../banner.png)
-
-# a smol course (um curso miudinho)
-
-Este é um curso prático sobre alinhar modelos de linguagem para o seu caso de uso específico. É uma maneira útil de começar a alinhar modelos de linguagem, porque tudo funciona na maioria das máquinas locais. Existem requisitos mínimos de GPU e nenhum serviço pago. O curso é baseado na série de modelos de [SmolLM2](https://github.com/huggingface/smollm/tree/main), mas você pode transferir as habilidades que aprende aqui para modelos maiores ou outros pequenos modelos de linguagem.
-
-<a href="http://hf.co/join/discord">
-<img src="https://img.shields.io/badge/Discord-7289DA?&logo=discord&logoColor=white"/>
-</a>
-
-<div style="background: linear-gradient(to right, #e0f7fa, #e1bee7, orange); padding: 20px; border-radius: 5px; margin-bottom: 20px; color: purple;">
-    <h2>A participação é aberta a todos, gratuita e já está disponível!</h2>
-    <p>Este curso é aberto e avaliado por pares (peer reviewed). Para começar o curso, <strong>abra um pull request (PR)</strong> e envie seu trabalho para revisão. Aqui estão as etapas:</p>
-    <ol>
-        <li>Dê um fork no repositório <a href="https://github.com/huggingface/smol-course/fork">aqui</a></li>
-        <li>Leia o material, faça alterações, faça os exercícios, adicione seus próprios exemplos</li>
-        <li>Abra um PR no branch december_2024</li>
-        <li>Tenha seu material revisado e mesclado no branch principal</li>
-    </ol>
-    <p>Isso deve te ajudar a aprender e a construir um curso feito pela comunidade, que está sempre melhorando.</p>
-</div>
-
-Podemos discutir o processo neste [tópico de discussão](https://github.com/huggingface/smol-course/discussions/2#discussion-7602932).
-
-## Sumário do Curso
-
-Este curso fornece uma abordagem prática para trabalhar com pequenos modelos de linguagem, desde o treinamento inicial até a implantação de produção.
-
-| Módulo | Descrição | Status | Data de Lançamento |
-|--------|-------------|---------|--------------|
-| [Instruction Tuning (Ajuste de Instrução)](./1_instruction_tuning) | Aprenda sobre o ajuste fino supervisionado, modelos de bate-papo e a fazer o modelo seguir instruções básicas | ✅ Completo | 3 Dez, 2024 |
-| [Preference Alignment (Alinhamento de Preferência)](./2_preference_alignment) | Explore técnicas DPO e ORPO para alinhar modelos com preferências humanas | ✅ Completo  | 6 Dez, 2024 |
-| [Parameter-efficient Fine-tuning (Ajuste Fino com Eficiência de Parâmetro)](./3_parameter_efficient_finetuning) | Aprenda sobre LoRA, ajuste de prompt e métodos de adaptação eficientes | ✅ Completo | 9 Dez, 2024 |
-| [Evaluation (Avaliação)](./4_evaluation) | Use benchmarks automáticos e crie avaliações de domínio personalizadas | ✅ Completo | 13 Dez, 2024 |
-| [Vision-language Models (Modelos de Conjunto Visão-linguagem)](./5_vision_language_models) | Adapte modelos multimodais para tarefas visão-linguagem | ✅ Completo | 16 Dez, 2024 |
-| [Synthetic Datasets (Conjuntos de Dados Sintéticos)](./6_synthetic_datasets) | Criar e validar conjuntos de dados sintéticos para treinamento | [🚧 Em Progresso](https://github.com/huggingface/smol-course/issues/83) | 20 Dez, 2024 |
-| [Inference (Inferência)](./7_inference) | Infira modelos com eficiência | 📝 Planejado | 23 Dez, 2024 |
-| Projeto Experimental | Use o que você aprendeu para ser o top 1 na tabela de classificação! | [🚧 Em Progresso](https://github.com/huggingface/smol-course/pull/97) | Dec 23, 2024 |
-
-## Por Que Pequenos Modelos de Linguagem?
-
-Embora os grandes modelos de linguagem tenham mostrado recursos e capacidades impressionantes, eles geralmente exigem recursos computacionais significativos e podem ser exagerados para aplicativos focados. Os pequenos modelos de linguagem oferecem várias vantagens para aplicativos de domínios específicos:
-
-- **Eficiência**: Requer significativamente menos recursos computacionais para treinar e implantar
-- **Personalização**: Mais fácil de ajustar e se adaptar a domínios específicos
-- **Controle**: Melhor compreensão e controle do comportamento do modelo
-- **Custo**: Menores custos operacionais para treinamento e inferência
-- **Privacidade**: Pode ser executado localmente sem enviar dados para APIs externas
-- **Tecnologia Verde**: Defende o uso eficiente de recursos com redução da pegada de carbono 
-- **Desenvolvimento de Pesquisa Acadêmica Mais Fácil**: Oferece um ponto de partida fácil para a pesquisa acadêmica com LLMs de ponta com menos restrições logísticas
-
-## Pré-requisitos
-
-Antes de começar, verifique se você tem o seguinte:
-- Entendimento básico de machine learning e natural language processing.
-- Familiaridade com Python, PyTorch e o módulo `transformers`.
-- Acesso a um modelo de linguagem pré-treinado e um conjunto de dados rotulado.
-
-## Instalação
-
-Mantemos o curso como um pacote para que você possa instalar dependências facilmente por meio de um gerenciador de pacotes. Recomendamos [uv](https://github.com/astral-sh/uv) para esse fim, mas você pode usar alternativas como `pip` ou` pdm`.
-
-### Usando `uv`
-
-Com o `uv` instalado, você pode instalar o curso deste modo:
-
-```bash
-uv venv --python 3.11.0
-uv sync
-```
-
-### Usando `pip`
-
-Todos os exemplos são executados no mesmo ambiente **python 3.11**, então você deve criar um ambiente e instalar dependências desta maneira:
-
-```bash
-# python -m venv .venv
-# source .venv/bin/activate
-pip install -r requirements.txt
-```
-
-### Google Colab
-
-**A partir do Google Colab** você precisará instalar dependências de maneira flexível com base no hardware que está usando. Pode fazer deste jeito:
-
-```bash
-pip install transformers trl datasets huggingface_hub
-```
-
-## Engajamento
-
-Vamos compartilhar isso, desse jeito um monte de gente vai poder aprender a ajustar LLMs sem precisar de um computador super caro.
-
-[![Gráfico de Histórico de Estrelas](https://api.star-history.com/svg?repos=huggingface/smol-course&type=Date)](https://star-history.com/#huggingface/smol-course&Date)
diff --git a/pull_request_template.md b/pull_request_template.md
deleted file mode 100644
index 7c51ff78..00000000
--- a/pull_request_template.md
+++ /dev/null
@@ -1,39 +0,0 @@
-# December 2024 Student Submission
-
-## Module Completed
-- [ ] Module 1: Instruction Tuning
-- [ ] Module 2: Preference Alignment
-- [ ] Module 3: Parameter-efficient Fine-tuning
-- [ ] Module 4: Evaluation
-- [ ] Module 5: Vision-language Models
-- [ ] Module 6: Synthetic Datasets
-- [ ] Module 7: Inference
-- [ ] Module 8: Deployment
-
-## Changes Made
-Describe what you've done in this PR:
-1. What concepts did you learn?
-2. What changes or additions did you make?
-3. Any challenges you faced?
-
-## Notebooks Added/Modified
-List any notebooks you've added or modified:
-- [ ] Added new example in `module_name/student_examples/my_example.ipynb`
-- [ ] Modified existing notebook with additional examples
-- [ ] Added documentation or comments
-
-## Checklist
-
-- [ ] I have read the module materials
-- [ ] My code runs without errors
-- [ ] I have pushed models and datasets to the huggingface hub
-- [ ] My PR is based on the `december-2024` branch
-
-## Questions or Discussion Points
-Add any questions you have or points you'd like to discuss:
-1. 
-2. 
-
-## Additional Notes
-Any other information that might be helpful for reviewers:
-
diff --git a/zh-cn/1_instruction_tuning/README.md b/zh-cn/1_instruction_tuning/README.md
deleted file mode 100644
index 43f9639e..00000000
--- a/zh-cn/1_instruction_tuning/README.md
+++ /dev/null
@@ -1,30 +0,0 @@
-# 指令微调
-
-本章内容主要聚焦大语言模型指令微调部分。指令微调会把预训练模型在特定领域的数据集上进一步训练，来适配特定的任务。这一过程能有效地提升模型在目标任务上的性能。
-
-具体而言，本章将会重点探索两个主题：聊天模板和有监督微调。
-
-## 1️⃣ 聊天模板
-
-聊天模板的主要作用是把用户和 AI 模型之间的交互信息结构化，确保模型能够稳定输出且根据上下文作出回答。一个聊天模板包含系统提示词和人机两个角色发送的消息。本章的[聊天模板教程](./chat_templates.md)将会详细讲述这一内容。
-
-## 2️⃣ 有监督微调
-
-有监督微调（SFT）是你将预训练模型往特定任务迁移时的一个重要过程。SFT 通过在特定领域的有标注数据集上进一步训练，来提升模型在这里应用领域的性能。本章[有监督微调教程](./supervised_fine_tuning.md)将会详细讲解相关内容，包括其中的重要步骤和最佳实践。
-
-## 实践练习
-
-| 标题 | 简介 | 习题 | 链接 | Colab |
-|-------|-------------|----------|------|-------|
-| 聊天模板 | 学习使用 SmolLM2 的聊天模板，并将数据集转换为 ChatML 聊天模板的格式 | 🐢 将 `HuggingFaceTB/smoltalk` 数据集转换为 ChatnML 格式 <br> 🐕 将 `openai/gsm8k` 转换为 ChatML 格式 | [Notebook](./notebooks/chat_templates_example_cn.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/1_instruction_tuning/notebooks/chat_templates_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-| 有监督微调 | 学习用 SFTTrainer 去微调 SmolLM2 模型 | 🐢 使用 `HuggingFaceTB/smoltalk` 数据集训练模型<br>🐕 使用 `bigcode/the-stack-smol` 数据集训练模型<br>🦁 针对一个实际场景选取数据集去训练 | [Notebook](./notebooks/sft_finetuning_example_cn.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-
-## 参考资料
-
-- [transformers 文档中关于聊天模板的介绍](https://huggingface.co/docs/transformers/main/en/chat_templating)
-- [使用 TRL 进行有监督微调的示例脚本](https://github.com/huggingface/trl/blob/main/examples/scripts/sft.py)
-- [TRL 官方文档关于 `SFTTrainer` 的介绍](https://huggingface.co/docs/trl/main/en/sft_trainer)
-- [DPO 算法论文](https://arxiv.org/abs/2305.18290)
-- [TRL 官方文档中关于有监督微调的教程](https://huggingface.co/docs/trl/main/en/tutorials/supervised_finetuning)
-- [博客：用 ChatML 和 TRL 微调 Google Gemma 模型](https://www.philschmid.de/fine-tune-google-gemma)
-- [教程：微调大语言模型使其输出 JSON 格式的内容](https://huggingface.co/learn/cookbook/en/fine_tuning_llm_to_generate_persian_product_catalogs_in_json_format)
diff --git a/zh-cn/1_instruction_tuning/chat_templates.md b/zh-cn/1_instruction_tuning/chat_templates.md
deleted file mode 100644
index ed6fe292..00000000
--- a/zh-cn/1_instruction_tuning/chat_templates.md
+++ /dev/null
@@ -1,120 +0,0 @@
-# 聊天模板
-
-如果想将模型与用户的交互信息结构化，那么一个聊天模板（chat template）就是必需的。它为对话提供了一个固定格式，让模型能够知道上下文信息以及每条消息是由谁发出的，只有这样模型才能生成恰当的回答。
-
-## 基础模型 vs 指令模型
-
-基础模型（base model）指的是在未经整理的文本数据上训练、用于预测下一个 token 的模型，而指令模型（instruct model）则是通过微调来跟随指令、参与对话的模型。举例来说，`SmolLM2-135M` 就是基础模型，而 `SmolLM2-135M-Instruct` 则是前者经指令调优得到的指令模型。
-
-为了让基础模型成为指令模型，我们需要对我们的输入提示词进行规范化，用一种固定的格式输入给模型，以便于模型理解。这就用到**聊天模板**了。举例来说，ChatML 就是一个这样的模板，它将对话过程完全结构化，清晰地指明了每段信息是由哪个角色（系统、用户、助手）说出的。
-
-需要注意，一个基础模型可以往不同的聊天模板上微调。所以当我们使用训练好的指令模型时，我们也需要注意不要用错聊天模板。
-
-## 聊天模板简介
-
-聊天模板定义了当用户和语言模型对话时，对话信息应该遵循什么样的格式。这其中包含来自三个角色的信息：系统级的指令、用户发出的信息、AI 助手的回答。这使得每次人机交互的信息格式都是一致的，确保模型针对不同问题都能恰当回答。下面就是一个聊天模板示例：
-
-```sh
-<|im_start|>user
-Hi there!<|im_end|>
-<|im_start|>assistant
-Nice to meet you!<|im_end|>
-<|im_start|>user
-Can I ask a question?<|im_end|>
-<|im_start|>assistant
-```
-
-实际上，如果你使用 `transformers` 库的 tokenizer，它将会为我们将对话信息转化为聊天模板形式。你可以在[这里](https://huggingface.co/docs/transformers/en/chat_templating#how-do-i-use-chat-templates)查看相关文档。我们仅需将对话信息结构化，后面的事情交给 tokenizer 即可。比如，你可以把聊天信息写成这样：
-
-```python
-messages = [
-    {"role": "system", "content": "You are a helpful assistant focused on technical topics."},
-    {"role": "user", "content": "Can you explain what a chat template is?"},
-    {"role": "assistant", "content": "A chat template structures conversations between users and AI models..."}
-]
-```
-
-接下来，我们将分解聊天信息的组成：系统信息和对话部分。
-
-### 系统消息
-
-系统消息从基本层面定义了模型应有的行为。它会影响接下来所有交互。看下列示例就能明白：
-
-```python
-system_message = {
-    "role": "system",
-    "content": "You are a professional customer service agent. Always be polite, clear, and helpful."
-}
-```
-
-### 对话部分
-
-聊天模板也需要保留对话历史记录，将之前发生的人机对话保存下来，作为后续对话的参考。只有这样，我们才能实现多轮交互式对话。
-
-```python
-conversation = [
-    {"role": "user", "content": "I need help with my order"},
-    {"role": "assistant", "content": "I'd be happy to help. Could you provide your order number?"},
-    {"role": "user", "content": "It's ORDER-123"},
-]
-```
-
-## 使用 Transformers 构建聊天模板 
-
-使用 `transformers` 构建聊天模板的示例如下： 
-
-```python
-from transformers import AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-135M-Instruct")
-
-messages = [
-    {"role": "system", "content": "You are a helpful coding assistant."},
-    {"role": "user", "content": "Write a Python function to sort a list"},
-]
-
-# Apply the chat template
-formatted_chat = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-```
-
-上述代码运行完后，`formatted_chat` 应该是这样：
-```
-'<|im_start|>system\nYou are a helpful coding assistant.<|im_end|>\n<|im_start|>user\nWrite a Python function to sort a list<|im_end|>\n<|im_start|>assistant\n'
-```
-
-### 自定义聊天模板格式
-
-你也可以自定义聊天模板格式，比如为不同角色的信息添加特殊的 token 来作为标识：
-
-```python
-template = """
-<|system|>{system_message}
-<|user|>{user_message}
-<|assistant|>{assistant_message}
-""".lstrip()
-```
-
-### 对多轮对话的支持
-
-聊天模板可以处理复杂多轮对话，同时保留上下文信息：
-
-```python
-messages = [
-    {"role": "system", "content": "You are a math tutor."},
-    {"role": "user", "content": "What is calculus?"},
-    {"role": "assistant", "content": "Calculus is a branch of mathematics..."},
-    {"role": "user", "content": "Can you give me an example?"},
-]
-```
-
-⏭️ [下一节课程：有监督微调](./supervised_fine_tuning.md)
-
-## 其它学习资源
-
-- [Hugging Face 聊天模板使用指南](https://huggingface.co/docs/transformers/main/en/chat_templating)
-- [Transformers 官方文档](https://huggingface.co/docs/transformers)
-- [包含各种聊天模板的代码仓库](https://github.com/chujiezheng/chat_templates) 
diff --git a/zh-cn/1_instruction_tuning/notebooks/.env.example b/zh-cn/1_instruction_tuning/notebooks/.env.example
deleted file mode 100644
index f0e16a46..00000000
--- a/zh-cn/1_instruction_tuning/notebooks/.env.example
+++ /dev/null
@@ -1,2 +0,0 @@
-# Duplicate this file to .env and fill in secret values
-HF_TOKEN=
diff --git a/zh-cn/1_instruction_tuning/notebooks/chat_templates_example.ipynb b/zh-cn/1_instruction_tuning/notebooks/chat_templates_example.ipynb
deleted file mode 100644
index a6d98a62..00000000
--- a/zh-cn/1_instruction_tuning/notebooks/chat_templates_example.ipynb
+++ /dev/null
@@ -1,5735 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "vZAvFVIAtFlq"
-   },
-   "source": [
-    "# 借助 SmolLM2 探索聊天模板\n",
-    "\n",
-    "这个教程演示如何在 `SmolLM2` 下使用聊天模板。借助聊天模板，人机之间的交互信息可以被结构化，这样我们才能确保模型的回答和上下文信息保持一致性。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "K-lZu8JvtwUN",
-    "outputId": "c3871418-15bc-4265-ae8d-6d6036036d0e"
-   },
-   "outputs": [],
-   "source": [
-    "# Install the requirements in Google Colab\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "# Authenticate to Hugging Face\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()\n",
-    "\n",
-    "# for convenience you can create an environment variable containing your hub token as HF_TOKEN"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "id": "tnHzBR7vtFlr"
-   },
-   "outputs": [],
-   "source": [
-    "# Import necessary libraries\n",
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "from trl import setup_chat_format\n",
-    "import torch"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "XTVOqbuetFlr"
-   },
-   "source": [
-    "## SmolLM2 的聊天模板\n",
-    "\n",
-    "我们先看看怎样使用 `SmolLM2` 的聊天模板。这里我们先定义一个简单的对话，然后用聊天模板转换它。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 397,
-     "referenced_widgets": [
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-     ]
-    },
-    "id": "Nrxh0oX6tFls",
-    "outputId": "953e1527-8168-4346-9338-6e188ca31a1a"
-   },
-   "outputs": [],
-   "source": [
-    "# Dynamically set the device\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=model_name\n",
-    ").to(device)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
-    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "id": "zkJwILrbtFls"
-   },
-   "outputs": [],
-   "source": [
-    "# Define messages for SmolLM2\n",
-    "messages = [\n",
-    "    {\"role\": \"user\", \"content\": \"Hello, how are you?\"},\n",
-    "    {\n",
-    "        \"role\": \"assistant\",\n",
-    "        \"content\": \"I'm doing well, thank you! How can I assist you today?\",\n",
-    "    },\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Ve4dgtjstFls"
-   },
-   "source": [
-    "# 先不进行 tokenize，应用一下聊天模板\n",
-    "\n",
-    "先把 `tokenize` 这个入参设为 `False`。可以看到，下面例子增加了额外的角色信息。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "pbAg-5x-tFls",
-    "outputId": "5f9482db-1fcf-4c13-ccaa-ef3f6eff7f76"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Conversation with template: <|im_start|>user\n",
-      "Hello, how are you?<|im_end|>\n",
-      "<|im_start|>assistant\n",
-      "I'm doing well, thank you! How can I assist you today?<|im_end|>\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "input_text = tokenizer.apply_chat_template(messages, tokenize=False)\n",
-    "\n",
-    "print(\"Conversation with template:\", input_text)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "sfvdglOqtFls"
-   },
-   "source": [
-    "# 对已经 tokenize 过的对话进行解码\n",
-    "\n",
-    "如果已经把 `tokenize` 设为 `True`，可以通过解码的方式查看对话内容。可以看到，这里多了一个 assistant 信息。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "mXUVdPeytFls",
-    "outputId": "80870e53-7bc1-426e-ac33-ba6748e030fc"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Conversation decoded: <|im_start|>user\n",
-      "Hello, how are you?<|im_end|>\n",
-      "<|im_start|>assistant\n",
-      "I'm doing well, thank you! How can I assist you today?<|im_end|>\n",
-      "<|im_start|>assistant\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "input_text = tokenizer.apply_chat_template(\n",
-    "    messages, tokenize=True, add_generation_prompt=True\n",
-    ")\n",
-    "\n",
-    "print(\"Conversation decoded:\", tokenizer.decode(token_ids=input_text))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "UcZQpspEtFlt"
-   },
-   "source": [
-    "# tokenize 过后的对话是什么形式\n",
-    "\n",
-    "对对话应用聊天模板并进行 tokenize 之后，我们发现，整个对话，包括特殊 token，都被转化为了一系列整数。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "jc2PLxAMtFlt",
-    "outputId": "d2098780-b3f4-41ec-a1f3-b6da2b593c62"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Conversation tokenized: [1, 4093, 198, 19556, 28, 638, 359, 346, 47, 2, 198, 1, 520, 9531, 198, 57, 5248, 2567, 876, 28, 9984, 346, 17, 1073, 416, 339, 4237, 346, 1834, 47, 2, 198, 1, 520, 9531, 198]\n"
-     ]
-    }
-   ],
-   "source": [
-    "input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)\n",
-    "\n",
-    "print(\"Conversation tokenized:\", input_text)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "m3eNp9a0tFlt"
-   },
-   "source": [
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>练习：为 SFT 准备数据集</h2>\n",
-    "    <p>从 HuggingFace 上找一个数据集，然后为后续 SFT 转化它</p>\n",
-    "    <p><b>难度等级</b></p>\n",
-    "    <p>🐢 将 `HuggingFaceTB/smoltalk` 数据集转换为 ChatML 形式</p>\n",
-    "    <p>🐕 将 `openai/gsm8k` 数据集转换为 ChatML 形式</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
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-    "id": "qbkXV2_ItFlt",
-    "outputId": "06deadc3-2c63-4660-d2bd-05096ef07c9f"
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-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<iframe\n",
-       "  src=\"https://huggingface.co/datasets/HuggingFaceTB/smoltalk/embed/viewer/all/train?row=0\"\n",
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-       "  width=\"100%\"\n",
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-    "from IPython.core.display import display, HTML\n",
-    "\n",
-    "display(\n",
-    "    HTML(\n",
-    "        \"\"\"<iframe\n",
-    "  src=\"https://huggingface.co/datasets/HuggingFaceTB/smoltalk/embed/viewer/all/train?row=0\"\n",
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-    "id": "4p3atw4_tFlu",
-    "outputId": "62ee9812-3819-4a9c-9e24-5687368ffcd8"
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-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "ds = load_dataset(\"HuggingFaceTB/smoltalk\", \"everyday-conversations\")\n",
-    "\n",
-    "\n",
-    "def process_dataset(sample):\n",
-    "    # TODO: 🐢 Convert the sample into a chat format\n",
-    "    # use the tokenizer's method to apply the chat template\n",
-    "    return sample\n",
-    "\n",
-    "\n",
-    "ds = ds.map(process_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
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-     "height": 381
-    },
-    "id": "81fQeazltFlu",
-    "outputId": "36cf7148-9881-4f13-d0ce-76c82c4ab219"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<iframe\n",
-       "  src=\"https://huggingface.co/datasets/openai/gsm8k/embed/viewer/main/train\"\n",
-       "  frameborder=\"0\"\n",
-       "  width=\"100%\"\n",
-       "  height=\"360px\"\n",
-       "></iframe>\n"
-      ],
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-    "display(\n",
-    "    HTML(\n",
-    "        \"\"\"<iframe\n",
-    "  src=\"https://huggingface.co/datasets/openai/gsm8k/embed/viewer/main/train\"\n",
-    "  frameborder=\"0\"\n",
-    "  width=\"100%\"\n",
-    "  height=\"360px\"\n",
-    "></iframe>\n",
-    "\"\"\"\n",
-    "    )\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true,
-    "id": "bWUSv7NMtFlu",
-    "jupyter": {
-     "outputs_hidden": true
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-   "outputs": [],
-   "source": [
-    "ds = load_dataset(\"openai/gsm8k\", \"main\")\n",
-    "\n",
-    "\n",
-    "def process_dataset(sample):\n",
-    "    # TODO: 🐕 Convert the sample into a chat format\n",
-    "\n",
-    "    # 1. create a message format with the role and content\n",
-    "\n",
-    "    # 2. apply the chat template to the samples using the tokenizer's method\n",
-    "\n",
-    "    return sample\n",
-    "\n",
-    "\n",
-    "ds = ds.map(process_dataset)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "qlXCuRKotFlu"
-   },
-   "source": [
-    "## 总结\n",
-    "\n",
-    "本文讲解了在 `SmolLM2` 模型中如何使用聊天模板。通过聊天模板来结构化人机交互信息，我们可以确保 AI 模型可以给出和上下文保持一致性的回答。\n",
-    "\n",
-    "在练习中，你将尝试将数据集转为 ChatML 格式。但实际情况中，TRL 会为你做这些事情。不过，充分理解背后的处理逻辑还是很有必要的。"
-   ]
-  }
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diff --git a/zh-cn/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb b/zh-cn/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb
deleted file mode 100644
index 7d259da3..00000000
--- a/zh-cn/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb
+++ /dev/null
@@ -1,273 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 用 SFTTrainer 实现有监督微调\n",
-    "\n",
-    "本教程将展示如何使用 `trl` 代码库中的 `SFTTrainer` 来微调 `HuggingFaceTB/SmolLM2-135M`。文中代码模块将会展示具体步骤。你可以根据难度选取在不同数据集上微调。\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>练习：用 SFTTrainer 微调 SmolLM2</h2>\n",
-    "    <p>从 HuggingFace 上选取一个数据集然后微调模型 </p> \n",
-    "    <p><b>难度等级</b></p>\n",
-    "    <p>🐢 使用 `HuggingFaceTB/smoltalk` 数据集</p>\n",
-    "    <p>🐕 尝试 `bigcode/the-stack-smol` 数据集，在 `data/python` 这个子集上微调一个代码生成模型</p>\n",
-    "    <p>🦁 选取一个你感兴趣的领域，使用真实世界数据集，为你的特定应用微调模型</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install the requirements in Google Colab\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "# Authenticate to Hugging Face\n",
-    "\n",
-    "from huggingface_hub import login\n",
-    "login()\n",
-    "\n",
-    "# for convenience you can create an environment variable containing your hub token as HF_TOKEN"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import necessary libraries\n",
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "from datasets import load_dataset\n",
-    "from trl import SFTConfig, SFTTrainer, setup_chat_format\n",
-    "import torch\n",
-    "\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "# Load the model and tokenizer\n",
-    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=model_name\n",
-    ").to(device)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
-    "\n",
-    "# Set up the chat format\n",
-    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n",
-    "\n",
-    "# Set our name for the finetune to be saved &/ uploaded to\n",
-    "finetune_name = \"SmolLM2-FT-MyDataset\"\n",
-    "finetune_tags = [\"smol-course\", \"module_1\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 用基准模型生成\n",
-    "\n",
-    "我们首先看看不使用聊天模板的基础模型是什么效果。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Let's test the base model before training\n",
-    "prompt = \"Write a haiku about programming\"\n",
-    "\n",
-    "# Format with template\n",
-    "messages = [{\"role\": \"user\", \"content\": prompt}]\n",
-    "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n",
-    "\n",
-    "# Generate response\n",
-    "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
-    "outputs = model.generate(**inputs, max_new_tokens=100)\n",
-    "print(\"Before training:\")\n",
-    "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 准备数据集\n",
-    "\n",
-    "接下来我们载入一个简单数据集，并转换格式。这个数据集需要结构化为“输入-输出”这样成对的形式，其中输入是一个提示语，输出就是我们期待的模型回答。\n",
-    "\n",
-    "**TRL 会自动根据聊天模板转换输入信息。**这些信息需要写成一个字典（dict）的列表，其中 key 是 `role` 和 `content`，分别代表哪个角色以及对应的话语。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load a sample dataset\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "# TODO: 稍后你可以使用你自己的数据集，替换以下的路径、名字等参数配置\n",
-    "ds = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO: 🦁 如果你的数据集不是 TRL 能转换成聊天模板的格式，你还需要额外处理一下。参考[聊天模板](../chat_templates_cn.md)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 配置 SFTTrainer\n",
-    "\n",
-    "我们还需要配置 `SFTTrainer` 的各项参数，来控制训练过程。这包括训练步数、batch size、学习率，以及评估策略。你需要根据你自己的情况和计算资源来调节。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure the SFTTrainer\n",
-    "sft_config = SFTConfig(\n",
-    "    output_dir=\"./sft_output\",\n",
-    "    max_steps=1000,  # Adjust based on dataset size and desired training duration\n",
-    "    per_device_train_batch_size=4,  # Set according to your GPU memory capacity\n",
-    "    learning_rate=5e-5,  # Common starting point for fine-tuning\n",
-    "    logging_steps=10,  # Frequency of logging training metrics\n",
-    "    save_steps=100,  # Frequency of saving model checkpoints\n",
-    "    evaluation_strategy=\"steps\",  # Evaluate the model at regular intervals\n",
-    "    eval_steps=50,  # Frequency of evaluation\n",
-    "    use_mps_device=(\n",
-    "        True if device == \"mps\" else False\n",
-    "    ),  # Use MPS for mixed precision training\n",
-    "    hub_model_id=finetune_name,  # Set a unique name for your model\n",
-    ")\n",
-    "\n",
-    "# Initialize the SFTTrainer\n",
-    "trainer = SFTTrainer(\n",
-    "    model=model,\n",
-    "    args=sft_config,\n",
-    "    train_dataset=ds[\"train\"],\n",
-    "    tokenizer=tokenizer,\n",
-    "    eval_dataset=ds[\"test\"],\n",
-    ")\n",
-    "\n",
-    "# TODO: 🦁 🐕 根据你自己的数据集调节 SFTTrainer 的参数。假如你用的是 `bigcode/the-stack-smol` 数据集，那你就需要选择 `content` 这一列。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 训练模型\n",
-    "\n",
-    " 当上述配置完成后，我们就可以训练了。这个过程将遍历数据集、计算损失函数、更新模型参数等。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Train the model\n",
-    "trainer.train()\n",
-    "\n",
-    "# Save the model\n",
-    "trainer.save_model(f\"./{finetune_name}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.push_to_hub(tags=finetune_tags)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>额外练习：用微调过的模型来生成</h2>\n",
-    "    <p>🐕 使用微调过的模型来生成回答，就像之前用基准模型一样。对比一下效果</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Test the fine-tuned model on the same prompt\n",
-    "\n",
-    "# Let's test the base model before training\n",
-    "prompt = \"Write a haiku about programming\"\n",
-    "\n",
-    "# Format with template\n",
-    "messages = [{\"role\": \"user\", \"content\": prompt}]\n",
-    "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n",
-    "\n",
-    "# Generate response\n",
-    "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
-    "\n",
-    "# TODO: 用微调过的模型生成相同的回答，看看有没有变化改进"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 💐 完成了！\n",
-    "\n",
-    "本文提供了使用 `SFTTrainer` 微调 `HuggingFaceTB/SmolLM2-135M` 的指南。依照这些步骤，你可以将模型适配到各种特殊任务场景上。如果你觉得这个课程还不错，你还可以尝试这些：\n",
-    "\n",
-    "- 使用这个 notebook 完成难点分级更高的任务\n",
-    "- 在 GitHub 上评审别人的 pull request\n",
-    "- 通过提出 Issue 或 PR 进一步改进我们的课程资料"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/zh-cn/1_instruction_tuning/supervised_fine_tuning.md b/zh-cn/1_instruction_tuning/supervised_fine_tuning.md
deleted file mode 100644
index e7250c38..00000000
--- a/zh-cn/1_instruction_tuning/supervised_fine_tuning.md
+++ /dev/null
@@ -1,41 +0,0 @@
-# 有监督微调
-
-有监督微调（Supervised Fine-Tuning），简称 SFT，是将预训练模型往特定领域或任务迁移的一个重要过程。虽然预训练模型总体性能也很不错，但如果需要应对特定场景，就必须针对场景进行定制化。SFT 通过在人工筛选的高质量数据上进一步训练，将预训练模型往特定任务上迁移。
-
-## SFT 原理简介
-
-SFT 核心思想是让预训练模型学习标注过的、特定领域的数据。这个过程会向模型提供很多我们想要的输入-输出，让模型去学习我们想要的特定回答模式。
-
-由于利用了预训练模型学到的基础知识，通过 SFT 将模型进一步适配到特定领域的训练还是非常高效的。
-
-## 什么时候使用 SFT
-
-SFT 通常在你当前模型的能力和你的特殊需求还存在差距时使用，尤其是当你想要精细控制模型输出，或是想让模型在特定领域发挥作用时。
-
-举例来说，如果你在开发客户服务相关的应用，那你可能需要让模型严格遵循公司规定、用标准化的流程去处理技术性的咨询。或者，在医疗或法律领域，准确表达特定领域的专业术语也很重要。在这些情况下，SFT 就能将模型性能对齐到领域内专家的水平，使得模型的回答符合专业标准。
-
-## 微调的过程
-
-SFT 主要就是在特定任务的数据集上训练模型。
-
-首先，你需要准备一个能反映任务类型的数据集，这个数据集需要涵盖尽可能广泛的领域内问答场景。数据的质量非常重要，每条数据都应该向模型展示你希望得到的回答类型。接下来就是实际微调阶段了，你可以使用 HuggingFace 的 `transformers` 和 `trl` 在数据集上训练模型。
-
-整个过程中，不断地对模型进行评测也是很重要的。你可以找一个验证集，然后实时监控模型性能，来确保模型学到了特定领域内的回答，同时又不丧失它原有的通用能力。在[第四章](../4_evaluation)，我们将讲解如何评测模型。
-
-## 通过 SFT 对齐特定偏好
-
-此外，SFT 也广泛用于将语言模型对齐到特定的人类偏好上。如 RLHF 和 DPO 等技术依靠 SFT 来形成对任务的基本理解，然后再进一步对模型的响应进行优化，以达到预期效果。预训练模型，尽管在通用的语言能力上效果很强，但它的回答可能不符合人类的偏好。SFT 通过引入专业领域的数据进行指导，可以改善模型生成与人类期望的匹配程度。
-
-## 使用 Transformer Reinforcement Learning 进行 SFT
-
-TRL（Transformer Reinforcement Learning）是常用于 SFT 的一个重要的软件包。如果你使用强化学习训练 transformer 系列的语言模型，TRL 将为你提供有用工具。
-
-TRL 基于 HuggingFace 的 `transformers` 构建，允许用户直接载入预训练模型，并支持大部分 decoder 或 encoder-decoder 的架构。这个库涵盖了针对语言模型的主流强化学习算法，包括 SFT、奖励模型、PPO、DPO 等。在本课程中，我们将大量使用 TRL。
-
-# 接下来的学习
-
-请通过下列 notebook 上手 SFT：
-
-⏭️ [聊天模板教程](./notebooks/chat_templates_example_cn.ipynb)
-
-⏭️ [有监督微调教程](./notebooks/supervised_fine_tuning_tutorial_cn.ipynb)
\ No newline at end of file
diff --git a/zh-cn/2_preference_alignment/README.md b/zh-cn/2_preference_alignment/README.md
deleted file mode 100644
index 48232423..00000000
--- a/zh-cn/2_preference_alignment/README.md
+++ /dev/null
@@ -1,40 +0,0 @@
-# 偏好对齐
-
-本章将学习如何将语言模型的输出和人类偏好对齐。虽然有监督微调（SFT）已经将模型适配到特定的任务领域了，但偏好对齐（Prefenrece Alignment）将会迫使模型的输出更加匹配人类的期望、符合人类的价值观。
-
-## 概览
-
-典型的偏好对齐方法一般都包含这几个步骤：
-1. 使用 SFT 将模型适配到特定的领域
-2. 使用偏好对齐（如 RLHF 或 DPO 算法）进一步提升模型回答的质量
-
-其它偏好对齐算法还包括 ORPO，这个算法将指令微调和偏好对齐结合进了一个单一步骤中。本章我们将重点学习 DPO 和 ORPO 算法。
-
-如果你还想进一步学习相关对齐算法，你可以阅读[这篇博客](https://argilla.io/blog/mantisnlp-rlhf-part-8)。
-
-### 1️⃣ 直接偏好优化（DPO）
-
-直接偏好优化（Direct Preference Optimization），简称 DPO，直接使用偏好数据对模型进行参数更新。这简化了偏好对齐的过程。这个方法无需额外设置激励模型、无需复杂强化学习步骤，比基于人类反馈的强化学习（RLHF）更高效更稳定。本章中对应的学习资料在[这里](./dpo.md)。
-
-### 2️⃣ 基于优势比的偏好优化（ORPO）
-
-基于优势比的偏好优化（Odds Ratio Preference Optimization），简称 ORPO，是一种将指令微调和偏好对齐结合在一起的方法。通过在 token 层面定义一个优势比（Odds），并在优势比上使用负对数似然损失函数，ORPO 改变了传统的语言建模的目标函数。ORPO 训练步骤简单、无需 DPO 中的参考模型，计算效率也更高。该方法在多项评测基准上展现了优秀的效果，尤其在 AlpacaEval 超越了传统方法。本章中对应的学习资料在[这里](./orpo.md)。
-
-## 实践练习
-
-| 标题 | 简介 | 习题 | 链接 | Colab |
-|-------|-------------|----------|------|-------|
-| DPO 训练 | 学习用 DPO 训练模型 | 🐢 在 Anthropic HH-RLHF 数据集上训练模型<br>🐕 使用你自己的偏好数据集<br>🦁 对不同的偏好数据集和不同大小的模型进行实验 | [Notebook](./notebooks/dpo_finetuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-| ORPO 训练 | 学习用 ORPO 训练模型 | 🐢 用指令数据和偏好数据训练模型<br>🐕 对不同的损失权重进行实验<br>🦁 对比 ORPO 和 DPO 的结果 | [Notebook](./notebooks/orpo_finetuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-
-
-## 参考资料
-
-- [TRL 官方文档](https://huggingface.co/docs/trl/index) - TRL 是一个基于 Transformers 的强化学习库，这里实现了包括 DPO 在内的各种对齐算法。
-- [DPO 论文](https://arxiv.org/abs/2305.18290) - 该论文针对当时已有的 RLHF 方法，提出了新的对齐方法，可以直接使用偏好数据优化模型参数。
-- [ORPO 论文](https://arxiv.org/abs/2403.07691) - ORPO 算法将指令微调和偏好优化和并进一个训练步骤中。
-- [RLHF 相关博客](https://argilla.io/blog/mantisnlp-rlhf-part-8/) - 这篇博客介绍了包括 RLHF、DPO 在内的对齐算法，同时也介绍了具体实现方法。
-- [DPO 相关博客](https://huggingface.co/blog/dpo-trl) - 介绍了使用 TRL 实现 DPO 的具体步骤，包括示例代码和其它最佳实践经验。
-- [DPO 示例训练脚本](https://github.com/huggingface/trl/blob/main/examples/scripts/dpo.py) - 完整的基于 TRL 的 DPO 训练代码。
-- [ORPO 示例训练脚本](https://github.com/huggingface/trl/blob/main/examples/scripts/orpo.py) - 完整的基于 TRL 的 ORPO 训练代码。
-- [Hugging Face 关于对齐训练的资料](https://github.com/huggingface/alignment-handbook) - 包括 SFT、DPO、RLHF 的语言模型对齐算法介绍，包括理论指导和实践代码。
\ No newline at end of file
diff --git a/zh-cn/2_preference_alignment/dpo.md b/zh-cn/2_preference_alignment/dpo.md
deleted file mode 100644
index ea3b23d8..00000000
--- a/zh-cn/2_preference_alignment/dpo.md
+++ /dev/null
@@ -1,72 +0,0 @@
-# 直接偏好优化（DPO）
-
-直接偏好优化（Direct Preference Optimization），简称 DPO，是一种非常简洁的使用人类偏好数据对齐模型的算法。DPO 直接使用偏好数据优化模型，无需 RLHF 的激励模型和强化学习步骤。
-
-## 理解 DPO
-
-DPO 将偏好对齐任务转化为了一个在偏好数据上训练的分类任务。传统的 RLHF 需要训练一个额外的激励模型，并使用强化学习方法（如 PPO）去对齐模型输出。DPO简化了这个步骤，通过定义一个损失函数，直接在“倾向的输出”和“不倾向的输出”上进行训练。
-
-这个方法在实践中十分高效，Llama 模型的训练就使用了 DPO。同时，没有了激励模型了强化学习，DPO 训练也更简单、更稳定。
-
-## DPO 工作原理
-
-在 DPO 之前，我们需要使用 SFT 微调模型，用指令跟随的数据集先把模型适配到特定任务领域中，让模型在这个领域具备基本能力。
-
-接下来才是偏好学习。模型将在“倾向的输出”和“不倾向的输出”这样成对的数据上训练，学习哪种类型的回答更符合人类的喜好。
-
-DPO 的关键原理在于它直接使用偏好数据进行优化。不同于 RLHF，DPO 使用了二分类的交叉墒损失函数，这里的损失直接在“倾向的输出”和“不倾向的输出”这样的成对数据上计算。这使得模型训练更稳定、更高效，同时效果甚至还比 RLHF 好。
-
-## DPO 数据集
-
-构造 DPO 专用数据集，一般需要对回答进行“倾向”和“不倾向”的标注。使用人工标注或自动化方法都可以实现这一步骤。下表就是一个示例数据集：
-
-| Prompt | Chosen | Rejected |
-|--------|--------|----------|
-| ...    | ...    | ...      |
-| ...    | ...    | ...      |
-| ...    | ...    | ...      |
-
-`Prompt` 这一栏提供问题， `Chosen` 和 `Rejected` 分别代表针对这个问题我们倾向的回答和不倾向的回答。`chosen` 和 `rejected` 也可以是一个列表形式，包含多个不同的回答。
-
-你可以在 Hugging Face 的[这个地方](https://huggingface.co/collections/argilla/preference-datasets-for-dpo-656f0ce6a00ad2dc33069478)找到很多 DPO 数据集。
-
-## 用 TRL 实现 DPO
-
-使用 TRL 实现 DPO 非常简单直接，仅需配置 `DPOConfig` 和 `DPOTrainer` 即可。这两个类遵循 `transformers` 的 API 风格。
-下面就是一个简单的例子：
-
-```python
-from trl import DPOConfig, DPOTrainer
-
-# Define arguments
-training_args = DPOConfig(
-    ...
-)
-
-# Initialize trainer
-trainer = DPOTrainer(
-    model,
-    train_dataset=dataset,
-    tokenizer=tokenizer,
-    ...
-)
-
-# Train model
-trainer.train()
-```
-
-我们还将在 [DPO 教程](./notebooks/dpo_finetuning_example.ipynb) 中详细讲解 `DPOConfig` 和 `DPOTrainer` 的配置。
-
-## 最佳实践
-
-数据质量对 DPO 的成败至关重要。偏好数据集必须足够多样，涵盖不同的想要的回答。在数据标注过程中，需要制定清晰明确的标注指导。通过提高数据集质量一般都可以提升模型性能，可能的做法包括对大规模数据集进行过滤，仅保留高质量数据，或仅保留和应用领域相关的数据。
-
-训练过程中，仔细监视损失的收敛情况、及时验证性能也很重要。及时调节 $\beta$ 参数，在偏好学习和通用能力间找到平衡。有规律地在多样的问题上做验证测试，确保模型不过你和。这些也都很重要。
-
-同时，也要对比一下原模型和优化后模型针对同一问题的回答，看看 模型是否学到了偏好。在包括极端情况下的问题集上测试，确保模型健壮性。
-
-## 接下来的学习
-
-⏩ 在 [DPO 教程](./notebooks/dpo_finetuning_example.ipynb)中，你可以直接上手实践。该教程将会带你实践 DPO 的整个过程，从数据准备指导模型训练和验证。
-
-⏭️ 之后，你还可以学习 [ORPO](./orpo.md)，了解更多偏好优化算法。
\ No newline at end of file
diff --git a/zh-cn/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb b/zh-cn/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
deleted file mode 100644
index e3d9869b..00000000
--- a/zh-cn/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
+++ /dev/null
@@ -1,5496 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 使用直接偏好优化（DPO）进行偏好对齐\n",
-    "\n",
-    "本教程将会带你使用直接偏好优化（DPO）的方法去微调一个语言模型。这里我们使用的是 SmolLM2-135M-Instruct 模型，它已经经过了 SFT 这一训练步骤，所以可以接着进行 DPO。当然你也可以使用你在[第一章](../../1_instruction_tuning/notebooks/sft_finetuning_example_cn.ipynb)训练好的模型。\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "     <h2 style='margin: 0;color:blue'>练习：用 DPOTrainer 对 SmolLM2 做对齐训练</h2>\n",
-    "     <p>从 Hugging Face hub 上找一个数据集并在上面对齐模型。</p> \n",
-    "     <p><b>难度等级</b></p>\n",
-    "     <p>🐢 使用 `trl-lib/ultrafeedback_binarized` 数据集</p>\n",
-    "     <p>🐕 使用 `argilla/ultrafeedback-binarized-preferences` 数据集</p>\n",
-    "     <p>🦁 选用你感兴趣的真实世界数据集，或者使用你在<a href=\"../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb\">上一章</a> SFT 训练好的模型</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install the requirements in Google Colab\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "# Authenticate to Hugging Face\n",
-    "\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()\n",
-    "\n",
-    "# for convenience you can create an environment variable containing your hub token as HF_TOKEN"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 准备好需要的 Python 库\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "_zIBL8IssExG",
-    "outputId": "90d70bd2-30e7-49a4-d5bf-20d74deb6cec"
-   },
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "import os\n",
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "from datasets import load_dataset\n",
-    "from trl import DPOTrainer, DPOConfig\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "d8CvUgROUDw-"
-   },
-   "source": [
-    "## 配置数据集及其格式"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "MCD77GZ60DOT"
-   },
-   "outputs": [],
-   "source": [
-    "# Load dataset\n",
-    "\n",
-    "# TODO: 🦁🐕 你也可以使用你感兴趣的其他数据集\n",
-    "dataset = load_dataset(path=\"trl-lib/ultrafeedback_binarized\", split=\"train\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO: 🐕 如果你的数据集不是对话列表形式，你可以使用 `process_dataset` 函数进行转化"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 选择一个经 SFT 训练过模型\n",
-    "\n",
-    "我们这里使用 SmolLM2-135M-Instruct 模型，因为它已经经过 SFT 了，所以可以接着进行 DPO 训练。你也可以使用你[第一章](../../1_instruction_tuning/notebooks/sft_finetuning_example_cn.ipynb) 训练好的模型。\n",
-    "\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; width:80%; color:black'>\n",
-    "     <p>🦁 如果你想使用<a href=\"../../1_instruction_tuning/notebooks/sft_finetuning_example_cn.ipynb\">第一章</a>你自己训练的模型，你需要修改下面代码的 model_name 到指定的路径</p>\n",
-    "</div>\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_name = \"HuggingFaceTB/SmolLM2-135M-Instruct\"\n",
-    "\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\"\n",
-    "    if torch.backends.mps.is_available()\n",
-    "    else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "# Model to fine-tune\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=model_name,\n",
-    "    torch_dtype=torch.float32,\n",
-    ").to(device)\n",
-    "model.config.use_cache = False\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
-    "tokenizer.pad_token = tokenizer.eos_token\n",
-    "\n",
-    "# Set our name for the finetune to be saved &/ uploaded to\n",
-    "finetune_name = \"SmolLM2-FT-DPO\"\n",
-    "finetune_tags = [\"smol-course\", \"module_1\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "DeT5eUK_UJgK"
-   },
-   "source": [
-    "## 开始 DPO 训练"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000,
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-     ]
-    },
-    "id": "rKPILNOLR-aK",
-    "outputId": "1e9c1a6e-910d-40f9-e0bb-2a83a322d015"
-   },
-   "outputs": [],
-   "source": [
-    "# Training arguments\n",
-    "training_args = DPOConfig(\n",
-    "    # 单 GPU 上 batch size 的大小\n",
-    "    per_device_train_batch_size=4,\n",
-    "    # 每经过多少步才进行一次反向传播、参数更新\n",
-    "    # 每个 GPU 实际起作用的 batch size 等于 per_device_train_batch_size 乘 gradient_accumulation_steps\n",
-    "    gradient_accumulation_steps=4,\n",
-    "    # 这个操作在前向传播时不保存中间结果激活值，可以节省内存\n",
-    "    # 在反向传播时，模型重新计算前向过程，获取计算梯度所需要的激活值\n",
-    "    gradient_checkpointing=True,\n",
-    "    # 训练时的基本学习率\n",
-    "    learning_rate=5e-5,\n",
-    "    # 训练过程中学习率变化策略：\"cosine\" 策略根据余弦函数的曲线形状逐步递减学习率\n",
-    "    lr_scheduler_type=\"cosine\",\n",
-    "    # 总的训练部数\n",
-    "    max_steps=200,\n",
-    "    # 在模型训练过程中不去保存 ckeckpoint\n",
-    "    save_strategy=\"no\",\n",
-    "    # 打印 log 的频率\n",
-    "    logging_steps=1,\n",
-    "    # 保存结果的目录\n",
-    "    output_dir=\"smol_dpo_output\",\n",
-    "    # 学习率 warmup 的步数\n",
-    "    warmup_steps=100,\n",
-    "    # 是否使用 bfloat16 精度来加速训练\n",
-    "    bf16=True,\n",
-    "    # 这里我们不使用 wandb 或 tensorboard \n",
-    "    report_to=None,\n",
-    "    # 这里我们保留数据集里没用到的数据列\n",
-    "    remove_unused_columns=False,\n",
-    "    # 如果你不使用 Mac 电脑，这里就是 False\n",
-    "    use_mps_device=device == \"mps\",\n",
-    "    # 如果你要将训练好的模型上传到 HuggingFace Hub，这里就是你的模型名字 \n",
-    "    hub_model_id=finetune_name,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer = DPOTrainer(\n",
-    "    # The model to be trained\n",
-    "    model=model,\n",
-    "    # Training configuration from above\n",
-    "    args=training_args,\n",
-    "    # Dataset containing preferred/rejected response pairs\n",
-    "    train_dataset=dataset,\n",
-    "    # Tokenizer for processing inputs\n",
-    "    processing_class=tokenizer,\n",
-    "    # DPO 特有的 temperature 参数，控制参考模型的强度\n",
-    "    # 较小的值可以让模型更保守、更缓慢地学习偏好\n",
-    "    beta=0.1,\n",
-    "    # 提示语的最大长度\n",
-    "    max_prompt_length=1024,\n",
-    "    # 提示语 + 回答 的序列最大长度\n",
-    "    max_length=1536,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Train the model\n",
-    "trainer.train()\n",
-    "\n",
-    "# Save the model\n",
-    "trainer.save_model(f\"./{finetune_name}\")\n",
-    "\n",
-    "# Save to the huggingface hub if login (HF_TOKEN is set)\n",
-    "if os.getenv(\"HF_TOKEN\"):\n",
-    "    trainer.push_to_hub(tags=finetune_tags)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 💐 完成\n",
-    "\n",
-    "本教程带你一步步地学习了用 `DPOTrainer` 训练 `HuggingFaceTB/SmolLM2-135M`的过程。利用这些步骤，你可以高效快速地进行特定任务领域的偏好优化训练。感兴趣的话，你还可以：\n",
-    "\n",
-    "- 尝试本教程难度等级更高的任务\n",
-    "- 在 GitHub 上评审别人的 Pull Request\n",
-    "- 通过 Issue 或 PR 提出课程改进的意见"
-   ]
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "gpuType": "A100",
-   "machine_shape": "hm",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.10"
-  },
-  "widgets": {
-   "application/vnd.jupyter.widget-state+json": {
-    "00c320491e8a4c968b1c43248e0a7a4f": {
-     "model_module": "@jupyter-widgets/controls",
-     "model_module_version": "1.5.0",
-     "model_name": "FloatProgressModel",
-     "state": {
-      "_dom_classes": [],
-      "_model_module": "@jupyter-widgets/controls",
-      "_model_module_version": "1.5.0",
-      "_model_name": "FloatProgressModel",
-      "_view_count": null,
-      "_view_module": "@jupyter-widgets/controls",
-      "_view_module_version": "1.5.0",
-      "_view_name": "ProgressView",
-      "bar_style": "success",
-      "description": "",
-      "description_tooltip": null,
-      "layout": "IPY_MODEL_752ab188972f43e8abd7b4cfbd371270",
-      "max": 3,
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-      "orientation": "horizontal",
-      "style": "IPY_MODEL_b2a52f9dd2954c0d92610a6775297013",
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diff --git a/zh-cn/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb b/zh-cn/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
deleted file mode 100644
index 39c1bd9a..00000000
--- a/zh-cn/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
+++ /dev/null
@@ -1,8643 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 使用基于优势比的偏好优化（ORPO）进行偏好对齐\n",
-    "\n",
-    "本教程将会带你使用基于优势比的偏好优化（ORPO）算法去微调一个语言模型。这里我们使用 SmolLM2-135M 模型，因为它还**没有**经过 SFT 的训练（所以这个模型是不能直接进行 DPO 训练的）。这里你就不能使用你在[第一章](../../1_instruction_tuning/notebooks/sft_finetuning_example_cn.ipynb)训练好的模型了。\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "     <h2 style='margin: 0;color:blue'>练习：借助 ORPOTrainer 对齐 SmolLM2 模型</h2>\n",
-    "     <p>从 Hugging Face hub 上找一个数据集并在上面对齐模型。</p> \n",
-    "     <p><b>难度等级</b></p>\n",
-    "     <p>🐢 使用 `trl-lib/ultrafeedback_binarized` 数据集</p>\n",
-    "     <p>🐕 尝试使用 `argilla/ultrafeedback-binarized-preferences` 数据集</p>\n",
-    "     <p>🦁 尝试使用 `mlabonne/orpo-dpo-mix-40k` 数据集的一个子集进行训练</p>\n",
-    "</div>\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 准备好需要的 Python 库\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "EwNxh0pCQhfI",
-    "outputId": "e4c288e1-2fa3-4717-d9dd-ae3ec0b91e3b"
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/ben/code/smol-course/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "import os\n",
-    "from datasets import load_dataset\n",
-    "from transformers import (\n",
-    "    AutoModelForCausalLM,\n",
-    "    AutoTokenizer,\n",
-    ")\n",
-    "from trl import ORPOConfig, ORPOTrainer, setup_chat_format\n",
-    "\n",
-    "# Authenticate to Hugging Face\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 配置数据集及其格式"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Generating train split: 100%|██████████| 62135/62135 [00:00<00:00, 450465.27 examples/s]\n",
-      "Generating test split: 100%|██████████| 1000/1000 [00:00<00:00, 382866.64 examples/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Load dataset\n",
-    "\n",
-    "# TODO: 🦁🐕 你也可以使用你感兴趣的其他数据集\n",
-    "dataset = load_dataset(path=\"trl-lib/ultrafeedback_binarized\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO: 🐕 如果你的数据集不是对话列表形式，你可以使用 `process_dataset` 函数进行转化"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 定义好你要使用的模型"
-   ]
-  },
-  {
-   "cell_type": "code",
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-    "outputId": "62246d3e-5e5d-4910-a9d9-dd1d24fd3c01"
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-   "outputs": [],
-   "source": [
-    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
-    "\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "# Model to fine-tune\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=model_name,\n",
-    "    torch_dtype=torch.float32,\n",
-    ").to(device)\n",
-    "model.config.use_cache = False\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
-    "model, tokenizer = setup_chat_format(model, tokenizer)\n",
-    "\n",
-    "# Set our name for the finetune to be saved &/ uploaded to\n",
-    "finetune_name = \"SmolLM2-FT-ORPO\"\n",
-    "finetune_tags = [\"smol-course\", \"module_1\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 开始 ORPO 训练"
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-    "orpo_args = ORPOConfig(\n",
-    "    # 使用较小的学习率可以预防“灾难性遗忘”\n",
-    "    learning_rate=8e-6,\n",
-    "    # 学习率衰减策略\n",
-    "    lr_scheduler_type=\"linear\",\n",
-    "    # 提示词 + 回答 的最大长度（最大 token 数量）\n",
-    "    max_length=1024,\n",
-    "    # 输入的提示词的最大长度\n",
-    "    max_prompt_length=512,\n",
-    "    # 优势比损失函数的权重（论文中的 λ 超参数）\n",
-    "    beta=0.1,\n",
-    "    # 单个 GPU 的 batch size\n",
-    "    per_device_train_batch_size=2,\n",
-    "    per_device_eval_batch_size=2,\n",
-    "    # 每多少步前向过程才更新一次参数，调节这里可以稳定训练\n",
-    "    gradient_accumulation_steps=4,\n",
-    "    # 使用 paged_adamw_8bit 优化器可以为 CUDA 节省内存，如果是 CPU 或 MPS 设备，则退回到使用 adamw_torch 优化器\n",
-    "    optim=\"paged_adamw_8bit\" if device == \"cuda\" else \"adamw_torch\",\n",
-    "    # 训练的 epoch 数量\n",
-    "    num_train_epochs=1,\n",
-    "    # 训练过程中验证的策略\n",
-    "    evaluation_strategy=\"steps\",\n",
-    "    # 每完成 20% 训练就验证一次\n",
-    "    eval_steps=0.2,\n",
-    "    # log 打印频率\n",
-    "    logging_steps=1,\n",
-    "    # 学习率 warmup 的步数\n",
-    "    warmup_steps=10,\n",
-    "    # 这里我们不使用 wandb 或 tensorboard，不使用外部打印 log 的功能\n",
-    "    report_to=None,\n",
-    "    # 保存训练结果的目录\n",
-    "    output_dir=\"./results/\",\n",
-    "    # 如果是苹果电脑，将会使用 MPS 硬件加速\n",
-    "    use_mps_device=device == \"mps\",\n",
-    "    hub_model_id=finetune_name,\n",
-    ")"
-   ]
-  },
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-   "execution_count": null,
-   "metadata": {},
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-    "trainer = ORPOTrainer(\n",
-    "    model=model,\n",
-    "    args=orpo_args,\n",
-    "    train_dataset=dataset[\"train\"],\n",
-    "    eval_dataset=dataset[\"test\"],\n",
-    "    processing_class=tokenizer,\n",
-    ")"
-   ]
-  },
-  {
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-   "execution_count": null,
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-    "trainer.train()  # Train the model\n",
-    "\n",
-    "# Save the model\n",
-    "trainer.save_model(f\"./{finetune_name}\")\n",
-    "\n",
-    "# Save to the huggingface hub if login (HF_TOKEN is set)\n",
-    "if os.getenv(\"HF_TOKEN\"):\n",
-    "    trainer.push_to_hub(tags=finetune_tags)"
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-    "## 💐 完成\n",
-    "\n",
-    "本教程带你一步步地学习了用 `ORPOTrainer` 去训练 `HuggingFaceTB/SmolLM2-135M` 模型。利用这些步骤，你可以高效快速地进行特定任务领域的偏好优化训练。感兴趣的话，你还可以：\n",
-    "\n",
-    "- 尝试本教程难度等级更高的任务\n",
-    "- 在 GitHub 上评审别人的 Pull Request\n",
-    "- 通过 Issue 或 PR 提出课程改进的意见"
-   ]
-  }
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diff --git a/zh-cn/2_preference_alignment/orpo.md b/zh-cn/2_preference_alignment/orpo.md
deleted file mode 100644
index a8808871..00000000
--- a/zh-cn/2_preference_alignment/orpo.md
+++ /dev/null
@@ -1,83 +0,0 @@
-# 基于优势比的偏好优化（ORPO）
-
-基于优势比的偏好优化（Odds Ratio Preference Optimization）或 ORPO，是一种更新颖的偏好对齐方法，它把微调和偏好对齐结合，组成一个统一的过程。这个算法相比于 RLHF 和 DPO 有着更高的效率和更好的性能。
-
-## 理解 ORPO
-
-诸如 DPO 的对齐方法一般包含两个步骤：使用 SFT 先让模型适配如这个领域或回答格式，然后进行偏好对齐训练。虽然 SFT 已经将模型对齐到了特定任务领域，但模型不可避免可能会产生我们不期望的回答，所以我们还需要进行下一步的偏好对齐。ORPO 则合并了这两个步骤。下图取自 ORPO 论文，对比了 RLHF、DPO 和 ORPO 的差异：
-
-![Alignment Techniques Comparison](https://argilla.io/images/blog/mantisnlp-rlhf/part-8-alignments.png)
-*三种对齐算法的对比*
-
-## ORPO 工作原理
-
-ORPO 训练使用的数据集和 DPO 相似：针对一个输入的问题包含两个可能输出：一个是“倾向的输出”，另一个是“不倾向的输出”。不同的是，ORPO 直接将偏好对齐加入到 SFT 中。这一整体性方法使得它无需 DPO 中的参考模型，同时也更高效、节省内存。
-
-ORPO 的损失函数包含两个部分：
-
-1. **SFT Loss**：这里使用标准的负对数似然函数，和 DPO 中的类似，用于扩大想要的 token 的生成概率。这个损失函数也有助于模型保持通用的语言能力。
-2. **Odds Ratio Loss**：这是新提出的损失函数。由于上述 SFT Loss 不能惩罚不想要的输出，所以这个函数在激励倾向的输出的同时，也惩罚不倾向的输出。具体来说，这里定义了计算优势比（Odds Ratio）的公式，通过抬高倾向输出和不倾向输出两者的优势比比值，在奖励倾向输出的同时，也压低不倾向输出的生成概率。
-
-在两个损失函数共同作用下，模型不仅被适配进了相应的任务领域，也压低了不倾向的回答的生成概率。其中，优势比这个机制提供了一个很直观的方法，模拟了倾向回答和不倾向回答之间的差异程度。你也可以阅读 [ORPO 的论文](https://arxiv.org/abs/2403.07691)，进一步了解其中的数学理论。如果你对具体的实现感兴趣，你可以阅读 [TRL 中关于这部分的实现](https://github.com/huggingface/trl/blob/b02189aaa538f3a95f6abb0ab46c0a971bfde57e/trl/trainer/orpo_trainer.py#L660)。
-
-## 训练结果
-
-ORPO 在很多测试基准上都取得了不错的效果。以下是它在 MT-Bench 测试基准上的结果（根据任务类别划分）：
-
-![MT-Bench Results](https://argilla.io/images/blog/mantisnlp-rlhf/part-8-mtbench.png)
-*Mistral-ORPO 模型在 MT-Bench 不同任务领域的结果*
-
-在 AlpacaEval 2.0 上，ORPO 展现了超越其它对齐算法的效果：
-
-![AlpacaEval Results](https://argilla.io/images/blog/mantisnlp-rlhf/part-8-winrate.png)
-*不同对齐算法在 AlpacaEval 2.0 的得分*
-
-相较于 SFT 加 DPO 的做法，ORPO 通过去除参考模型、减半前向推理的策略，大大降低了计算资源的要求。同时，训练过程也更稳定，需要调节的超参数也更少。在性能上，ORPO 对人类偏好的适配做得也更好。
-
-## 代码实现
-
-成功训练 ORPO 也极度依赖高质量数据集。所以标注训练数据时，我们也需要清晰明确的标注标准，确保对话场景的多样性，同时倾向和不倾向的回答需要分布均匀。
-
-### 用 TRL 实现 ORPO
-
-以下代码提供了用 TRL 实现 ORPO 的基本示例：
-
-```python
-from trl import ORPOConfig, ORPOTrainer
-
-# Configure ORPO training
-orpo_config = ORPOConfig(
-    learning_rate=1e-5,
-    per_device_train_batch_size=4,
-    gradient_accumulation_steps=4,
-    max_steps=1000,
-    orpo_alpha=1.0,  # Controls strength of preference optimization
-    orpo_beta=0.1,   # Temperature parameter for odds ratio
-)
-
-# Initialize trainer
-trainer = ORPOTrainer(
-    model=model,
-    args=orpo_config,
-    train_dataset=dataset,
-    tokenizer=tokenizer,
-)
-
-# Start training
-trainer.train()
-```
-
-关键参数：
-- `orpo_alpha`：用来控制偏好优化部分的权重
-- `orpo_beta`：计算优势比（Odds Ratio）时的 Temperature 参数
-- `learning_rate`：这里需要用较小的学习率，用来防治灾难性遗忘（catastrophic forgetting）
-- `gradient_accumulation_steps`：调节这个也能稳定训练
-
-## 接下来的学习
-
-⏩ 学习 [ORPO 教程](./notebooks/orpo_finetuning_example.ipynb) 来实践 ORPO 算法。
-
-## 学习资源
-- [ORPO 论文](https://arxiv.org/abs/2403.07691)
-- [TRL 官方文档](https://huggingface.co/docs/trl/index)
-- [Argilla RLHF Guide](https://argilla.io/blog/mantisnlp-rlhf-part-8/) 
\ No newline at end of file
diff --git a/zh-cn/3_parameter_efficient_finetuning/README.md b/zh-cn/3_parameter_efficient_finetuning/README.md
deleted file mode 100644
index 15d13900..00000000
--- a/zh-cn/3_parameter_efficient_finetuning/README.md
+++ /dev/null
@@ -1,39 +0,0 @@
-# 高效的参数微调（PEFT）
-
-随着语言模型越来越大，使用传统的模型微调方法去微调 LLM 已经变得越来越难了。举例来说，微调一个 1.7B 参数量的模型需要把所有参数都放进 GPU 显存、保存模型优化和状态信息，甚至还需要保存模型备份，这就需要很大的 GPU 显存使用了。同时，微调所有参数还有很大的“灾难性遗忘”风险，可能损失模型原有的能力。针对此问题，高效的参数微调（Parameter-efficient fine-tuning 或 PEFT）被提出。它在微调模型时，保留大部分参数不变，只微调一小部分参数，大大节省了计算资源的需求。
-
-传统微调需要更新所有模型参数，对大模型很不现实。而 PEFT 相关方法则发现，仅更新一小部分参数，就足以对模型进行适配，达到微调所期待的效果。这部分需要更新的参数甚至还不到总参数量的 1%。这一重大改进使得以下操作成为可能：
-
-- 在 GPU 显存受限的消费级显卡上微调 LLM
-- 高效地为不同任务领域保存不同的微调结果
-- 在数据量不足的微调场景下也可保持很好的泛化性
-- 微调训练耗时更少
-  
-## PEFT 相关算法
-
-在本章教程中，我们主要讲解两种比较常用的 PEFT 算法：
-
-### 1️⃣ LoRA (Low-Rank Adaptation)
-
-LoRA 可以说是最常用的 PEFT 算法了，它为高效模型微调提供了一个非常优雅的解决方案。LoRA 在需要更新的参数（一般是 attention layers 的参数）上插入可以训练的参数矩阵，训练时仅训练这部分参数。当模型训练好后，我们会利用这部分参数对原有模型进行重参数化（re-parameterization）。这样可以不改变 LLM 的整体结构和参数数量。通过这种方法，需要更新的参数量能至少减少 90%，同时性能也不差于全量参数微调。我们将在 [LoRA 低秩分解](./lora_adapters_cn.md)部分进一步讲解。
-
-### 2️⃣ Prompt Tuning
-
-Prompt Tuning 则更加轻量化。它通过在输入部分加入**可训练的 token** 来微调，而不是改变模型的参数。Prompt Tuning 没有 LoRA 那么常用，但对于适配模型到新的任务领域来说，是个非常有用的技术。我们将在 [Prompt Tuning](./prompt_tuning_cn.md) 部分进一步讲解。
-
-## 实践练习
-
-| 标题 | 简介 | 习题 | 链接 | Colab |
-|-------|-------------|----------|------|-------|
-| LoRA 微调 | 学习使用 LoRA adapters 微调模型 | 🐢 用 LoRA 训练一个模型 <br>🐕 试验不同低秩值的效果 <br>🦁 与全量参数微调的效果进行对比 | [Notebook](./notebooks/finetune_sft_peft.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-| 载入 LoRA Adapter | 学习如何加载并使用 LoRA adapters | 🐢 加载训练好的 adapter<br>🐕 将 adapter 融入原有模型中 <br>🦁 实现不同 adapter 的切换 | [Notebook](./notebooks/load_lora_adapter.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-<!-- | Prompt Tuning | Learn how to implement prompt tuning | 🐢 Train soft prompts<br>🐕 Compare different initialization strategies<br>🦁 Evaluate on multiple tasks | [Notebook](./notebooks/prompt_tuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/prompt_tuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | -->
-
-## 参考资料
-- [PEFT 代码库官方文档](https://huggingface.co/docs/peft)
-- [LoRA 论文](https://arxiv.org/abs/2106.09685)
-- [QLoRA 论文](https://arxiv.org/abs/2305.14314)
-- [Prompt Tuning 论文](https://arxiv.org/abs/2104.08691)
-- [Hugging Face PEFT 代码库相关博客](https://huggingface.co/blog/peft)
-- [文章：How to Fine-Tune LLMs in 2024 with Hugging Face](https://www.philschmid.de/fine-tune-llms-in-2024-with-trl) 
-- [TRL 代码库官方文档](https://huggingface.co/docs/trl/index)
diff --git a/zh-cn/3_parameter_efficient_finetuning/images/lora_adapter.png b/zh-cn/3_parameter_efficient_finetuning/images/lora_adapter.png
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diff --git a/zh-cn/3_parameter_efficient_finetuning/lora_adapters.md b/zh-cn/3_parameter_efficient_finetuning/lora_adapters.md
deleted file mode 100644
index 147cd872..00000000
--- a/zh-cn/3_parameter_efficient_finetuning/lora_adapters.md
+++ /dev/null
@@ -1,126 +0,0 @@
-# LoRA（低秩分解）
-
-LoRA 是最常用的 PEFT 算法。它针对 attention 层的权重加入参数量较少、低秩分解过的参数矩阵，用模型原有参数和低秩分解参数计算出的激活值之和代表微调过后的激活值。这样当我们只更新低秩分解过的参数矩阵时，我们需要训练的参数量能减少大约 90%。
-
-## 理解 LoRA
-
-LoRA 全称是 Low-Rank Adaptation，或叫做“低秩分解”。它的基本做法是，在微调时，冻结所有预训练模型的参数，同时为需要微调的模型层注入额外的可训练的参数矩阵（通常称之为 Adapter）。通过对需要微调的层的参数矩阵进行低秩分解，可以得到两个参数量较小的新参数矩阵；而这一层的前向计算激活值则可以用“原有参数矩阵计算出的激活值”加上“低秩分解出的两个矩阵计算出的激活值”而得到。训练时，我们只需要训练两个低秩分解的矩阵即可，这样极大减少了所需微调的参数量，同时也能保持原有模型性能。例如，如果我们用 LoRA 微调 GPT-3 175B 模型，相比于全量参数的微调，LoRA 需要参与训练的参数量可减少至万分之一、GPU 现存需求可减少至三分之一。感兴趣的读者可以阅读 [LoRA 的论文](https://arxiv.org/pdf/2106.09685)。
-
-一般而言，LoRA 都是对 transformer 层的参数进行低秩分解的，尤其是在与注意力机制相关的参数上。在推理过程中，这些 adapter 的参数可以被直接融合进模型中，得到与原模型结构完全一致的新模型，无需增加新的层。得益于此，LoRA 尤其适合在低计算资源情况下，将大模型适配进入特定的任务领域。
-
-## 如何载入 LoRA 的 Adapters
-
-如果你使用 `peft` 库，你可以用 `load_adapter()` 载入 LoRA Adapters。这对你尝试不同的 adapter 非常管用，因为它还不会将参数融合进原模型。你可以使用 `set_adapter()` 指定哪个 LoRA Adapter 在生效。如果想返回原模型，你可以使用 `unload()` 卸载所有 LoRA 参数。这种设定使得在不同任务领域间切换模型变得非常容易。
-
-```python
-from transformers import AutoModelForCausalLM
-from peft import PeftModel
-
-base_model = AutoModelForCausalLM.from_pretrained("<base_model_name>")
-peft_model_id = "<peft_adapter_id>"
-model = PeftModel.from_pretrained(base_model, peft_model_id)
-```
-
-![lora_load_adapter](./images/lora_adapter.png)
-
-## 将 LoRA 参数融入原模型
-
-如果在 LoRA 微调结束后，你想直接获取一套新的模型参数，而不是每次使用的时候都需要加载 LoRA 的 Adapter，你可以直接将 LoRA 的参数融入原模型中。
-
-融合的时候，我们首先需要注意内存的管理以及参数的精度。因为我们要同时载入原模型和 LoRA 参数，需要注意 GPU 或 CPU 的内存是否够用。在 `transformers` 中使用 `device_map="auto"` 可以替我们自动进行内存管理。同时，要注意原模型、LoRA 参数的精度需保持一致。融合后，检查模型输出是否和未融合是一致也很重要。
-
-## 代码实现
-
-在 `notebooks/` 目录下，有 PEFT 相关方法的实践教程以及练习题。我们首先会在 `load_lora_adapter_example.ipynb` 学习加载 LoRA Adapter 相关的内容，然后在 `lora_finetuning.ipynb` 中，我们将学习如果用 LoRA 进行 SFT。
-
-一个比较好的 LoRA 训练流程应该是，首先从较低的秩开始，一般是 4 到 8，同时观察训练损失值。使用验证集及时查看避免过拟合。不同任务可能有较大差异，所有还是要以实际实验现象为准。
-
-## OLoRA
-
-[OLoRA](https://arxiv.org/abs/2406.01775) 使用 QR 分解来初始化 LoRA 的 Adapter。该算法对原有的参数矩阵 W 分解为 Q 和 R 两个矩阵，其中 Q 矩阵包含 W 矩阵的 r 个正交向量，使得优化能够在一个较好的子空间进行。这样可以很大地提升收敛速度，同时也达到了非常好的效果。
-
-##  TRL 与 PEFT 结合使用
-
-PEFT 也可以和 TRL 库一起使用，这对 RLHF（Reinforcement Learning from Human Feedback）尤其实用。
-
-```python
-from peft import LoraConfig
-from transformers import AutoModelForCausalLM
-
-# Load model with PEFT config
-lora_config = LoraConfig(
-    r=16,
-    lora_alpha=32,
-    lora_dropout=0.05,
-    bias="none",
-    task_type="CAUSAL_LM"
-)
-
-# Load model on specific device
-model = AutoModelForCausalLM.from_pretrained(
-    "your-model-name",
-    load_in_8bit=True,  # Optional: use 8-bit precision
-    device_map="auto",
-    peft_config=lora_config
-)
-```
-
-在上述代码中，我们用 `device_map="auto"` 自动分配模型到正确的计算设备上。关于具体计算设备，你也可以手动修改：`device_map={"": device_index}`；当然你也可以扩大训练规模，如实用多 GPU 训练等。
-
-## 基本的参数融合实现
-
-
-
-训练好 LoRA adapter 后，将权重融合回原模型的方法如下：
-
-
-```python
-import torch
-from transformers import AutoModelForCausalLM
-from peft import PeftModel
-
-# 1. Load the base model
-base_model = AutoModelForCausalLM.from_pretrained(
-    "base_model_name",
-    torch_dtype=torch.float16,
-    device_map="auto"
-)
-
-# 2. Load the PEFT model with adapter
-peft_model = PeftModel.from_pretrained(
-    base_model,
-    "path/to/adapter",
-    torch_dtype=torch.float16
-)
-
-# 3. Merge adapter weights with base model
-try:
-    merged_model = peft_model.merge_and_unload()
-except RuntimeError as e:
-    print(f"Merging failed: {e}")
-    # Implement fallback strategy or memory optimization
-
-# 4. Save the merged model
-merged_model.save_pretrained("path/to/save/merged_model")
-```
-
-保存的时候，你可能也需要保存 tokenizer 到相应目录。
-
-```python
-# Save both model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained("base_model_name")
-merged_model.save_pretrained("path/to/save/merged_model")
-tokenizer.save_pretrained("path/to/save/merged_model")
-```
-
-## 接下来
-
-⏩ 继续学习 [Prompt Tuning](prompt_tuning_cn.md)，了解这种微调方式如何运作。
-
-⏩ 实践 [加载 LoRA Adapters 的教程](./notebooks/load_lora_adapter_cn.ipynb) 练习加载 LoRA adapters。
-
-# 学习资源
-
-- [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/pdf/2106.09685)
-- [PEFT 官方文档](https://huggingface.co/docs/peft)
-- [Hugging Face 有关 PEFT 的博客](https://huggingface.co/blog/zh/peft)
diff --git a/zh-cn/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb b/zh-cn/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
deleted file mode 100644
index 4e0e0bd2..00000000
--- a/zh-cn/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
+++ /dev/null
@@ -1,519 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "z-6LLOPZouLg"
-   },
-   "source": [
-    "# 在 TRL 框架下用 LoRA 微调大语言模型\n",
-    "\n",
-    "这个 notebook 展示如何用 LoRA 高效微调大语言模型。LoRA 是一种高效的参数微调方法，有如下优点：\n",
-    "- 不更新预训练模型权重\n",
-    "- 仅在注意力层添加少量低秩分解矩阵作为训练参数\n",
-    "- 基本能减少 90% 训练参数\n",
-    "- 能保留模型原有的能力\n",
-    "\n",
-    "本文涵盖这些步骤：\n",
-    "1. 配置开发环境、设定 LoRA 相关配置\n",
-    "2. 准备数据集\n",
-    "3. 使用 `trl` 框架下的 `SFTTrainer` 进行 LoRA 微调\n",
-    "4. 测试模型性能、学习加载 adapter\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "fXqd9BXgouLi"
-   },
-   "source": [
-    "## 1. 配置开发环境\n",
-    "\n",
-    "我们首先需要安装 PyTorch 和 Hugging Face 相关的库，这包括 `trl`、`transformers`、`datasets`。其中 `trl` 基于 `transformers` 和 `datasets`，用以微调模型、进行 RLHF、对齐 LLM 等。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "tKvGVxImouLi"
-   },
-   "outputs": [],
-   "source": [
-    "# Install the requirements in Google Colab\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "# Authenticate to Hugging Face\n",
-    "\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()\n",
-    "\n",
-    "# for convenience you can create an environment variable containing your hub token as HF_TOKEN"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "XHUzfwpKouLk"
-   },
-   "source": [
-    "## 2. 载入数据集"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "id": "z4p6Bvo7ouLk"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['full_topic', 'messages'],\n",
-       "        num_rows: 2260\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['full_topic', 'messages'],\n",
-       "        num_rows: 119\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Load a sample dataset\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "# TODO: 你也可以用自己的数据集\n",
-    "dataset = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")\n",
-    "dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "9TOhJdtsouLk"
-   },
-   "source": [
-    "## 3. 在 `trl` 框架下用 `SFTTrainer` 实现大语言模型的 LoRA 微调\n",
-    "\n",
-    "在 `trl` 中，[SFTTrainer](https://huggingface.co/docs/trl/sft_trainer) 通过 [PEFT](https://huggingface.co/docs/peft/en/index) 提供了 LoRA Adapter 的集成。这样的设定有以下几个好处：\n",
-    "\n",
-    "1. **高效利用内存**：\n",
-    "   - 仅 Adapter 的参数会保存在 GPU 显存中\n",
-    "   - 原模型参数被冻结，所以可以用低精度载入\n",
-    "   - 这使得在消费级显卡上也可以微调大模型\n",
-    "2. **训练层面**：\n",
-    "   - 原生 PEFT/LoRA 集成，用户开发所需代码量少\n",
-    "   - 支持 QLoRA（量化版 LoRA），可以进一步减少内存使用\n",
-    "\n",
-    "3. **Adapter 管理**：\n",
-    "   - 可以方便地训练过程中保存 Adapter\n",
-    "   - 可以方便地把 Adapter 融合进原模型\n",
-    "\n",
-    "本文将会进行 LoRA 微调，当然你也可以尝试 4-bit 量化来进一步减少内存使用。配置步骤包含以下几步：\n",
-    "1. 定义好 LoRA 的相关参数（主要是 rank、alpha、dropout）\n",
-    "2. 创建一个 SFTTrainer 的实例\n",
-    "3. 训练模型、保存 adapter 的参数"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import necessary libraries\n",
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "from datasets import load_dataset\n",
-    "from trl import SFTConfig, SFTTrainer, setup_chat_format\n",
-    "import torch\n",
-    "\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "# Load the model and tokenizer\n",
-    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
-    "\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=model_name\n",
-    ").to(device)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
-    "\n",
-    "# Set up the chat format\n",
-    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n",
-    "\n",
-    "# Set our name for the finetune to be saved &/ uploaded to\n",
-    "finetune_name = \"SmolLM2-FT-MyDataset\"\n",
-    "finetune_tags = [\"smol-course\", \"module_1\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ZbuVArTHouLk"
-   },
-   "source": [
-    "由于 `SFTTrainer`  原生支持 `peft`，使用 LoRA 训练 LLM 就变得非常简单。我们需要配置的只有 `LoraConfig`。\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>练习：为微调定义好 LoRA 相关参数</h2>\n",
-    "    <p>从 Hugging Face hub 找一个合适的数据然后微调模型 </p> \n",
-    "    <p><b>难度等级</b></p>\n",
-    "    <p>🐢 使用默认的 LoRA 超参数直接微调</p>\n",
-    "    <p>🐕 改变一些超参数，学习通过 weights & biases 平台查看</p>\n",
-    "    <p>🦁 改变一些超参数，训练后查看这些改变是否影响了推理性能</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "blDSs9swouLk"
-   },
-   "outputs": [],
-   "source": [
-    "from peft import LoraConfig\n",
-    "\n",
-    "# TODO: 配置 LoRA 参数\n",
-    "# r: 低秩分解的秩，越小则需要训练的参数量越少\n",
-    "rank_dimension = 6\n",
-    "# lora_alpha: 将训练好的参数加到原模型上时的缩放倍数，越大则微调参数作用越明显\n",
-    "lora_alpha = 8\n",
-    "# lora_dropout: LoRA 相关层的 dropout，可以用来应对过拟合\n",
-    "lora_dropout = 0.05\n",
-    "\n",
-    "peft_config = LoraConfig(\n",
-    "    r=rank_dimension,  # 一般选择 4 到 32\n",
-    "    lora_alpha=lora_alpha,  # 一般是 rank 的 2 倍\n",
-    "    lora_dropout=lora_dropout,  # Dropout probability for LoRA layers\n",
-    "    bias=\"none\",  # Bias type for LoRA. the corresponding biases will be updated during training.\n",
-    "    target_modules=\"all-linear\",  # 模型哪些层会添加 LoRA Adapter\n",
-    "    task_type=\"CAUSAL_LM\",  # Task type for model architecture\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "l5NUDPcaouLl"
-   },
-   "source": [
-    "此外我们还需定义训练的超参数（`TrainingArguments`）。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "NqT28VZlouLl"
-   },
-   "outputs": [],
-   "source": [
-    "# Training configuration\n",
-    "# Hyperparameters based on QLoRA paper recommendations\n",
-    "args = SFTConfig(\n",
-    "    # Output settings\n",
-    "    output_dir=finetune_name,  # Directory to save model checkpoints\n",
-    "    # Training duration\n",
-    "    num_train_epochs=1,  # Number of training epochs\n",
-    "    # Batch size settings\n",
-    "    per_device_train_batch_size=2,  # Batch size per GPU\n",
-    "    gradient_accumulation_steps=2,  # Accumulate gradients for larger effective batch\n",
-    "    # Memory optimization\n",
-    "    gradient_checkpointing=True,  # Trade compute for memory savings\n",
-    "    # Optimizer settings\n",
-    "    optim=\"adamw_torch_fused\",  # Use fused AdamW for efficiency\n",
-    "    learning_rate=2e-4,  # Learning rate (QLoRA paper)\n",
-    "    max_grad_norm=0.3,  # Gradient clipping threshold\n",
-    "    # Learning rate schedule\n",
-    "    warmup_ratio=0.03,  # Portion of steps for warmup\n",
-    "    lr_scheduler_type=\"constant\",  # Keep learning rate constant after warmup\n",
-    "    # Logging and saving\n",
-    "    logging_steps=10,  # Log metrics every N steps\n",
-    "    save_strategy=\"epoch\",  # Save checkpoint every epoch\n",
-    "    # Precision settings\n",
-    "    bf16=True,  # Use bfloat16 precision\n",
-    "    # Integration settings\n",
-    "    push_to_hub=False,  # Don't push to HuggingFace Hub\n",
-    "    report_to=None,  # Disable external logging\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "cGhR7uFBouLl"
-   },
-   "source": [
-    "配置完毕，我们可以创建 `SFTTrainer` 来训练模型了。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "M00Har2douLl"
-   },
-   "outputs": [],
-   "source": [
-    "max_seq_length = 1512  # max sequence length for model and packing of the dataset\n",
-    "\n",
-    "# Create SFTTrainer with LoRA configuration\n",
-    "trainer = SFTTrainer(\n",
-    "    model=model,\n",
-    "    args=args,\n",
-    "    train_dataset=dataset[\"train\"],\n",
-    "    peft_config=peft_config,  # LoRA configuration\n",
-    "    max_seq_length=max_seq_length,  # Maximum sequence length\n",
-    "    tokenizer=tokenizer,\n",
-    "    packing=True,  # Enable input packing for efficiency\n",
-    "    dataset_kwargs={\n",
-    "        \"add_special_tokens\": False,  # Special tokens handled by template\n",
-    "        \"append_concat_token\": False,  # No additional separator needed\n",
-    "    },\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "zQ_kRN24ouLl"
-   },
-   "source": [
-    "通过启动 `train()` 函数，我们开始训练。本次训练包含 3 个 epoch。由于我们用了 PEFT，我们可以在训练过程中或结束后，只保存 adapter 的参数，无需保存原模型参数。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Tq4nIYqKouLl"
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "300e5dfbb4b54750b77324345c7591f9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/72 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "TrainOutput(global_step=72, training_loss=1.6402628521124523, metrics={'train_runtime': 195.2398, 'train_samples_per_second': 1.485, 'train_steps_per_second': 0.369, 'total_flos': 282267289092096.0, 'train_loss': 1.6402628521124523, 'epoch': 0.993103448275862})"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# start training, the model will be automatically saved to the hub and the output directory\n",
-    "trainer.train()\n",
-    "\n",
-    "# save model\n",
-    "trainer.save_model()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "y4HHSYYzouLl"
-   },
-   "source": [
-    "我们模型使用了 Flash Attention 加速训练。在当前数据集（15k 的样本量）训练了 3 轮，在一个 `g5.2xlarge` 机器上用了 4 小时 14 分钟 36 秒。该机器报价 `1.21$/h`，所以我们总花费仅 `5.3$`。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "C309KsXjouLl"
-   },
-   "source": [
-    "### 将 LoRA Adapter 融入原模型\n",
-    "\n",
-    "训练 LoRA 时，我们只训练 adapter 里的参数，而不训练原模型。所以保存的参数也只有 adapter 里的参数（可能也就 2MB 到 10MB）。然而在部署阶段，你可能需要把 Adapter 融合进原模型：\n",
-    "1. **简化的部署流程**：仅载入一个模型参数文件即可，无需额外载入 adapter 参数文件\n",
-    "2. **推理速度提升**：adapter 引入的计算已经融合进了模型中\n",
-    "3. **框架的兼容性**：更能和服务框架适配"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from peft import AutoPeftModelForCausalLM\n",
-    "\n",
-    "\n",
-    "# Load PEFT model on CPU\n",
-    "model = AutoPeftModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=args.output_dir,\n",
-    "    torch_dtype=torch.float16,\n",
-    "    low_cpu_mem_usage=True,\n",
-    ")\n",
-    "\n",
-    "# Merge LoRA and base model and save\n",
-    "merged_model = model.merge_and_unload()\n",
-    "merged_model.save_pretrained(\n",
-    "    args.output_dir, safe_serialization=True, max_shard_size=\"2GB\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "-yO6E9quouLl"
-   },
-   "source": [
-    "## 3. 测试模型、进行推理\n",
-    "\n",
-    "训练结束后，我们可能需要测试模型。可以从数据集找一些样本，然后看看模型在这些样本上的性能。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "id": "I5B494OdouLl"
-   },
-   "outputs": [],
-   "source": [
-    "# free the memory again\n",
-    "del model\n",
-    "del trainer\n",
-    "torch.cuda.empty_cache()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "P1UhohVdouLl"
-   },
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from peft import AutoPeftModelForCausalLM\n",
-    "from transformers import AutoTokenizer, pipeline\n",
-    "\n",
-    "# Load Model with PEFT adapter\n",
-    "tokenizer = AutoTokenizer.from_pretrained(finetune_name)\n",
-    "model = AutoPeftModelForCausalLM.from_pretrained(\n",
-    "    finetune_name, device_map=\"auto\", torch_dtype=torch.float16\n",
-    ")\n",
-    "pipe = pipeline(\n",
-    "    \"text-generation\", model=merged_model, tokenizer=tokenizer, device=device\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "99uFDAuuouLl"
-   },
-   "source": [
-    "现在我们找些样本来测试："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "id": "-shSmUbvouLl",
-    "outputId": "16d97c61-3b31-4040-c780-3c4de75c3824"
-   },
-   "outputs": [],
-   "source": [
-    "prompts = [\n",
-    "    \"What is the capital of Germany? Explain why thats the case and if it was different in the past?\",\n",
-    "    \"Write a Python function to calculate the factorial of a number.\",\n",
-    "    \"A rectangular garden has a length of 25 feet and a width of 15 feet. If you want to build a fence around the entire garden, how many feet of fencing will you need?\",\n",
-    "    \"What is the difference between a fruit and a vegetable? Give examples of each.\",\n",
-    "]\n",
-    "\n",
-    "\n",
-    "def test_inference(prompt):\n",
-    "    prompt = pipe.tokenizer.apply_chat_template(\n",
-    "        [{\"role\": \"user\", \"content\": prompt}],\n",
-    "        tokenize=False,\n",
-    "        add_generation_prompt=True,\n",
-    "    )\n",
-    "    outputs = pipe(\n",
-    "        prompt,\n",
-    "    )\n",
-    "    return outputs[0][\"generated_text\"][len(prompt) :].strip()\n",
-    "\n",
-    "\n",
-    "for prompt in prompts:\n",
-    "    print(f\"    prompt:\\n{prompt}\")\n",
-    "    print(f\"    response:\\n{test_inference(prompt)}\")\n",
-    "    print(\"-\" * 50)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>额外练习：载入自己训练的 LoRA Adapter</h2>\n",
-    "    <p>结束本教程后，你可以使用学到的技术，自己训练一个 LoRA，然后载入 LoRA Adapter</p> \n",
-    "</div>"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.10"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/zh-cn/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb b/zh-cn/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
deleted file mode 100644
index b879831b..00000000
--- a/zh-cn/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
+++ /dev/null
@@ -1,5061 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "otj46qRbtpnd",
-        "outputId": "0fa81296-44b7-479d-e03b-7ef196cc89e4"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install -q bitsandbytes datasets accelerate loralib\n",
-        "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git"
-      ]
-    },
-    {
-      "cell_type": "code",
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-        "id": "cg3fiQOvmI3Q",
-        "outputId": "92fcd520-ad2c-4488-8f66-664ffc566b89"
-      },
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
-        "import torch\n",
-        "import torch.nn as nn\n",
-        "import bitsandbytes as bnb\n",
-        "from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "S65GcxNGA9kz"
-      },
-      "source": [
-        "## 从 HuggingFace Hub 加载 adapters\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 517,
-          "referenced_widgets": [
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-          ]
-        },
-        "id": "hsD1VKqeA62Z",
-        "outputId": "83ecbf31-3f84-4b8e-d667-10317f7adcec"
-      },
-      "outputs": [],
-      "source": [
-        "import torch\n",
-        "import transformers\n",
-        "from datasets import load_dataset\n",
-        "from peft import LoraConfig, get_peft_model\n",
-        "from peft import PeftModel, PeftConfig\n",
-        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-        "\n",
-        "peft_model_id = \"ybelkada/opt-6.7b-lora\"\n",
-        "config = PeftConfig.from_pretrained(peft_model_id)\n",
-        "model = AutoModelForCausalLM.from_pretrained(\n",
-        "    config.base_model_name_or_path,\n",
-        "    return_dict=True,\n",
-        "    load_in_8bit=True,\n",
-        "    device_map=\"auto\",\n",
-        ")\n",
-        "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
-        "\n",
-        "# 载入 Lora \n",
-        "model = PeftModel.from_pretrained(model, peft_model_id)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "MDqJWba-tpnv",
-        "outputId": "dd65644f-6921-46e1-920c-400bd7d888cf"
-      },
-      "outputs": [],
-      "source": [
-        "batch = tokenizer(\"Machine learning is: \", return_tensors='pt')\n",
-        "\n",
-        "with torch.cuda.amp.autocast():\n",
-        "  output_tokens = model.generate(**batch, max_new_tokens=50)\n",
-        "\n",
-        "print('\\n\\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True))"
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "machine_shape": "hm",
-      "provenance": []
-    },
-    "gpuClass": "standard",
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
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-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
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diff --git a/zh-cn/3_parameter_efficient_finetuning/prompt_tuning.md b/zh-cn/3_parameter_efficient_finetuning/prompt_tuning.md
deleted file mode 100644
index 2372354c..00000000
--- a/zh-cn/3_parameter_efficient_finetuning/prompt_tuning.md
+++ /dev/null
@@ -1,74 +0,0 @@
-# Prompt Tuning
-
-Prompt tuning 也是一个高效的微调手段，不同于微调模型参数，它改变的是输入模型的表征。具体来说，prompt tuning 在训练前会添加几个额外的 token，训练过程中，token 的 embedding 被更新，而模型参数一直保持不变。
-
-## 理解 Prompt Tuning
-
-Prompt tuning 通常把可以训练的连续向量（也称为soft prompts）接在输入文本的前面。与离散的文本提示词不同，这些 soft prompts 是通过训练过程中经反向传播更新的，而语言模型则在训练中不变。该方法在 [The Power of Scale for Parameter-Efficient Prompt Tuning](https://arxiv.org/abs/2104.08691) 中提出，展示了其在模型尺寸增大时的有效性：当模型尺寸在 10B 参数量左右时，prompt tuning 只更新 soft prompts 的几百个参数，即可达到模型全量微调的效果。
-
-Soft prompts 是模型的 embedding 空间中的一些连续数值的向量，它们会在微调过程中被更新。传统的 prompts 是一些离散的 tokens，在自然语言层面代表某些语义信息；soft prompt 则没有这些内在含义，经过参数更新后，它被用来从模型中引出一些特定行为。这种方法在多任务领域尤其有效，因为每个任务仅需保存一个 soft prompt 的向量（通常仅几百个参数），而不是对整个模型复制。这种做法不仅现存占用少，而且还能支持快速的任务切换，无需模型重新加载。
-
-## 训练过程
-
-Soft prompts 一般包含 8 到 32 个 tokens，它的初始化可以是随机初始化，也可以是来自现有的文本。初始化过程很影响训练，后者的方法通常效果更好。
-
-训练过程中，仅这些 soft prompts 的参数会被更新；训练用的损失函数也没有变化；但学习率需要我们认真调节，同时也建议观察 soft prompts 上面的梯度信息，以免训练失败。
-
-## 基于 PEFT 的代码实现
-
-使用 PEFT 库实现 prompt tuning 非常简单直接，以下是一个简单的例子：
-
-```python
-from peft import PromptTuningConfig, TaskType, get_peft_model
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-# Load base model
-model = AutoModelForCausalLM.from_pretrained("your-base-model")
-tokenizer = AutoTokenizer.from_pretrained("your-base-model")
-
-# Configure prompt tuning
-peft_config = PromptTuningConfig(
-    task_type=TaskType.CAUSAL_LM,
-    num_virtual_tokens=8,  # soft prompts 的 token 数量
-    prompt_tuning_init="TEXT",  # 初始化方法为从文本初始化
-    prompt_tuning_init_text="Classify if this text is positive or negative:",
-    tokenizer_name_or_path="your-base-model",
-)
-
-# Create prompt-tunable model
-model = get_peft_model(model, peft_config)
-```
-
-## 与其它方法的对比
-
-对比与其它 PEFT 方法，prompt tuning 胜在它的高效性。虽然 LoRA 也减少了训练参数以及所需显存，但反复加载 adapter 来切换任务就很麻烦。Prompt tuning 需要的训练参数更少，且任务切换更加方便。而全量参数微调则既需要超大的训练资源，也需要通过全量参数重新载入来切换任务。
-
-| 方法 | 训练参数量 | 显存需求 | 任务切换难度 |
-|--------|------------|---------|----------------|
-| Prompt Tuning | 很低 | 很低 | 非常简单 |
-| LoRA | 低 | 低 | 需要加载 Adapter |
-| 全量参数微调 | 很高 | 很高 | 加载所有参数 |
-
-在实际 prompt tuning 训练过程中，建议先从较小的 virtual tokens 数量开始（如 8 到 16 个），仅当任务复杂度增加时，再增加 virtual tokens 数量。从文本初始化通常比随机初始化更好，尤其是你使用和任务相关的文本时。初始化方法需要和你的任务复杂度匹配。
-
-训练过程中，你还需注意学习率。如果使用较大的学习率，你需要时刻观察 soft prompt 更新时的梯度信息，以防训练崩溃。训练过程中，定期的验证也是确保性能的良好手段。
-
-## 应用
-
-Prompt tuning 在这些场景下优势明显：
-
-1. 多任务下的大语言模型部署
-2. 计算资源有限的训练场景
-3. 需要在不同任务间快速切换的场景
-4. 针对隐私敏感的应用场景
-
-而当模型变小时，prompt tuning 就没有那么有竞争力了。比如在 SmolLM2 这样的模型尺寸下，prompt tuning 的意义就不大，可能还不如全量微调。
-
-## 接下来
-
-⏭️ 学习 [LoRA Adapters 的教程](./notebooks/finetune_sft_peft.ipynb)，了解如何实操 LoRA 微调模型的过程。
-
-## 学习资源
-- [PEFT 官方文档](https://huggingface.co/docs/peft)
-- [Prompt Tuning 论文](https://arxiv.org/abs/2104.08691)
-- [Hugging Face Cookbook 中关于 prompt tuning 的部分](https://huggingface.co/learn/cookbook/prompt_tuning_peft)
diff --git a/zh-cn/4_evaluation/README.md b/zh-cn/4_evaluation/README.md
deleted file mode 100644
index aaa4700a..00000000
--- a/zh-cn/4_evaluation/README.md
+++ /dev/null
@@ -1,40 +0,0 @@
-# 评测
-
-在开发和部署语言模型的过程中，模型评测是一个非常关键的步骤。它有助于我们理解模型在不同方面的能力究竟如何，并找到进一步提示改进的空间。本章将涵盖标准基准测试和特定领域的评估方法，以全面评估你的 smol 模型。
-
-我们将使用 [`lighteval`](https://github.com/huggingface/lighteval) 这个强大的评测代码库。它由 HuggingFace 开发，并完美地集成入了 HuggingFace 的生态系统。我们还提供了[指南书籍](https://github.com/huggingface/evaluation-guidebook)以便读者想要深入学习评测的相关概念和最佳实践。
-
-## 章节总览
-
-一个全面的评估策略会检查模型多个方面的性能。我们将评估模型特定领域的能力，比如回答问题、概括总结，来理解模型处理不同问题的能力。我们通过生成的连贯性和事实准确性等因素来衡量输出质量。同时，我们也需要安全评测，防治模型输出有害的信息或带有偏见的观点。最后，我们还可以进行特定领域的专业性测试，来确认模型是否在特定领域掌握了专业知识。
-
-## 目录
-
-### 1️⃣ [自动化基准测试](./automatic_benchmarks_cn.md)
-
-学习如何使用标准的测试基准和指标来评估模型。我们将会学习常见的测试基准，如 MMLU 和 TruthfulQA，理解重要指标和相关配置，同时为可复现的评估结果提供最佳实践。
-
-### 2️⃣ [自定义领域的评测](./custom_evaluation_cn.md)
-
-学习怎样为你的特定任务领域量身定做评估流程。我们将学习设计自定义评估任务、代码实现特定的指标，以及构建符合你要求的评估数据集。
-
-### 3️⃣ [领域评估的项目示例](./project/README_CN.md)
-
-通过一个完整的例子，学习构建特定领域的评测流程。这包含：生成评测数据集、使用 Argilla 平台进行数据标注、构建标准化的数据集、用 LightEval 评测模型。
-
-### Notebook 练习
-
-| 标题 | 简述 | 习题 | 链接 | Colab |
-|-------|-------------|----------|------|-------|
-| 评测并分析你的大语言模型 | 学习使用 LightEval 在特定领域评测、比较模型 | 🐢 使用医学相关领域的任务评估模型 <br> 🐕 Create a new domain evaluation with different MMLU tasks <br> 🦁 为你的特定任务领域创建一个自定义的评测任务 | [Notebook](./notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-
-
-## Resources
-
-- [评测的指南书籍](https://github.com/huggingface/evaluation-guidebook) - 大语言模型评测领域的全面指南
-- [LightEval 文档](https://github.com/huggingface/lighteval) - LightEval 官方文档
-- [Argilla 文档](https://docs.argilla.io) - 了解 Argilla 标注平台
-- [MMLU 论文](https://arxiv.org/abs/2009.03300) - 关于 MMLU 测评基准的论文
-- [LightEval 如何添加自定义任务](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
-- [LightEval 如何添加自定义测评指标](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
-- [LightEval 如何使用现有的测评指标](https://github.com/huggingface/lighteval/wiki/Metric-List)
\ No newline at end of file
diff --git a/zh-cn/4_evaluation/automatic_benchmarks.md b/zh-cn/4_evaluation/automatic_benchmarks.md
deleted file mode 100644
index 9d63a003..00000000
--- a/zh-cn/4_evaluation/automatic_benchmarks.md
+++ /dev/null
@@ -1,137 +0,0 @@
-# 自动化基准测试
-
-自动化基准测试可以作为一个标准化的工具，来衡量语言模型在不同任务上的能力。不过，虽然它可以有效地用来了解模型当前的性能，但我们还要认识到，这些评测结果仅仅是模型全面评估的一小部分，不能完全反映模型性能。
-
-## 理解自动化基准评测
-
-自动化基准评测一般是在定义好的领域和评测指标下，对特定的数据集进行测试。这种基准测试会评估模型多方面的能力，从最基础的语言理解到复杂的逻辑推理。其最主要的优点还是标准型方面，不同模型都使用相同标准来测试，可以用来对比不同模型的效果，同时测评出的结果也是可复现的。
-
-然而，我们也需要知道，这种基准测试也不是完全反映模型的现实能力的。比如，一个在学术评测基准上表现优异的模型，也许在其它应用领域或实践层面表现很差。
-
-## 现有评测基准及其局限性
-
-### 通识层面的评测基准
-
-MMLU（Massive Multitask Language Understanding）这个评测基准，涵盖了从科学到人文共 57 个学科的知识，是一个通识层面的评测基准。但虽然全面，它可能在某些领域的专业深度还不算够。另一方面，TruthfulQA 这个评测基准，涵盖 38 个学科问答，则会评测模型输出的真实性如何。
-
-### 推理层面的评测基准
-
-BBH（Big Bench Hard）和 GSM8K 这两个评测基准重点关注复杂的推理任务。BBH 主要测试逻辑思考和规划能力，GSM8K 则特别关注数学问题的求解。这些评测基准可以用来评测模型的分析问题能力，但人在现实世界中微妙的推理细节可能会被忽略。
-
-### 语言理解层面的评测基准
-
-在语言理解层面，HELM 提供了一个全面的评测框架，而 WinoGrande 则通过代词指代的歧义消除，测试模型在常识层面的能力。这些评测基准让我们能深入了解模型在语言处理层面的水平，但缺点是暂未模仿到人与人之间对话的复杂性，同时暂未测评到专业术语。
-
-## 其它评测方法
-
-除了上述评基准，很多机构也开发了其它评测方法，以应对标准化基准测试的缺陷：
-
-### 用大语言模型作为评审
-
-用一个大语言模型去评测另一个大语言模型的输出，这种方法最近开始常用起来。相比于传统的评测指标，这种方法可以提供更细致入微的反馈。缺点是作为评审的大语言模型自己也有偏见和局限性，可能导致评测结果不够好。
-
-### 在竞技场内相互评测
-
-像 Anthropic's Constitutional AI Arena 这样的平台，可以让模型在里面相互互动、评测。这样的评测场景也有助于模型发现各自的强项和弱点。
-
-### 自定义评测基准
-
-很多组织也会自己开发对内的评测基准，通常是针对特定的需求或应用场景开发的。这样开发出来的评测基准一般包含特定的专业领域知识、反映产品的实际应哟过场景。
-
-## Creating Your Own Evaluation Strategy
-
-虽然使用 LightEval 可以很方便地进行标准化的基准评测，但作为一个 LLM 开发者，你必须也要针对你们产品的应用场景开发自己的评测方案。标准化的基准评测仅仅是一测评开始的第一步，你绝不能只用它进行模型测试。
-
-如何自定义你的方位测评？方法如下：
-
-1. 首先从相关的标准化基准测试中开始，建立一个基准，保证能够和其它模型进行对比。
-
-2. 针对你的应用场景的独特需求，确认你的模型将会应对的挑战。例如，你的模型上线后将会主要执行什么任务？有可能出现哪些问题？哪些 bad case 是最应该避免的？
-
-3. 开发你自己的测试数据集，以便专门应对你的测试场景。这可能包括：
-   - 在你的特定领域里，真实用户的请求
-   - 常见的边缘案例
-   - 可能发生的有挑战性的情况
-
-4. 也需要考虑开发一个多层级的评测策略：
-   - 首先，为了能快速获取反馈，你可以设置一个自动化的评测指标
-   - 针对细微的语言理解能力，考虑人工测评
-   - 针对专业领域的应用，引入行业专家的评审
-   - 在控制变量的环境下，进行 A/B 测试
-
-## 使用 LightEval 进行基准评测
-
-LightEval 评测任务通过以下格式定义：
-```
-{suite}|{task}|{num_few_shot}|{auto_reduce}
-```
-
-- **suite**：哪一套基准评测方法（比如 'mmlu'、'truthfulqa'）
-- **task**：这一套基准评测方法中的哪个任务（比如 'abstract_algebra'）
-- **num_few_shot**：提示词中加入的示例的数量（如果是 0，那就是 zero-shot 测试）
-- **auto_reduce**：当提示词太长时，是否自动减少提示词中 few-shot 的样本量
-
-举例来说，`"mmlu|abstract_algebra|0|0"` 就会评测 MMLU 的 abstract algebra 任务，推理是 zero-shot 形式。
-
-### 评测代码示例
-
-以下代码就是一个在某个领域进行自动化评测的示例：
-
-```python
-from lighteval.tasks import Task, Pipeline
-from transformers import AutoModelForCausalLM
-
-# Define tasks to evaluate
-domain_tasks = [
-    "mmlu|anatomy|0|0",
-    "mmlu|high_school_biology|0|0", 
-    "mmlu|high_school_chemistry|0|0",
-    "mmlu|professional_medicine|0|0"
-]
-
-# Configure pipeline parameters
-pipeline_params = {
-    "max_samples": 40,  # Number of samples to evaluate
-    "batch_size": 1,    # Batch size for inference
-    "num_workers": 4    # Number of worker processes
-}
-
-# Create evaluation tracker
-evaluation_tracker = EvaluationTracker(
-    output_path="./results",
-    save_generations=True
-)
-
-# Load model and create pipeline
-model = AutoModelForCausalLM.from_pretrained("your-model-name")
-pipeline = Pipeline(
-    tasks=domain_tasks,
-    pipeline_parameters=pipeline_params,
-    evaluation_tracker=evaluation_tracker,
-    model=model
-)
-
-# Run evaluation
-pipeline.evaluate()
-
-# Get and display results
-results = pipeline.get_results()
-pipeline.show_results()
-```
-
-测评结果会以表格形式打印出来：
-
-```
-|                  Task                  |Version|Metric|Value |   |Stderr|
-|----------------------------------------|------:|------|-----:|---|-----:|
-|all                                     |       |acc   |0.3333|±  |0.1169|
-|leaderboard:mmlu:_average:5             |       |acc   |0.3400|±  |0.1121|
-|leaderboard:mmlu:anatomy:5              |      0|acc   |0.4500|±  |0.1141|
-|leaderboard:mmlu:high_school_biology:5  |      0|acc   |0.1500|±  |0.0819|
-```
-
-使用 pandas 的 `DataFrame` 或其它可视化方式呈现结果也是可以的。
-
-# 接下来
-
-⏩ 学习 [Custom Domain Evaluation](./custom_evaluation_cn.md)，了解如何根据你的特定需求创建自定义的评测流程。
diff --git a/zh-cn/4_evaluation/custom_evaluation.md b/zh-cn/4_evaluation/custom_evaluation.md
deleted file mode 100644
index eb1451f9..00000000
--- a/zh-cn/4_evaluation/custom_evaluation.md
+++ /dev/null
@@ -1,135 +0,0 @@
-# 在特定领域进行自定义评测
-
-虽然标准化的评测基准让我们对模型的性能有了初步的认识，但针对特定的应用场景，我们还需要专门制定评测方法，考察模型在特定领域的表现。本文将带你创建自定义的评测流程，针对你的目标领域对模型进行精准评测。
-
-## 设计评测策略
-
-成功创建自定义评测策略的第一步是确定清晰的目标。你需要考虑在你的特定领域，哪些特殊能力是你的模型需要具备的？这可能涉及技术层面的知识、推理的模式、特定的格式等。你需要认真记录好这些需求，然后参考这些需求去设计测试任务、选择评测指标。
-
-测试的样例不仅需要包含标准应用场景，也要考虑边缘场景。举例来说，如果是医学领域，常见的诊断场景和罕见情况都是需要考虑的。在金融领域，除了常规交易，复杂交易（比如设计多种货币或特殊条件的情况）的处理能力也需要被测试到。
-
-## 使用 LightEval 的代码实现
-
-LightEval 是一个非常灵活的框架，可以用来实现自定义的测评任务。下面代码展示了如何创建自定义测试任务：
-
-```python
-from lighteval.tasks import Task, Doc
-from lighteval.metrics import SampleLevelMetric, MetricCategory, MetricUseCase
-
-class CustomEvalTask(Task):
-    def __init__(self):
-        super().__init__(
-            name="custom_task",
-            version="0.0.1",
-            metrics=["accuracy", "f1"],  # Your chosen metrics
-            description="Description of your custom evaluation task"
-        )
-    
-    def get_prompt(self, sample):
-        # Format your input into a prompt
-        return f"Question: {sample['question']}\nAnswer:"
-    
-    def process_response(self, response, ref):
-        # Process model output and compare to reference
-        return response.strip() == ref.strip()
-```
-
-## 自定义评价指标
-
-特定领域的测试任务通常也需要特殊的评价指标。LightEval 也可以灵活地做到这一点：
-
-```python
-from aenum import extend_enum
-from lighteval.metrics import Metrics, SampleLevelMetric, SampleLevelMetricGrouping
-import numpy as np
-
-# Define a sample-level metric function
-def custom_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> dict:
-    """Example metric that returns multiple scores per sample"""
-    response = predictions[0]
-    return {
-        "accuracy": response == formatted_doc.choices[formatted_doc.gold_index],
-        "length_match": len(response) == len(formatted_doc.reference)
-    }
-
-# Create a metric that returns multiple values per sample
-custom_metric_group = SampleLevelMetricGrouping(
-    metric_name=["accuracy", "length_match"],  # Names of sub-metrics
-    higher_is_better={  # Whether higher values are better for each metric
-        "accuracy": True,
-        "length_match": True
-    },
-    category=MetricCategory.CUSTOM,
-    use_case=MetricUseCase.SCORING,
-    sample_level_fn=custom_metric,
-    corpus_level_fn={  # How to aggregate each metric
-        "accuracy": np.mean,
-        "length_match": np.mean
-    }
-)
-
-# Register the metric with LightEval
-extend_enum(Metrics, "custom_metric_name", custom_metric_group)
-```
-
-如果每个样例只有一个指标，代码可以是这样：
-
-```python
-def simple_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> bool:
-    """Example metric that returns a single score per sample"""
-    response = predictions[0]
-    return response == formatted_doc.choices[formatted_doc.gold_index]
-
-simple_metric_obj = SampleLevelMetric(
-    metric_name="simple_accuracy",
-    higher_is_better=True,
-    category=MetricCategory.CUSTOM,
-    use_case=MetricUseCase.SCORING,
-    sample_level_fn=simple_metric,
-    corpus_level_fn=np.mean  # How to aggregate across samples
-)
-
-extend_enum(Metrics, "simple_metric", simple_metric_obj)
-```
-
-实现完代码后，你就可以在你的评测任务中引用这些指标的名称，然后在你的评测任务中使用。这些指标会在测试过程中自动在每个样本上计算，并最终统计数值。
-
-如果需要使用更复杂的评测指标，你还可以实现这些功能：
-
-- 使用元数据，对不同测试样本的分数进行加权或其它调整
-- 对所有样本的指标进行统计时，你可以实现一个自定义的函数（上述示例中 corpus-level 统计使用了取平均的方法）
-- 对输入到你的评测指标函数中的数据进行格式检查
-- 记录边缘场景及其期望的行为
-
-
-
-你可以学习本章 [domain evaluation project](./project/README_CN.md) 这个项目课程，真正地动手实践一下自定义测评。
-
-## 测试数据集的创建
-
-高质量的测评需要高质量的测试数据集。在创建数据集时，需要考虑以下方面：
-
-1. 专家级的标注：与领域专家一起创建和检验测试样本。你可以用 [Argilla](https://github.com/argilla-io/argilla) 高效地进行标注。
-
-2. 真实世界的数据：收集真实数据并进行脱敏，确保这些样本能代表真实部署模型的场景。
-
-3. 借助合成数据：使用 LLM 生成一些初始样本，然后让领域专家检查、修改。这样可以助你快速创建数据集。
-
-## 最佳实践
-
-- 全面记录你的测试方法，包括各种假设和局限
-- 保证测试样本的多样性，确保你的领域内各个方面都能被测试到
-- 如有需要，自动化的测试指标和人工评测都要用上
-- 对测评数据集和代码进行版本控制
-- 定期更新你的评测流程，不断加入新的边缘场景、完善新的需求
-
-## 参考资料
-
-- [LightEval 如何添加自定义任务](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
-- [LightEval 如何添加自定义测评指标](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
-- [Argilla 文档](https://docs.argilla.io) 可以用来进行数据标注
-- [评测的指南书籍](https://github.com/huggingface/evaluation-guidebook) 大语言模型评测领域的全面指南
-- 
-# 接下来
-
-⏩ 完整的自定义测评请见本章 [domain evaluation project](./project/README_CN.md)。
\ No newline at end of file
diff --git a/zh-cn/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb b/zh-cn/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
deleted file mode 100644
index 592b84ce..00000000
--- a/zh-cn/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
+++ /dev/null
@@ -1,19422 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TZVw9f5QYWPL"
-      },
-      "source": [
-        "# 使用 lighteval 作为你的 AI 测评框架\n",
-        "\n",
-        "本文将带你实践如何使用 lighteval 测评大语言模型，并与其它模型进行对比。\n",
-        "\n",
-        "使用 `lighteval` 可以很方便地在主流评测基准上计算评测分数。最近，`lighteval` 被进行了一次重构，以便在 Python 中最为一个库进行使用。\n",
-        "\n",
-        "接下来，我们将试着计算一些分数。\n",
-        "\n",
-        "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-        "    <h2 style='margin: 0;color:blue'>练习：评测你的模型</h2>\n",
-        "    <p>基于前面的学习，你可以试着选一个你感兴趣的领域，在上面测试模型。</p>\n",
-        "    <p><b>难度等级</b></p>\n",
-        "    <p>🐢 从 Hugging Face hub 另找一个模型，在已有的医学领域测试模型</p>\n",
-        "    <p>🐕 选择一个不同的 MMLU 任务（如 comupter science、mathematics、physcics），进行模型测试</p>\n",
-        "    <p>🦁 用 LightEval 自定义评测任务，测试你选的模型</p>\n",
-        "</div>\n",
-        "\n",
-        "## 安装依赖"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "afW_CLJoPCnF",
-        "outputId": "91611cd5-51d6-4f73-9b3d-ac79c918d074"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install -qqq -U \"torch<2.5\" \"torchvision<2.5\" \"torchaudio<2.5\" --index-url https://download.pytorch.org/whl/cu121\n",
-        "!pip list | grep torch"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "8xMnn_cQ1EEi",
-        "outputId": "bcaf8498-19c3-46af-9ae1-0306326f006b"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install -qqq git+https://github.com/huggingface/lighteval.git tiktoken"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TDKs5ShvXw8K"
-      },
-      "source": [
-        "## 配置 `lighteval` 评测环境和评测流程\n",
-        "\n",
-        "我们首先需要配置 `lighteval` 评测环境和评测流程。注意 `push_to_hub` 和 `push_to_tensorboard` 的用法。\n",
-        "\n",
-        "### `push_to_hub`\n",
-        "\n",
-        "如果我们想要把评测结果和模型的配置和权重绑定在一起，我们可以用 `push_to_hub` 将其保存到 Hugging Face hub 上。\n",
-        "\n",
-        "### `push_to_tensorboard`\n",
-        "\n",
-        "如果我们建立了评测工具或脚本用来可视化结果，我们可以使用 `push_to_tensorboard` 来进行可视化。"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 296,
-          "referenced_widgets": [
-            "711ff69b059c421f84405dc798c84297",
-            "3c90ad18d29042caa880291056159985",
-            "66d9640217374065a9af74765929d326",
-            "5965b90591764931a635cc9305f2fa40",
-            "b34849662b994d87a912fdee127e1eed",
-            "1c358b2f428a4bb9bc0245b94d6394e8",
-            "9e9b42ee82614d56ab89f79bc2639d29",
-            "39db106760854445bc1d1c0a246400f9",
-            "737225a54b724c60ac0fb543b94bd39a",
-            "b6b2341d52504399840f08eb6d25bd84",
-            "4d137ae7c6e442f380e7764a741572d6",
-            "f837792155cc4e93884c80bd0bad7881",
-            "0ed8df4245654e928f9c9b584e02e203",
-            "2b426e2817f84313bbaf6c8f78ff3150",
-            "0ade751a6028417aa6f305217007bc64",
-            "5b2d53ff4a0a40f280757c5b396c2d88",
-            "b1201caf6e9d43a0b44f93307c248085",
-            "4fe67e045c8e4123beff0f1fb603cc0f",
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-          ]
-        },
-        "id": "3cUebd-z6IWs",
-        "outputId": "4b5ef2ce-04a0-4b2a-f3d7-5ecb7afb662e"
-      },
-      "outputs": [],
-      "source": [
-        "import lighteval\n",
-        "import os\n",
-        "from datetime import timedelta\n",
-        "from transformers import AutoModelForCausalLM\n",
-        "\n",
-        "from lighteval.logging.evaluation_tracker import EvaluationTracker\n",
-        "from lighteval.logging.hierarchical_logger import hlog_warn, htrack\n",
-        "from lighteval.models.model_config import create_model_config\n",
-        "from lighteval.pipeline import EnvConfig, ParallelismManager, Pipeline, PipelineParameters\n",
-        "\n",
-        "TOKEN = os.getenv(\"HF_TOKEN\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "muikmXNQXgFv"
-      },
-      "outputs": [],
-      "source": [
-        "env_config = EnvConfig(token=TOKEN, cache_dir=\"~/tmp\")\n",
-        "\n",
-        "evaluation_tracker = EvaluationTracker(\n",
-        "    output_dir=\"~/tmp\",\n",
-        "    save_details=False,\n",
-        "    push_to_hub=False,\n",
-        "    push_to_tensorboard=False,\n",
-        "    public=False,\n",
-        "    hub_results_org=False,\n",
-        ")\n",
-        "\n",
-        "pipeline_params = PipelineParameters(\n",
-        "    launcher_type=ParallelismManager.ACCELERATE,\n",
-        "    env_config=env_config,\n",
-        "    job_id=1,\n",
-        "    override_batch_size=1,\n",
-        "    num_fewshot_seeds=0,\n",
-        "    max_samples=10,\n",
-        "    use_chat_template=False,\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "nsNjwzCtltkA"
-      },
-      "source": [
-        "# 用 `lighteval` 对比不同模型\n",
-        "\n",
-        "这里我们尝试对比两个模型：`Qwen2.5-0.5B` 和 `SmolLM2-360M-Instruct`。我们选择的领域是医学领域。\n",
-        "\n",
-        "这里我们是用 MMLU 的一个字任务创建评测的。在 lighteval 中，不同测试任务可以用字符串指代，格式如下：\n",
-        "\n",
-        "`{suite}|{task}:{subtask}|{num_few_shot}|{0 or 1 to reduce num_few_shot if prompt is too long}`\n",
-        "\n",
-        "所以，针对我们选择的医学领域，我们可以传入一系列任务，用逗号分隔开：\n",
-        "\n",
-        "```\n",
-        "\"leaderboard|mmlu:anatomy|5|0,leaderboard|mmlu:professional_medicine|5|0,leaderboard|mmlu:high_school_biology|5|0,leaderboard|mmlu:high_school_chemistry|5|0\"\n",
-        "```\n",
-        "\n",
-        "上述字符串代表以下四个任务：\n",
-        "\n",
-        "| Suite | Task | Num Fewshot Example | Limit Fewshots |\n",
-        "|---|---|---|---|\n",
-        "| leaderboard | mmlu:anatomy | 5 | False |\n",
-        "| leaderboard | mmlu:professional_medicine | 5 | False |\n",
-        "| leaderboard | mmlu:high_school_biology | 5 | False |\n",
-        "| leaderboard | mmlu:high_school_chemistry | 5 | False |\n",
-        "\n",
-        "完整的任务列表，可以参考[这篇文档](https://github.com/huggingface/lighteval/wiki/Available-Tasks)。"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "qTqsizv9mVbO"
-      },
-      "outputs": [],
-      "source": [
-        "domain_tasks = \"leaderboard|mmlu:anatomy|5|0,leaderboard|mmlu:professional_medicine|5|0,leaderboard|mmlu:high_school_biology|5|0,leaderboard|mmlu:high_school_chemistry|5|0\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "XwcJklSFX4H6"
-      },
-      "source": [
-        "# 评测 Qwen2.5 0.5B"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
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-          ]
-        },
-        "id": "PXJuaXVxUNBO",
-        "outputId": "87aca361-5bd5-4b12-9097-3e97f1165d7b"
-      },
-      "outputs": [],
-      "source": [
-        "qwen_model = AutoModelForCausalLM.from_pretrained(\"Qwen/Qwen2.5-0.5B\")\n",
-        "\n",
-        "pipeline = Pipeline(\n",
-        "    tasks=domain_tasks,\n",
-        "    pipeline_parameters=pipeline_params,\n",
-        "    evaluation_tracker=evaluation_tracker,\n",
-        "    model=qwen_model\n",
-        ")\n",
-        "\n",
-        "pipeline.evaluate()\n",
-        "\n",
-        "qwen_results = pipeline.get_results()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "CIwCaCxJX_hA"
-      },
-      "source": [
-        "# 评测 SmolLM 360M"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000,
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-        },
-        "id": "Dxg0RtlNVT4y",
-        "outputId": "ee40393c-bf62-499d-d443-e96da72411e8"
-      },
-      "outputs": [],
-      "source": [
-        "smol_model = AutoModelForCausalLM.from_pretrained(\"HuggingFaceTB/SmolLM2-360M-Instruct\")\n",
-        "\n",
-        "pipeline = Pipeline(\n",
-        "    tasks=domain_tasks,\n",
-        "    pipeline_parameters=pipeline_params,\n",
-        "    evaluation_tracker=evaluation_tracker,\n",
-        "    model=smol_model\n",
-        ")\n",
-        "\n",
-        "pipeline.evaluate()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "pdjyzfKHVt52"
-      },
-      "outputs": [],
-      "source": [
-        "smol_results = pipeline.get_results()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "eugvMFfgV1VD",
-        "outputId": "62956e3f-29c8-4493-f868-4ba63b7d8e96"
-      },
-      "outputs": [],
-      "source": [
-        "pipeline.show_results()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0HD8aFwSYGHu"
-      },
-      "source": [
-        "# 可视化结果\n",
-        "\n",
-        "得到结果后，我们可以可视化一下，并列在一起对比。这里我们使用了 `pandas` 简单地实现了可视化操作，不过你也可以使用不同的可视化方法："
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 447
-        },
-        "id": "1sReqrgQUO9r",
-        "outputId": "9a0779e1-c278-48ab-9ecc-5055028ea3fd"
-      },
-      "outputs": [],
-      "source": [
-        "import pandas as pd\n",
-        "\n",
-        "df = pd.DataFrame.from_records(smol_results[\"results\"]).T[\"acc\"].rename(\"SmolLM2-360M-Instruct\")\n",
-        "_df = pd.DataFrame.from_records(qwen_results[\"results\"]).T[\"acc\"].rename(\"Qwen2-0.5B-DPO\")\n",
-        "df = pd.concat([df, _df], axis=1)\n",
-        "df.plot(kind=\"barh\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "qJEbQeYDplKX"
-      },
-      "source": [
-        "# 💐 就是这些！\n",
-        "\n",
-        "本文提供了标准化评测的简单代码，使用这些代码，你还可以：\n",
-        "\n",
-        "- 选择模型进行推理\n",
-        "- 在训练过程中针对不同阶段保存的模型实时测试\n",
-        "- 分享你的测试成绩到开源社区"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jWdS38syaipm"
-      },
-      "source": [
-        "🏃接下来\n",
-        "\n",
-        "- 如果你还想深入学习，可以看看这个 [notebook](https://github.com/huggingface/evaluation-guidebook/blob/main/contents/examples/comparing_task_formulations.ipynb)\n"
-      ]
-    }
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-            "_view_name": "LayoutView",
-            "align_content": null,
-            "align_items": null,
-            "align_self": null,
-            "border": null,
-            "bottom": null,
-            "display": null,
-            "flex": null,
-            "flex_flow": null,
-            "grid_area": null,
-            "grid_auto_columns": null,
-            "grid_auto_flow": null,
-            "grid_auto_rows": null,
-            "grid_column": null,
-            "grid_gap": null,
-            "grid_row": null,
-            "grid_template_areas": null,
-            "grid_template_columns": null,
-            "grid_template_rows": null,
-            "height": null,
-            "justify_content": null,
-            "justify_items": null,
-            "left": null,
-            "margin": null,
-            "max_height": null,
-            "max_width": null,
-            "min_height": null,
-            "min_width": null,
-            "object_fit": null,
-            "object_position": null,
-            "order": null,
-            "overflow": null,
-            "overflow_x": null,
-            "overflow_y": null,
-            "padding": null,
-            "right": null,
-            "top": null,
-            "visibility": null,
-            "width": null
-          }
-        }
-      }
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
diff --git a/zh-cn/4_evaluation/project/README.md b/zh-cn/4_evaluation/project/README.md
deleted file mode 100644
index c896e4af..00000000
--- a/zh-cn/4_evaluation/project/README.md
+++ /dev/null
@@ -1,81 +0,0 @@
-# 用 Argilla、Distilabel 和 LightEval 进行特定领域评测
-
-绝大多数的常用评测基准关注的都是模型非常基本的能力，比如推理、数学、编程等，而忽略了特定的专业领域能力。如何进行专业领域（如金融、法律、医学等）的模型评测呢？
-
-本教程将会展示**自定义领域**模型测试的完整流程。我们重点关注数据部分。在相关数据收集、标注测试数据方面，我们会使用 [Argilla](https://github.com/argilla-io/argilla)、[Distilabel](https://github.com/argilla-io/distilabel) 和 [LightEval](https://github.com/huggingface/lighteval) 作为工具，生成考试问题相关的数据。
-
-
-## 项目结构
-
-本项目包含四份 Python 代码文件。我们分四个步骤完成模型在自定义领域的测评，每份代码对应一个步骤。这四个步骤分别是：数据生成、数据标注、相关测试样本的提取，以及模型评测。
-
-| 代码文件 | 概述 |
-|-------------|-------------|
-| generate_dataset.py | 使用一个专门的语言模型，从多个文本文档中生成考试问题。 |
-| annotate_dataset.py | 用 Argilla 创建一个数据集，手动为生成的考试问题数据进行标注。 |
-| create_dataset.py | 处理标注过的数据，并创建对应的 HuggingFace 数据集。 |
-| evaluation_task.py | 自定义了一个 LightEval 任务，在前面建立好的测试数据上测试。 |
-
-## 步骤
-
-### 1. 数据集的生成
-
-使用 `generate_dataset.py`，我们可以用 `distilabel` 这个库根据几个文本文档生成一些考试问题。一个特定的模型（这里默认使用 Meta-Llama-3.1-8B-Instruct）被拿来用以生成问题、正确答案和错误答案（错误答案用来作为干扰项）。你需要加入你自己的数据，也可以切换使用别的模型。
-
-通过以下命令可以开始生成：
-
-```sh
-python generate_dataset.py --input_dir path/to/your/documents --model_id your_model_id --output_path output_directory
-```
-
-代码运行中将会创建一个 [Distiset](https://distilabel.argilla.io/dev/sections/how_to_guides/advanced/distiset/)，它包含根据文档生成的考试问题，其中保存文档的目录是 `input_dir`。
-
-### 2. 标注数据集
-
-使用 `annotate_dataset.py` 可以将生成的问题创建为一个 Argilla 数据集，用以标注。该程序会构建起数据集结构，并把生成的问题和回答填充进统一结构中，还可以改变数据样本的顺序来避免偏向性。使用 Argilla，你或者一个领域专家可以用选择的方式给出每个问题的正确回答。
-
-在标注界面中，已生成的几个回答会随机排列着，供标注人员选择正确答案。这其中，LLM 会提供一个建议的正确回答，你可以选择你认为正确的回答，也可以对选中的正确回答进行编辑改进。标注过程的耗时取决于你的数据集规模、领域内数据的复杂度，以及 LLM 的能力强弱。举例来说，我们是可以借助 Llama-3.1-70B-Instruct 在 1 小时内标注好迁移学习领域 150 个样本的，大多数时候，直接选择正确答案即可。
-
-通过以下命令可以开始标注：
-
-
-```sh
-python annotate_dataset.py --dataset_path path/to/distiset --output_dataset_name argilla_dataset_name
-```
-
-这将会创建一个 Argilla 数据集，用来手工检查和标注数据。
-
-![argilla_dataset](./images/domain_eval_argilla_view.png)
-
-可以参考[这里的指引](https://docs.argilla.io/latest/getting_started/quickstart/)，在本地或 Hugging Face 的 space 里部署标注任务。
-
-### 3. 创建数据集
-
-使用 `create_dataset.py` 可以进一步处理 Argilla 标注的数据，并创建一个 Hugging Face 数据集。这个数据集里每条数据包含这些内容：问题、可能的回答、正确回答（所在的列的名字）。运行以下命令即可创建最终的数据集：
-
-```sh
-huggingface_hub login
-python create_dataset.py --dataset_path argilla_dataset_name --dataset_repo_id your_hf_repo_id
-```
-
-最终，数据集会被推送到 Hugging Face Hub 里。本示例的数据集已经上传了，可以在[这里](https://huggingface.co/datasets/burtenshaw/exam_questions/viewer/default/train)查看，大致是这样：
-
-![hf_dataset](./images/domain_eval_dataset_viewer.png)
-
-### 4. 开始测评
-
-使用 `evaluation_task.py`，你可以自定义一个 LightEval 任务，用来在前面创建的数据集上测试模型。具体执行命令如下：
-
-```sh
-lighteval accelerate \
-    --model_args "pretrained=HuggingFaceH4/zephyr-7b-beta" \
-    --tasks "community|exam_questions|0|0" \
-    --custom_tasks domain-eval/evaluation_task.py \
-    --output_dir "./evals"
-```
-
-此外，lighteval 的 wiki 也提供了更详细的讲解：
-
-- [自定义评测任务](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
-- [自定义评测指标](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
-- [使用已有的评测指标](https://github.com/huggingface/lighteval/wiki/Metric-List)
diff --git a/zh-cn/4_evaluation/project/annotate_dataset.py b/zh-cn/4_evaluation/project/annotate_dataset.py
deleted file mode 100644
index dadffc10..00000000
--- a/zh-cn/4_evaluation/project/annotate_dataset.py
+++ /dev/null
@@ -1,129 +0,0 @@
-import argparse
-import json
-from random import choices, sample
-
-import argilla as rg
-from distilabel.distiset import Distiset
-
-################################################################################
-# Script Parameters
-################################################################################
-
-parser = argparse.ArgumentParser(
-    description="Annotate exam questions dataset using Argilla."
-)
-parser.add_argument(
-    "--argilla_api_key",
-    type=str,
-    default="argilla.apikey",
-    help="API key for Argilla",
-)
-parser.add_argument(
-    "--argilla_api_url",
-    type=str,
-    default="http://localhost:6900",
-    help="API URL for Argilla",
-)
-parser.add_argument(
-    "--dataset_path",
-    type=str,
-    default="exam_questions",
-    help="Path to the exam questions dataset",
-)
-parser.add_argument(
-    "--dataset_config",
-    type=str,
-    default="default",
-    help="Configuration of the dataset",
-)
-parser.add_argument(
-    "--dataset_split",
-    type=str,
-    default="train",
-    help="Split of the dataset to use",
-)
-parser.add_argument(
-    "--output_dataset_name",
-    type=str,
-    default="exam_questions",
-    help="Name of the output Argilla dataset",
-)
-
-args = parser.parse_args()
-
-################################################################################
-# Create Argilla dataset with the feedback task for validation
-################################################################################
-
-client = rg.Argilla(api_key=args.argilla_api_key, api_url=args.argilla_api_url)
-
-if client.datasets(args.output_dataset_name):
-    print(f"Deleting existing dataset '{args.output_dataset_name}'")
-    client.datasets(args.output_dataset_name).delete()
-
-settings = rg.Settings(
-    fields=[
-        rg.TextField("question"),
-        rg.TextField("answer_a"),
-        rg.TextField("answer_b"),
-        rg.TextField("answer_c"),
-        rg.TextField("answer_d"),
-    ],
-    questions=[
-        rg.LabelQuestion(
-            name="correct_answer",
-            labels=["answer_a", "answer_b", "answer_c", "answer_d"],
-        ),
-        rg.TextQuestion(
-            name="improved_question",
-            description="Could you improve the question?",
-        ),
-        rg.TextQuestion(
-            name="improved_answer",
-            description="Could you improve the best answer?",
-        ),
-    ],
-)
-
-dataset = rg.Dataset(settings=settings, name=args.output_dataset_name)
-dataset.create()
-
-################################################################################
-# Load the Distiset and process and add records to Argilla dataset
-# We will validate that questions appear in random order to avoid bias
-# but we will show correct answers in the Argilla UI as suggestions.
-################################################################################
-
-distiset = Distiset.load_from_disk(args.dataset_path)
-answer_names = ["answer_a", "answer_b", "answer_c", "answer_d"]
-dataset_records = []
-
-for exam in distiset[args.dataset_config][args.dataset_split]:
-    exam_json = json.loads(exam["generation"])["exam"]
-
-    for question in exam_json:
-        answer = question["answer"]
-        distractors = question["distractors"]
-        distractors = choices(distractors, k=3)
-        answers = distractors + [answer]
-        answers = sample(answers, len(answers))
-        suggestion_idx = answers.index(answer)
-        fields = dict(zip(answer_names, answers))
-        fields["question"] = question["question"]
-
-        record = rg.Record(
-            fields=fields,
-            suggestions=[
-                rg.Suggestion(
-                    question_name="correct_answer",
-                    value=answer_names[suggestion_idx],
-                )
-            ],
-        )
-        dataset_records.append(record)
-
-dataset.records.log(dataset_records)
-
-print(
-    f"Dataset '{args.output_dataset_name}' has been created and populated in Argilla."
-)
diff --git a/zh-cn/4_evaluation/project/create_dataset.py b/zh-cn/4_evaluation/project/create_dataset.py
deleted file mode 100644
index 822e268e..00000000
--- a/zh-cn/4_evaluation/project/create_dataset.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import argparse
-
-import argilla as rg
-from datasets import Dataset
-
-################################################################################
-# Script Parameters
-################################################################################
-
-parser = argparse.ArgumentParser(
-    description="Create a Hugging Face dataset from annotated Argilla data."
-)
-parser.add_argument(
-    "--argilla_api_key",
-    type=str,
-    default="argilla.apikey",
-    help="API key for Argilla",
-)
-parser.add_argument(
-    "--argilla_api_url",
-    type=str,
-    default="http://localhost:6900",
-    help="API URL for Argilla",
-)
-parser.add_argument(
-    "--dataset_path",
-    type=str,
-    default="exam_questions",
-    help="Path to the Argilla dataset",
-)
-parser.add_argument(
-    "--dataset_repo_id",
-    type=str,
-    default="burtenshaw/exam_questions",
-    help="Hugging Face dataset repository ID",
-)
-
-args = parser.parse_args()
-
-################################################################################
-# Initialize Argilla client and load dataset
-################################################################################
-
-client = rg.Argilla(api_key=args.argilla_api_key, api_url=args.argilla_api_url)
-dataset = client.datasets(args.dataset_path)
-
-################################################################################
-# Process Argilla records
-################################################################################
-
-dataset_rows = []
-
-for record in dataset.records(with_suggestions=True, with_responses=True):
-    row = record.fields
-
-    if len(record.responses) == 0:
-        answer = record.suggestions["correct_answer"].value
-        row["correct_answer"] = answer
-    else:
-        for response in record.responses:
-            if response.question_name == "correct_answer":
-                row["correct_answer"] = response.value
-    dataset_rows.append(row)
-
-################################################################################
-# Create Hugging Face dataset and push to Hub
-################################################################################
-
-hf_dataset = Dataset.from_list(dataset_rows)
-hf_dataset.push_to_hub(repo_id=args.dataset_repo_id)
-
-print(f"Dataset has been successfully pushed to {args.dataset_repo_id}")
diff --git a/zh-cn/4_evaluation/project/evaluation_task.py b/zh-cn/4_evaluation/project/evaluation_task.py
deleted file mode 100644
index 397c75ad..00000000
--- a/zh-cn/4_evaluation/project/evaluation_task.py
+++ /dev/null
@@ -1,86 +0,0 @@
-import numpy as np
-
-from lighteval.tasks.lighteval_task import LightevalTaskConfig
-from lighteval.tasks.requests import Doc
-from lighteval.metrics.utils.metric_utils import (
-    SampleLevelMetric,
-    MetricCategory,
-    MetricUseCase,
-)
-
-################################################################################
-# Define the prompt function based on the structure of the dataset
-################################################################################
-
-
-def prompt_fn(line, task_name: str = None):
-    """Converts a dataset line to a Doc object for evaluation."""
-    instruction = "Choose the correct answer for the following exam question:"
-    return Doc(
-        task_name=task_name,
-        query=f"{instruction} {line['question']}",
-        choices=[
-            f" {line['answer_a']}",
-            f" {line['answer_b']}",
-            f" {line['answer_c']}",
-            f" {line['answer_d']}",
-        ],
-        gold_index=["answer_a", "answer_b", "answer_c", "answer_d"].index(
-            line["correct_answer"]
-        ),
-        instruction=instruction,
-    )
-
-
-################################################################################
-# Define the custom metric based on guide here https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric
-# Or use an existing metric based on the guide here: https://github.com/huggingface/lighteval/wiki/Metric-List
-# Existing metrics can be imported from lighteval.metrics.metrics
-################################################################################
-
-
-def sample_level_fn(formatted_doc: Doc, **kwargs) -> bool:
-    response = np.argmin(kwargs["choices_logprob"])
-    return response == formatted_doc.gold_index
-
-
-custom_metric = SampleLevelMetric(
-    metric_name="exam_question_accuracy",
-    higher_is_better=True,
-    category=MetricCategory.MULTICHOICE,
-    use_case=MetricUseCase.NONE,
-    sample_level_fn=sample_level_fn,
-    corpus_level_fn=np.mean,
-)
-
-################################################################################
-# Define the task based on the prompt function and the custom metric
-# Based on the guide here: https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task
-################################################################################
-
-task = LightevalTaskConfig(
-    name="example",
-    prompt_function=prompt_fn,
-    suite=["community"],
-    hf_repo="burtenshaw/exam_questions",
-    hf_subset="default",
-    hf_avail_splits=["train"],
-    evaluation_splits=["train"],
-    few_shots_split=None,
-    few_shots_select=None,
-    metric=[custom_metric],
-)
-
-# Add the task to TASKS_TABLE
-TASKS_TABLE = [task]
-
-# MODULE LOGIC
-if __name__ == "__main__":
-    print([t.name for t in TASKS_TABLE])
-    print(len(TASKS_TABLE))
-
-# lighteval accelerate \
-# "pretrained=HuggingFaceTB/SmolLM2-135M-Instruct" \
-# "community|example|0|0" \
-# --custom-tasks "submitted_tasks/example.py" \
-# --output-dir "results"
diff --git a/zh-cn/4_evaluation/project/generate_dataset.py b/zh-cn/4_evaluation/project/generate_dataset.py
deleted file mode 100644
index e0dbd6ed..00000000
--- a/zh-cn/4_evaluation/project/generate_dataset.py
+++ /dev/null
@@ -1,168 +0,0 @@
-import argparse
-import os
-from pydantic import BaseModel, Field
-from datasets import Dataset
-from typing import List
-
-from distilabel.llms import InferenceEndpointsLLM
-from distilabel.pipeline import Pipeline
-from distilabel.steps.tasks import TextGeneration
-
-
-################################################################################
-# Script Parameters
-################################################################################
-
-parser = argparse.ArgumentParser(
-    description="Generate exam questions from text files in a directory."
-)
-parser.add_argument(
-    "--model_id",
-    type=str,
-    default="Qwen/Qwen2.5-7B-Instruct",
-    help="Model ID for text generation",
-)
-parser.add_argument(
-    "--tokenizer_id",
-    type=str,
-    default="Qwen/Qwen2.5-7B-Instruct",
-    help="Tokenizer ID for text generation",
-)
-parser.add_argument(
-    "--input_dir",
-    type=str,
-    help="Directory containing input text files",
-    default="data",
-)
-parser.add_argument(
-    "--max_new_tokens",
-    type=int,
-    default=2048,
-    help="Maximum number of new tokens to generate",
-)
-parser.add_argument(
-    "--output_path",
-    type=str,
-    default="exam_questions_output",
-    help="Directory to save the generated datasets",
-)
-
-args = parser.parse_args()
-
-################################################################################
-# Load the documents
-# We assume that the documents are in the input directory, and that each file
-# is a separate document about the same topic.
-################################################################################
-
-# Process all text files in the input directory
-documents = []
-for filename in os.listdir(args.input_dir):
-    if filename.endswith(".txt"):
-        file_path = os.path.join(args.input_dir, filename)
-        with open(file=file_path, mode="r", encoding="utf-8") as file:
-            document_content = file.read()
-            documents.append(document_content)
-
-# Create a single dataset from all document contents
-dataset = Dataset.from_dict({"document": documents})
-
-################################################################################
-# Define the prompts
-# We use a system prompt to guide the model to generate the correct output format.
-# A template is used to insert the document into the prompt.
-################################################################################
-
-SYSTEM_PROMPT = """\
-You are an exam writer specialized in writing exams for students.
-Your goal is to create questions and answers based on the document provided, 
-and a list of distractors, that are incorrect but viable answers to the question.
-Your answer must adhere to the following format:
-```
-[
-    {
-        "question": "Your question",
-        "answer": "The correct answer to the question",
-        "distractors": ["wrong answer 1", "wrong answer 2", "wrong answer 3"]
-    },
-    ... (more questions and answers as required)
-]
-```
-""".strip()
-
-INSTRUCTION_TEMPLATE = """\
-    Generate a list of answers and questions about the document. 
-    Document:\n\n{{ instruction }}"""
-
-################################################################################
-# Define the output structure
-# We define a data model for the output of the pipeline, this is used to ensure
-# that the output is in the correct format for the evaluation task.
-################################################################################
-
-
-class ExamQuestion(BaseModel):
-    question: str = Field(..., description="The question to be answered")
-    answer: str = Field(..., description="The correct answer to the question")
-    distractors: List[str] = Field(
-        ..., description="A list of incorrect but viable answers to the question"
-    )
-
-
-class ExamQuestions(BaseModel):
-    exam: List[ExamQuestion]
-
-
-################################################################################
-# Create the pipeline
-# We create a pipeline with a single task that generates the exam questions
-# based on the document and in the correct format. We will Hugging Face
-# InferenceEndpoints and the model specified in the arguments.
-################################################################################
-
-with Pipeline(
-    name="Domain-Eval-Questions",
-    description="Generate exam questions based on given documents.",
-) as pipeline:
-    # Set up the text generation task
-    text_generation = TextGeneration(
-        name="exam_generation",
-        llm=InferenceEndpointsLLM(
-            model_id=args.model_id,
-            tokenizer_id=args.model_id,
-            api_key=os.environ["HF_TOKEN"],
-            structured_output={
-                "schema": ExamQuestions.model_json_schema(),
-                "format": "json",
-            },
-        ),
-        input_batch_size=8,
-        output_mappings={"model_name": "generation_model"},
-        input_mappings={"instruction": "document"},
-        system_prompt=SYSTEM_PROMPT,
-        template=INSTRUCTION_TEMPLATE,
-    )
-
-
-################################################################################
-# Run the pipeline
-# We run the pipeline for all documents and save the results to the output path.
-################################################################################
-
-if __name__ == "__main__":
-    # Run the pipeline for all documents
-    distiset = pipeline.run(
-        parameters={
-            "exam_generation": {
-                "llm": {
-                    "generation_kwargs": {
-                        "max_new_tokens": args.max_new_tokens,
-                    }
-                }
-            }
-        },
-        use_cache=False,
-        dataset=dataset,
-    )
-
-    distiset.save_to_disk(args.output_path)
diff --git a/zh-cn/4_evaluation/project/images/domain_eval_argilla_view.png b/zh-cn/4_evaluation/project/images/domain_eval_argilla_view.png
deleted file mode 100644
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diff --git a/zh-cn/5_vision_language_models/README.md b/zh-cn/5_vision_language_models/README.md
deleted file mode 100644
index cc087c22..00000000
--- a/zh-cn/5_vision_language_models/README.md
+++ /dev/null
@@ -1,40 +0,0 @@
-# 视觉语言模型
-
-## 1. 视觉语言模型的用处
-
-视觉语言模型（Vision Language Models 或简称 VLM）是一种不仅仅接收语言输入、而且可以处理图片输入的模型，它可以支持诸如图像文本描述生成（image captioning）、视觉问答（visual question answering）、多模态推理（multimodal reasoning）等任务。
-
-一个典型的 VLM 架构包含一个图像编码器（用来提取视觉特征）、一个映射层（用来对齐视觉特征和文本表征）以及一个语言模型（用以处理视觉语言特征并输出文本）。这使得模型得以在视觉元素和语言概念之间建立起连接。
-
-VLM 可以有很多用处。基本的用途包括通用的视觉语言任务，而那些针对聊天对话场景优化过的 VLM 则可以支持对话式的人机互动。还有些模型可以根据图像中的证据预测事实，或者进行特定的任务，如物体检测。
-
-关于 VLM 的更多技术和使用，建议读者在 [VLM 的使用](./vlm_usage_cn.md)这一节中学习。
-
-## 2. 视觉语言模型的微调
-
-微调一个 VLM 的过程，通常是指选择一个预训练过的模型，在一个新的数据集上学习处理特定领域的任务。这个过程可以参考本教程第 1 和 2 章的相关方法，比如有监督微调、偏好优化等。
-
-虽然核心工具与技术和大语言模型（LLMs）所用的大致相同，但微调视觉语言模型（VLMs）需要格外关注图像的数据表示与准备。如此才能确保模型有效整合并处理视觉与文本数据，以实现最佳性能。鉴于演示模型 SmolVLM 比前一模块使用的语言模型大得多，探索高效的微调方法至关重要。量化和参数高效微调（PEFT）等技术，能让这一过程更易于操作且成本更低，使更多用户能够对该模型进行试验。
-
-如果你想了解详细的微调 VLM 的技术，你可以学习 [VLM 微调](./vlm_finetuning_cn.md) 这一节。  
-
-
-## Exercise Notebooks  
-
-
-| 标题 | 概述 | 练习 | 链接 | Colab |
-|-------|-------------|----------|------|-------|
-| VLM 的使用 | 学习如何载入并使用一个预训练过的 VLM 来处理各种任务 | 🐢 尝试处理一张图片 <br>🐕 尝试用 batch 的方式处理多个图片 <br>🦁 尝试处理一整个视频 | [Notebook](./notebooks/vlm_usage_sample_cn.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/5_vision_language_models/notebooks/vlm_usage_sample.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-| VLM 微调 | 学习针对特定任务数据集来微调一个预训练过的 VLM | 🐢 使用一个基本数据集进行微调<br>🐕 尝试使用新数据集 <br>🦁 试验一种不同的微调方法 | [Notebook](./notebooks/vlm_sft_sample_cn.ipynb)| <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/5_vision_language_models/notebooks/vlm_sft_sample.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | 
-
-
-## 参考资料  
-- [Hugging Face Learn: Supervised Fine-Tuning VLMs](https://huggingface.co/learn/cookbook/fine_tuning_vlm_trl)
-- [Hugging Face Learn: Supervised Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_smol_vlm_sft_trl)  
-- [Hugging Face Learn: Preference Optimization Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_vlm_dpo_smolvlm_instruct)  
-- [Hugging Face Blog: Preference Optimization for VLMs](https://huggingface.co/blog/dpo_vlm)
-- [Hugging Face Blog: Vision Language Models](https://huggingface.co/blog/vlms)
-- [Hugging Face Blog: SmolVLM](https://huggingface.co/blog/smolvlm)  
-- [Hugging Face Model: SmolVLM-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct)
-- [CLIP: Learning Transferable Visual Models from Natural Language Supervision](https://arxiv.org/abs/2103.00020)  
-- [Align Before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://arxiv.org/abs/2107.07651)  
diff --git a/zh-cn/5_vision_language_models/images/VLM_Architecture.png b/zh-cn/5_vision_language_models/images/VLM_Architecture.png
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diff --git a/zh-cn/5_vision_language_models/notebooks/vlm_sft_sample.ipynb b/zh-cn/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
deleted file mode 100644
index 9ddf65a6..00000000
--- a/zh-cn/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
+++ /dev/null
@@ -1,461 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 微调一个 VLM \n",
-    "\n",
-    "本文将展示如何用 `trl` 库的 `SFTTrainer` 微调 `HuggingFaceTB/SmolVLM-Instruct` 模型。文中的代码均可运行，你可选择不同的微调数据集尝试训练。\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>练习：用 SFTTrainer 微调 SmolVLM </h2>\n",
-    "    <p>从 Hugging Face hub 找一个数据，在该数据集上微调模型 </p> \n",
-    "    <p><b>难度等级</b></p>\n",
-    "    <p>🐢 用 `HuggingFaceM4/ChartQA` 数据集进行 SFT </p>\n",
-    "    <p>🐕 用微调过的模型生成回答，和未微调的模型进行效果对比</p>\n",
-    "    <p>🦁 尝试使用不同的数据集进行微调训练，并对比效果</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install the requirements in Google Colab\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "# Authenticate to Hugging Face\n",
-    "from huggingface_hub import login\n",
-    "login()\n",
-    "\n",
-    "# for convenience you can create an environment variable containing your hub token as HF_TOKEN"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import dependencies\n",
-    "\n",
-    "import torch\n",
-    "import os\n",
-    "from transformers import AutoProcessor, AutoModelForVision2Seq, BitsAndBytesConfig\n",
-    "from transformers.image_utils import load_image"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`low_cpu_mem_usage` was None, now default to True since model is quantized.\n",
-      "You shouldn't move a model that is dispatched using accelerate hooks.\n",
-      "Some kwargs in processor config are unused and will not have any effect: image_seq_len. \n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "quantization_config = BitsAndBytesConfig(load_in_4bit=True, \n",
-    "                                         bnb_4bit_use_double_quant=True, \n",
-    "                                         bnb_4bit_quant_type=\"nf4\", \n",
-    "                                         bnb_4bit_compute_dtype=torch.bfloat16)\n",
-    "model_name = \"HuggingFaceTB/SmolVLM-Instruct\"\n",
-    "model = AutoModelForVision2Seq.from_pretrained(\n",
-    "    model_name,\n",
-    "    quantization_config=quantization_config,\n",
-    "    torch_dtype=torch.bfloat16,\n",
-    ").to(device)\n",
-    "processor = AutoProcessor.from_pretrained(\"HuggingFaceTB/SmolVLM-Instruct\")\n",
-    "\n",
-    "# Set our name for the finetune to be saved &/ uploaded to\n",
-    "finetune_name = \"SmolVLM-FT-MyDataset\"\n",
-    "finetune_tags = [\"smol-course\", \"module_5\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 数据集准备\n",
-    "\n",
-    "我们将加载一个示例数据集，并对其进行格式化以用于训练。该数据集应构建为输入-输出对的形式，其中每个输入是一个提示语（包含文本和图像），输出是模型预期的响应。\n",
-    "\n",
-    "**TRL 将根据模型的聊天模板对输入消息进行格式化。** 它们会被整理成一个字典列表，字典包含的 key 有 `role` 和 `content`。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['image', 'query', 'label', 'human_or_machine'],\n",
-       "        num_rows: 28299\n",
-       "    })\n",
-       "    val: Dataset({\n",
-       "        features: ['image', 'query', 'label', 'human_or_machine'],\n",
-       "        num_rows: 1920\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['image', 'query', 'label', 'human_or_machine'],\n",
-       "        num_rows: 2500\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Load a sample dataset\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "# TODO: define your dataset and config using the path and name parameters\n",
-    "dataset_name = \"HuggingFaceM4/ChartQA\"\n",
-    "ds = load_dataset(path=dataset_name)\n",
-    "ds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=314x281 at 0x7D354DDE73D0>, 'query': 'What is the sum favourable  value in the year 2014 and 2015?', 'label': ['95'], 'human_or_machine': 0, 'image_size': ((314, 281),)}\n",
-      "Query: What is the sum favourable  value in the year 2014 and 2015?\n",
-      "Expected Answer: 95\n",
-      "Model Prediction: User:<image>What is the sum favourable  value in the year 2014 and 2015?\n",
-      "Answer: 91.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "sample = ds['train'][3]\n",
-    "\n",
-    "# Display the structure of a single sample\n",
-    "sample['image_size'] = sample['image'].size,\n",
-    "\n",
-    "# Visualize the image and related metadata\n",
-    "plt.imshow(sample['image'])\n",
-    "plt.axis(\"off\")\n",
-    "plt.title(\"Sample Chart Image\")\n",
-    "plt.show()\n",
-    "\n",
-    "print(sample)\n",
-    "\n",
-    "# Preprocess the sample\n",
-    "prompt = [{\"role\": \"user\", \"content\": [{\"type\": \"image\"}, {\"type\": \"text\", \"text\": sample[\"query\"]}]}]\n",
-    "formatted_query = processor.apply_chat_template(prompt, tokenize=False)\n",
-    "\n",
-    "inputs = processor(\n",
-    "    images=sample[\"image\"], \n",
-    "    text=formatted_query, \n",
-    "    return_tensors=\"pt\"\n",
-    ").to(device)\n",
-    "inputs = {key: val.to(device, dtype=torch.bfloat16) if val.dtype == torch.float else val.to(device) for key, val in inputs.items()}\n",
-    "\n",
-    "# Generate predictions\n",
-    "with torch.no_grad():\n",
-    "    outputs = model.generate(**inputs,\n",
-    "                             max_length=1600)\n",
-    "\n",
-    "# Decode the prediction\n",
-    "prediction = processor.batch_decode(outputs, skip_special_tokens=True)\n",
-    "\n",
-    "# Display the result\n",
-    "print(f\"Query: {sample['query']}\")\n",
-    "print(f\"Expected Answer: {sample['label'][0]}\")\n",
-    "print(f\"Model Prediction: {prediction[0]}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 配置 LoRA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "trainable params: 2,568,192 || all params: 2,248,841,072 || trainable%: 0.1142\n"
-     ]
-    }
-   ],
-   "source": [
-    "from peft import LoraConfig, get_peft_model\n",
-    "\n",
-    "# Configure LoRA\n",
-    "peft_config = LoraConfig(\n",
-    "    lora_alpha=16,\n",
-    "    lora_dropout=0.05,\n",
-    "    r=8,\n",
-    "    bias=\"none\",\n",
-    "    target_modules=[\"q_proj\", \"v_proj\"],\n",
-    "    task_type=\"CAUSAL_LM\",\n",
-    ")\n",
-    "\n",
-    "# Apply PEFT model adaptation\n",
-    "peft_model = get_peft_model(model, peft_config)\n",
-    "\n",
-    "# Print trainable parameters\n",
-    "peft_model.print_trainable_parameters()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 配置 Trainer\n",
-    "\n",
-    "通过调整 `Trainer` 的各种参数，我们可以控制训练过程。这些参数包括训练步数、batch size、学习率以及评估策略。你可根据具体需求和计算资源来调整这些参数。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def collate_fn(examples):\n",
-    "    # System message template for the VLM\n",
-    "    system_message = \"\"\"You are a Vision Language Model specialized in interpreting visual data from chart images.\n",
-    "    Your task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\n",
-    "    The charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\n",
-    "    Focus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.\"\"\"\n",
-    "\n",
-    "    # Initialize lists for text and image inputs\n",
-    "    text_inputs = []\n",
-    "    image_inputs = []\n",
-    "\n",
-    "    # Process all examples in one loop\n",
-    "    for example in examples:\n",
-    "        # Format the chat structure for the processor\n",
-    "        formatted_example = {\n",
-    "            \"messages\": [\n",
-    "                {\n",
-    "                    \"role\": \"system\",\n",
-    "                    \"content\": [{\"type\": \"text\", \"text\": system_message}],\n",
-    "                },\n",
-    "                {\n",
-    "                    \"role\": \"user\",\n",
-    "                    \"content\": [\n",
-    "                        {\n",
-    "                            \"type\": \"image\",\n",
-    "                        },\n",
-    "                        {\n",
-    "                            \"type\": \"text\",\n",
-    "                            \"text\": example[\"query\"],\n",
-    "                        },\n",
-    "                    ],\n",
-    "                },\n",
-    "            ]\n",
-    "        }\n",
-    "        # Apply chat template and strip extra spaces\n",
-    "        text_inputs.append(processor.apply_chat_template(formatted_example[\"messages\"], tokenize=False).strip())\n",
-    "        \n",
-    "        # Ensure images are in RGB mode\n",
-    "        image = example[\"image\"]\n",
-    "        if image.mode != 'RGB':\n",
-    "            image = image.convert('RGB')\n",
-    "        image_inputs.append( [image] )\n",
-    "\n",
-    "    # Tokenize the texts and process the images\n",
-    "    batch = processor(\n",
-    "        text=text_inputs,\n",
-    "        images=image_inputs,\n",
-    "        return_tensors=\"pt\",\n",
-    "        padding=True\n",
-    "    )\n",
-    "\n",
-    "    # Clone input IDs for labels\n",
-    "    labels = batch[\"input_ids\"].clone()\n",
-    "    labels[labels == processor.tokenizer.pad_token_id] = -100  # Mask padding tokens in labels\n",
-    "\n",
-    "    # Ensure image_token is converted to string if it is an AddedToken\n",
-    "    # In some processor, processor.image_token return a list for each image.\n",
-    "    # TODO: AutoProcessor.from_pretrained(\"HuggingFaceTB/SmolVLM-Instruct\") only have one ?\n",
-    "    image_token_id = processor.tokenizer.convert_tokens_to_ids(str(processor.image_token))\n",
-    "\n",
-    "    # Mask image token IDs in the labels\n",
-    "    labels[labels == image_token_id] = -100\n",
-    "\n",
-    "    # Add labels back to the batch\n",
-    "    batch[\"labels\"] = labels\n",
-    "\n",
-    "\n",
-    "    return batch\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from trl import SFTConfig, SFTTrainer\n",
-    "\n",
-    "# Configure the Trainer\n",
-    "training_args = SFTConfig(\n",
-    "    output_dir=\"sft_output\",  # Directory to save the model\n",
-    "    num_train_epochs=3,                     # number of training epochs\n",
-    "    per_device_train_batch_size=1,          # batch size per device during training\n",
-    "    gradient_accumulation_steps=16,         # number of steps before performing a backward/update pass\n",
-    "    gradient_checkpointing=True,            # use gradient checkpointing to save memory\n",
-    "    optim=\"adamw_torch_fused\",              # use fused adamw optimizer\n",
-    "    logging_steps=5,                        # log every 10 steps\n",
-    "    save_strategy=\"epoch\",                  # save checkpoint every epoch\n",
-    "    learning_rate=2e-4,                     # learning rate, based on QLoRA paper\n",
-    "    bf16=True,                              # use bfloat16 precision\n",
-    "    tf32=True,                              # use tf32 precision\n",
-    "    max_grad_norm=0.3,                      # max gradient norm based on QLoRA paper\n",
-    "    warmup_ratio=0.03,                      # warmup ratio based on QLoRA paper\n",
-    "    lr_scheduler_type=\"constant\",           # use constant learning rate scheduler\n",
-    "    push_to_hub=True,                       # push model to hub\n",
-    "    gradient_checkpointing_kwargs = {\"use_reentrant\": False}, # use reentrant checkpointing\n",
-    "    # dataloader_num_workers=16, \n",
-    "    dataset_text_field=\"\", # need a dummy field for collator\n",
-    "    dataset_kwargs = {\"skip_prepare_dataset\": True}, # important for collator\n",
-    "    remove_unused_columns = False                    # necessary else features except label will be removed\n",
-    ")\n",
-    "# Initialize the Trainer\n",
-    "trainer = SFTTrainer(\n",
-    "    model=model,\n",
-    "    args=training_args,\n",
-    "    train_dataset=ds[\"train\"],\n",
-    "    eval_dataset=ds[\"test\"],\n",
-    "    data_collator=collate_fn,\n",
-    "    peft_config=peft_config,\n",
-    "    tokenizer=processor.tokenizer,\n",
-    ")\n",
-    "\n",
-    "# TODO: 🦁 🐕 align the SFTTrainer params with your chosen dataset. For example, if you are using the `bigcode/the-stack-smol` dataset, you will need to choose the `content` column`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 训练模型\n",
-    "\n",
-    "训练器配置完成后，我们现在就可以着手训练模型了。训练过程将包括对数据集进行迭代，计算损失值，并更新模型参数以最小化该损失。 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6b4e2897ddbc47fa90254bcf12ce92f6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/5304 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Train the model\n",
-    "trainer.train()\n",
-    "\n",
-    "# Save the model\n",
-    "trainer.save_model(f\"./{finetune_name}\")\n",
-    "\n",
-    "# Save to the huggingface hub if login (HF_TOKEN is set)\n",
-    "if os.getenv(\"HF_TOKEN\"):\n",
-    "    trainer.push_to_hub(tags=finetune_tags)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 💐 完成了！\n",
-    "\n",
-    "本文提供了一份使用 `SFTTrainer` 对 `HuggingFaceTB/SmolVLM` 模型进行微调的分步指南。按照这些步骤操作，你可以让模型更有效地执行特定任务。如果你想继续学习本课程，不妨尝试以下步骤：\n",
-    "\n",
-    "- 尝试更高难度等级的练习题。\n",
-    "- 对 GitHub 上的 PRs 进行review。\n",
-    "- 通过 GitHub 提出 issue 或 PR，为完善本课程材料贡献力量。\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py310",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/zh-cn/5_vision_language_models/notebooks/vlm_usage_sample.ipynb b/zh-cn/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
deleted file mode 100644
index 7554d217..00000000
--- a/zh-cn/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
+++ /dev/null
@@ -1,473 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 用 VLM 处理图片或视频相关的多模态任务\n",
-    "\n",
-    "本文以 `HuggingFaceTB/SmolVLM-Instruct` 的 4bit 量化模型为例，展示如何处理以下多模态任务：\n",
-    "- 视觉问题回答（VQA）：基于图片内容回答问题\n",
-    "- 文本识别（OCR）：提取并识别图片中的文本文字\n",
-    "- 视频理解：通过分析一系列的视频帧，来生成对视频的描述\n",
-    "\n",
-    "通过有效地组织提示语（文本 + 视觉），你可以将该模型应用于诸多领域，如场景理解、文档分析以及动态视觉推理。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install the requirements in Google Colab\n",
-    "# !pip install transformers datasets trl huggingface_hub bitsandbytes\n",
-    "\n",
-    "# Authenticate to Hugging Face\n",
-    "from huggingface_hub import notebook_login\n",
-    "notebook_login()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`low_cpu_mem_usage` was None, now default to True since model is quantized.\n",
-      "You shouldn't move a model that is dispatched using accelerate hooks.\n",
-      "Some kwargs in processor config are unused and will not have any effect: image_seq_len. \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'longest_edge': 1536}\n"
-     ]
-    }
-   ],
-   "source": [
-    "import torch, PIL\n",
-    "from transformers import AutoProcessor, AutoModelForVision2Seq, BitsAndBytesConfig\n",
-    "from transformers.image_utils import load_image\n",
-    "\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "quantization_config = BitsAndBytesConfig(load_in_4bit=True)\n",
-    "model_name = \"HuggingFaceTB/SmolVLM-Instruct\"\n",
-    "model = AutoModelForVision2Seq.from_pretrained(\n",
-    "    model_name,\n",
-    "    quantization_config=quantization_config,\n",
-    ").to(device)\n",
-    "processor = AutoProcessor.from_pretrained(\"HuggingFaceTB/SmolVLM-Instruct\")\n",
-    "\n",
-    "print(processor.image_processor.size)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 图像级输入的情况"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们先探索生成图片的文本描述，以及针对图片提问的情况。后面我们也会探索多图输入的情况。\n",
-    "\n",
-    "### 1. 单副图片的文本描述生成"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<img src=\"https://cdn.pixabay.com/photo/2024/11/20/09/14/christmas-9210799_1280.jpg\"/>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<img src=\"https://cdn.pixabay.com/photo/2024/11/23/08/18/christmas-9218404_1280.jpg\"/>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from IPython.display import Image, display\n",
-    "\n",
-    "image_url1 = \"https://cdn.pixabay.com/photo/2024/11/20/09/14/christmas-9210799_1280.jpg\"\n",
-    "display(Image(url=image_url1))\n",
-    "\n",
-    "image_url2 = \"https://cdn.pixabay.com/photo/2024/11/23/08/18/christmas-9218404_1280.jpg\"\n",
-    "display(Image(url=image_url2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/duydl/Miniconda3/envs/py310/lib/python3.10/site-packages/bitsandbytes/nn/modules.py:451: UserWarning: Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference or training speed.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['User:<image>Can you describe the image?\\nAssistant: The image is a scene of a person walking in a forest. The person is wearing a coat and a cap. The person is holding the hand of another person. The person is walking on a path. The path is covered with dry leaves. The background of the image is a forest with trees.']\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Load  one image\n",
-    "image1 = load_image(image_url1)\n",
-    "\n",
-    "# Create input messages\n",
-    "messages = [\n",
-    "    {\n",
-    "        \"role\": \"user\",\n",
-    "        \"content\": [\n",
-    "            {\"type\": \"image\"},\n",
-    "            {\"type\": \"text\", \"text\": \"Can you describe the image?\"}\n",
-    "        ]\n",
-    "    },\n",
-    "]\n",
-    "\n",
-    "# Prepare inputs\n",
-    "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
-    "inputs = processor(text=prompt, images=[image1], return_tensors=\"pt\")\n",
-    "inputs = inputs.to(device)\n",
-    "\n",
-    "# Generate outputs\n",
-    "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
-    "generated_texts = processor.batch_decode(\n",
-    "    generated_ids,\n",
-    "    skip_special_tokens=True,\n",
-    ")\n",
-    "\n",
-    "print(generated_texts)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2. 对比多幅图片\n",
-    "模型也可以处理并对比多幅图片。我们接下来让模型总结一下两张图片的共同主题。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['User:<image>What event do they both represent?\\nAssistant: Christmas.']\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "# Load images\n",
-    "image2 = load_image(image_url2)\n",
-    "\n",
-    "# Create input messages\n",
-    "messages = [\n",
-    "    # {\n",
-    "    #     \"role\": \"user\",\n",
-    "    #     \"content\": [\n",
-    "    #         {\"type\": \"image\"},\n",
-    "    #         {\"type\": \"image\"},\n",
-    "    #         {\"type\": \"text\", \"text\": \"Can you describe the two images?\"}\n",
-    "    #     ]\n",
-    "    # },\n",
-    "    {\n",
-    "        \"role\": \"user\",\n",
-    "        \"content\": [\n",
-    "            {\"type\": \"image\"},\n",
-    "            {\"type\": \"image\"},\n",
-    "            {\"type\": \"text\", \"text\": \"What event do they both represent?\"}\n",
-    "        ]\n",
-    "    },\n",
-    "]\n",
-    "\n",
-    "# Prepare inputs\n",
-    "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
-    "inputs = processor(text=prompt, images=[image1, image2], return_tensors=\"pt\")\n",
-    "inputs = inputs.to(device)\n",
-    "\n",
-    "# Generate outputs\n",
-    "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
-    "generated_texts = processor.batch_decode(\n",
-    "    generated_ids,\n",
-    "    skip_special_tokens=True,\n",
-    ")\n",
-    "\n",
-    "print(generated_texts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 🔠 文字识别（OCR）\n",
-    "\n",
-    "VLM 也可以识别并提取文字，这使得它也可以用来处理诸如文档分析之类的任务。\n",
-    "你也可以试试使用文字更密集的图片。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<img src=\"https://cdn.pixabay.com/photo/2020/11/30/19/23/christmas-5792015_960_720.png\"/>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['User:<image>What is written?\\nAssistant: MERRY CHRISTMAS AND A HAPPY NEW YEAR']\n"
-     ]
-    }
-   ],
-   "source": [
-    "document_image_url = \"https://cdn.pixabay.com/photo/2020/11/30/19/23/christmas-5792015_960_720.png\"\n",
-    "display(Image(url=document_image_url))\n",
-    "\n",
-    "# Load the document image\n",
-    "document_image = load_image(document_image_url)\n",
-    "\n",
-    "# Create input message for analysis\n",
-    "messages = [\n",
-    "    {\n",
-    "        \"role\": \"user\",\n",
-    "        \"content\": [\n",
-    "            {\"type\": \"image\"},\n",
-    "            {\"type\": \"text\", \"text\": \"What is written?\"}\n",
-    "        ]\n",
-    "    }\n",
-    "]\n",
-    "\n",
-    "# Prepare inputs\n",
-    "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
-    "inputs = processor(text=prompt, images=[document_image], return_tensors=\"pt\")\n",
-    "inputs = inputs.to(device)\n",
-    "\n",
-    "# Generate outputs\n",
-    "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
-    "generated_texts = processor.batch_decode(\n",
-    "    generated_ids,\n",
-    "    skip_special_tokens=True,\n",
-    ")\n",
-    "\n",
-    "print(generated_texts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 视频级输入的情况\n",
-    "\n",
-    "视觉语言模型（VLMs）可以通过提取关键帧并按时间顺序对其进行推理，从而间接处理视频。虽然视觉语言模型缺乏专用视频模型的时间建模能力，但它们仍然能够：\n",
-    "\n",
-    "- 通过按顺序分析采样帧来描述动作或事件。\n",
-    "- 根据具有代表性的关键帧回答有关视频的问题。\n",
-    "- 通过组合多个帧的文本描述来总结视频内容。\n",
-    "\n",
-    "我们用这个视频作为例子：\n",
-    "\n",
-    "<video width=\"640\" height=\"360\" controls>\n",
-    "  <source src=\"https://cdn.pixabay.com/video/2023/10/28/186794-879050032_large.mp4\" type=\"video/mp4\">\n",
-    "  Your browser does not support the video tag.\n",
-    "</video>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# !pip install opencv-python"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from IPython.display import Video\n",
-    "import cv2\n",
-    "import numpy as np\n",
-    "\n",
-    "def extract_frames(video_path, max_frames=50, target_size=None):\n",
-    "    cap = cv2.VideoCapture(video_path)\n",
-    "    if not cap.isOpened():\n",
-    "        raise ValueError(f\"Could not open video: {video_path}\")\n",
-    "    \n",
-    "    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
-    "    frame_indices = np.linspace(0, total_frames - 1, max_frames, dtype=int)\n",
-    "\n",
-    "    frames = []\n",
-    "    for idx in frame_indices:\n",
-    "        cap.set(cv2.CAP_PROP_POS_FRAMES, idx)\n",
-    "        ret, frame = cap.read()\n",
-    "        if ret:\n",
-    "            frame = PIL.Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n",
-    "            if target_size:\n",
-    "                frames.append(resize_and_crop(frame, target_size))\n",
-    "            else:\n",
-    "                frames.append(frame)\n",
-    "    cap.release()\n",
-    "    return frames\n",
-    "\n",
-    "def resize_and_crop(image, target_size):\n",
-    "    width, height = image.size\n",
-    "    scale = target_size / min(width, height)\n",
-    "    image = image.resize((int(width * scale), int(height * scale)), PIL.Image.Resampling.LANCZOS)\n",
-    "    left = (image.width - target_size) // 2\n",
-    "    top = (image.height - target_size) // 2\n",
-    "    return image.crop((left, top, left + target_size, top + target_size))\n",
-    "\n",
-    "# Video link\n",
-    "video_link = \"https://cdn.pixabay.com/video/2023/10/28/186794-879050032_large.mp4\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Response: User: Following are the frames of a video in temporal order.<image>Describe what the woman is doing.\n",
-      "Assistant: The woman is hanging an ornament on a Christmas tree.\n"
-     ]
-    }
-   ],
-   "source": [
-    "question = \"Describe what the woman is doing.\"\n",
-    "\n",
-    "def generate_response(model, processor, frames, question):\n",
-    "\n",
-    "    image_tokens = [{\"type\": \"image\"} for _ in frames]\n",
-    "    messages = [\n",
-    "        {\n",
-    "            \"role\": \"user\",\n",
-    "            \"content\": [{\"type\": \"text\", \"text\": \"Following are the frames of a video in temporal order.\"}, *image_tokens, {\"type\": \"text\", \"text\": question}]\n",
-    "        }\n",
-    "    ]\n",
-    "    inputs = processor(\n",
-    "        text=processor.apply_chat_template(messages, add_generation_prompt=True),\n",
-    "        images=frames,\n",
-    "        return_tensors=\"pt\"\n",
-    "    ).to(model.device)\n",
-    "\n",
-    "    outputs = model.generate(\n",
-    "        **inputs, max_new_tokens=100, num_beams=5, temperature=0.7, do_sample=True, use_cache=True\n",
-    "    )\n",
-    "    return processor.decode(outputs[0], skip_special_tokens=True)\n",
-    "\n",
-    "\n",
-    "# Extract frames from the video\n",
-    "frames = extract_frames(video_link, max_frames=15, target_size=384)\n",
-    "\n",
-    "processor.image_processor.size = (384, 384)\n",
-    "processor.image_processor.do_resize = False\n",
-    "# Generate response\n",
-    "response = generate_response(model, processor, frames, question)\n",
-    "\n",
-    "# Display the result\n",
-    "# print(\"Question:\", question)\n",
-    "print(\"Response:\", response)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 💐 完成了！\n",
-    "\n",
-    "本文展示了使用视觉语言模型的多个案例，按照文中的步骤操作，你可以对视觉语言模型展开很多试验，探索其应用场景。\n",
-    "\n",
-    "后续你还可以：\n",
-    "\n",
-    "- 尝试视觉语言模型（VLMs）更多的用例。\n",
-    "- 对 GitHub 上的 PRs 进行review。\n",
-    "- 通过 GitHub 提出 issue 或 PR，为完善本课程材料贡献力量。\n",
-    "\n",
-    "祝你学习愉快！ 🌟"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py310",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/zh-cn/5_vision_language_models/vlm_finetuning.md b/zh-cn/5_vision_language_models/vlm_finetuning.md
deleted file mode 100644
index 45ea6cd6..00000000
--- a/zh-cn/5_vision_language_models/vlm_finetuning.md
+++ /dev/null
@@ -1,102 +0,0 @@
-# VLM 微调
-
-## 高效微调
-
-### 量化
-
-量化可降低模型权重与激活值的精度，大幅减少内存使用并加快计算速度。例如，从 `float32` 转换为 `bfloat16`，每个参数的内存需求减半，同时还能维持性能。若要进行更激进的压缩，可采用 8 位和 4 位量化，进一步降低内存使用，不过这会牺牲一定的准确性。这些技术既能应用于模型，也能用于优化器设置，从而使在资源有限的硬件上高效训练成为可能。
-
-### PEFT & LoRA
-
-专注于学习紧凑的秩分解矩阵，同时保持原始模型的权重不变。这极大地减少了可训练参数的数量，显著降低了资源需求。当 LoRA 与 PEFT 相结合时，仅通过调整一小部分可训练参数就能对大型模型进行微调。这种方法对于特定任务的微调特别有效，它能将数十亿个可训练参数减少到仅数百万个，同时维持性能。
-
-### Batch Size 的优化
-
-为优化微调时的 batch size 大小，可先从较大的值开始，如果出现内存不足（OOM）错误，则将其减小。可通过增加 `gradient_accumulation_steps` 来补偿，从而在多次更新过程中有效地维持总批量大小。此外，启用 `gradient_checkpointing` 可通过在反向传播期间重新计算中间状态来降低内存使用，以计算时间为代价来减少激活内存需求。这些策略可使硬件利用率最大化，并有助于克服内存限制。
-
-```python
-from transformers import TrainingArguments
-
-training_args = TrainingArguments(
-    output_dir="./fine_tuned_model",  # Directory for model checkpoints
-    per_device_train_batch_size=4,   # Batch size per device (GPU/TPU)
-    num_train_epochs=3,              # Total training epochs
-    learning_rate=5e-5,              # Learning rate
-    save_steps=1000,                 # Save checkpoint every 1000 steps
-    bf16=True,                       # Use mixed precision for training
-    gradient_checkpointing=True,     # Enable to reduce activation memory usage
-    gradient_accumulation_steps=16,  # Accumulate gradients over 16 steps
-    logging_steps=50                 # Log metrics every 50 steps
-)
-```
-
-## **有监督微调（SFT）**
-
-有监督微调（Supervised Fine-tuning 或 SFT）通过利用包含配对输入（例如图像和相应文本）的标注数据集，使预训练的视觉语言模型（VLM）适应特定任务。这种方法可增强模型执行特定领域或特定任务功能的能力，如视觉问答（VQA）、图像文本描述生成、图表解析。
-
-### **简介**
-
-当你需要视觉语言模型（VLM）专门从事某个特定领域，或解决基础模型无法解决的问题时，有监督微调（SFT）就显得至关重要。例如，如果该模型在处理特殊的视觉特征或特定领域的术语方面表现不佳，有监督微调可让它从标注数据中学习，获得处理这些领域的任务的能力。
-
-While SFT is highly effective, it has notable limitations:
-虽然 SFT 非常高效，但它也有很多明显的局限性：
-- **数据依赖**：针对特定任务的高质量标注数据是必须要有的
-- **计算资源**：对大型的 VLM 进行微调需耗费大量计算资源
-- **过拟合的风险**：如果微调范围过窄，模型可能会失去其泛化能力
-
-尽管存在这些挑战，有监督微调（SFT）仍是一种强大的技术，可在特定场景中提升模型性能。
-
-### **实践方法**
-
-1. **数据准备**：从一个将图像与文本（如问题和答案）进行配对的标注数据集入手。例如，在图表分析这类任务中，“HuggingFaceM4/ChartQA” 数据集包含图表图像、查询内容以及简洁的回答。
-2. **模型设置**：加载适合该任务的预训练视觉语言模型（VLM），比如 `HuggingFaceTB/SmolVLM - Instruct`，以及用于处理文本和图像输入的模型。针对监督学习并结合硬件条件对模型进行配置。
-3. **微调过程**：
-  - **数据格式化**：将数据集构建成类似聊天机器人的格式，把系统消息、用户查询以及相应答案进行配对。
-  - **训练配置**：使用诸如 Hugging Face 的 `TrainingArguments` 或 TRL 的 `SFTConfig` 等工具来设置训练参数。这些参数包括 batch size、学习率和梯度累积步数，以优化资源使用。
-  - **优化技术**：在训练过程中使用 **gradient checkpointing** 来节省内存。采用量化模型降低内存需求并加快计算速度。
-  - 利用 TRL 库中的 `SFTTrainer` 训练器来简化训练流程。
-
-## 偏好优化
-
-偏好优化，尤其是直接偏好优化（DPO），旨在训练视觉语言模型（VLM）使其与人类偏好保持一致。该模型并非严格遵循预先设定的指令，而是学习对人类主观上更青睐的输出结果作为优先选择。这种方法在涉及创造性判断、精细化推理或存在多种可接受答案的任务中尤为实用。
-
-### **简介**
-
-偏好优化适用于那些人类主观偏好对任务成功起着关键作用的场景。通过在反映人类偏好的数据集上进行微调，直接偏好优化（DPO）提升了模型生成在上下文和风格上都符合用户期望的回复的能力。这种方法在创意写作、客户互动或多项选择等场景的任务中特别有效。
-
-尽管偏好优化有诸多益处，但它也面临一些挑战：
-
-- **数据质量**：需要高质量且带有偏好注释的数据集，这往往使数据收集成为瓶颈。
-- **复杂性**：训练可能涉及复杂的过程，比如对偏好进行成对采样以及平衡计算资源。
-
-偏好数据集必须清晰体现候选输出之间的偏好差异。例如，一个数据集可能会将一个问题与两个回复配对，一个是受偏好的，另一个则不太能被接受。模型会学习预测出那个受偏好的回复，即便它并非完全正确，只要它更符合人类的判断即可。
-
-### **实践方法**
-1. **数据准备**  
-   准备好一个带有偏好注释的数据集对训练至关重要。每个数据样本一般包含一个问题和一副图片，以及两个候选回答：一个是受偏好的，另一个则不太能被接受。比如：
-   - **问题**: How many families?  
-     - **不倾向的回答**: The image does not provide any information about families.  
-     - **倾向的回答**: The image shows a Union Organization table setup with 18,000 families.  
-
-   该数据集引导模型优先选择偏好的回答，即便这些回答并非尽善尽美。
-
-2. **模型配置**  
-   加载一个预训练的视觉语言模型，准备好处理文本和图像输入的模型，结合 Hugging Face 的 `TRL` 库（支持 DPO 算法）写训练程序。针对监督学习进行模型配置，并使其适配你的硬件。
-
-3. **训练流程** 
-   训练需要配置 DPO 相关参数，主要有这些方向需要注意：
-
-   - **数据集格式**：对每个样本进行结构化，整理好问题、图片、候选回答。
-   - **损失函数**：需要使用和偏向性相关的函数，以此训练模型去选择倾向的回答。
-   - **高效训练**：结合量化、梯度累积、LoRA 等技术，减少显存使用和计算量。
-
-## 学习资源
-
-- [Hugging Face Learn: Supervised Fine-Tuning VLMs](https://huggingface.co/learn/cookbook/fine_tuning_vlm_trl) 
-- [Hugging Face Learn: Supervised Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_smol_vlm_sft_trl)  
-- [Hugging Face Learn: Preference Optimization Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_vlm_dpo_smolvlm_instruct)  
-- [Hugging Face Blog: Preference Optimization for VLMs](https://huggingface.co/blog/dpo_vlm)
-
-## 接下来
-
-⏩ 学习 [vlm_finetune_sample_cn.ipynb](./notebooks/vlm_finetune_sample.ipynb)，动手实现 VLM 的偏好对齐训练。
\ No newline at end of file
diff --git a/zh-cn/5_vision_language_models/vlm_usage.md b/zh-cn/5_vision_language_models/vlm_usage.md
deleted file mode 100644
index e35e1091..00000000
--- a/zh-cn/5_vision_language_models/vlm_usage.md
+++ /dev/null
@@ -1,80 +0,0 @@
-# 视觉语言模型
-
-视觉语言模型（VLMs）弥合了图像与文本之间的鸿沟，使得诸如生成图像的文字描述、基于视觉内容回答问题，以及理解文本与视觉数据之间的关系等高级任务得以实现。其架构旨在无缝处理这两种模态的数据。
-
-### 模型架构
-
-视觉语言模型将图像处理组件与文本生成模型相结合，以实现统一的理解。其架构的主要组成部分包括：
-
-![VLM Architecture](./images/VLM_Architecture.png)
-
-- **图像编码器（Image Encoder）**：用来将图片转化为用数值表示的特征，通常使用经训练过的 CLIP 或 ViT。
-- **特征映射模块（Embedding Projector）**：用来将图片特征映射到文本特征空间，使其与文本特征兼容，通常使用全联接层或线性变换实现。
-- **文本解码器（Text Decoder）**：作为文本生成模块，将融合的多模态信息转换成连贯的文字。举例来说，这部分可以用 Llama、Vicuna 等生成模型实现。
-- **多模态映射模块（Multimodal Projector）**：: 提供了一个额外的层，来融合图像与文本的表征。对于像 LLaVA 这样的模型而言，这一层对于在两种模态之间建立更紧密的联系至关重要。
-
-大多数视觉语言模型（VLMs）会利用预训练的图像编码器和文本解码器，并通过在图像-文本这样的成对数据上进行微调，使二者的特征相互对齐。这种方法既能提高训练效率，又能让模型有效地实现泛化。
-
-### 使用方法
-
-![VLM Process](./images/VLM_Process.png)
-
-视觉语言模型（VLMs）被应用于一系列多模态任务。凭借其适应性，只需进行不同程度的微调，就能在多个领域发挥作用：
-
-- **图像字幕生成**：为图像生成描述性文字。
-- **视觉问答（VQA）**：针对图像内容回答相关问题。
-- **跨模态检索**：为给定图像找到对应的文本，或反之，为给定文本找到对应的图像。
-- **创意应用**：辅助设计、艺术创作，或生成引人入胜的多媒体内容。
-
-
-
-![VLM Usage](./images/VLM_Usage.png)
-
-训练和微调视觉语言模型（VLMs）依赖于高质量的数据集，这些数据集将图像与文本注释进行配对。像 Hugging Face 的 `transformers` 库这样的工具，为获取预训练的视觉语言模型提供了便捷途径，同时也简化了自定义微调的工作流程。
-
-### Chat Format
-
-很多视觉语言模型也是通过像聊天机器人一样的方式与用户互动的。这种格式包含以下信息：
-
-
-
-- **系统信息（system message）**：为模型设定角色、上下文，例如“你是一个小助理，将会帮我分析视觉数据”这样。
-- **用户查询（User queries）**：用户询问模型时输入给模型的信息，这包括视觉信息和文本信息
-- **模型回答（Assistant responses）**：模型给出的文本形式的回复
-
-这种对话结构直观易懂，符合用户预期，尤其适用于诸如客服或教育工具这类交互式应用程序。
-
-下面就是一个格式化的输入示例：
-
-```json
-[
-    {
-        "role": "system",
-        "content": [{"type": "text", "text": "You are a Vision Language Model specialized in interpreting visual data from chart images..."}]
-    },
-    {
-        "role": "user",
-        "content": [
-            {"type": "image", "image": "<image_data>"},
-            {"type": "text", "text": "What is the highest value in the bar chart?"}
-        ]
-    },
-    {
-        "role": "assistant",
-        "content": [{"type": "text", "text": "42"}]
-    }
-]
-```
-
-**多图输入或视频输入**
-
-视觉语言模型（VLMs）还能够处理多张图像，甚至是视频。方法是调整输入结构，以适应连续或并行的视觉输入。对于视频，可以提取多帧并将其作为图像进行处理，同时保持时间顺序。
-
-## 学习资源
-
-- [Hugging Face Blog: Vision Language Models](https://huggingface.co/blog/vlms)
-- [Hugging Face Blog: SmolVLM](https://huggingface.co/blog/smolvlm) 
-
-## 接下来
-
-⏩ 你可以尝试 [vlm_usage_sample_cn.ipynb](./notebooks/vlm_usage_sample.ipynb)，了解如何使用 SmolVLM 来处理各种任务。
\ No newline at end of file
diff --git a/zh-cn/6_synthetic_datasets/README.md b/zh-cn/6_synthetic_datasets/README.md
deleted file mode 100644
index 4a760d18..00000000
--- a/zh-cn/6_synthetic_datasets/README.md
+++ /dev/null
@@ -1,39 +0,0 @@
-# 合成数据集
-
-合成数据是人工生成的数据，用于模拟现实世界中的真实情况。它能够通过扩充或强化数据集，克服数据方面的局限。尽管合成数据此前已在某些应用场景中有所运用，但大语言模型使得合成数据集在语言模型的预训练、后训练以及评估过程中变得更为流行。
-
-我们将使用 [`distilabel`](https://distilabel.argilla.io/latest/)，这是一个为工程师提供合成数据和人工智能反馈的框架。如果需要基于经验证的研究论文来构建快速、可靠且可扩展的流程，该框架十分适用。如果你想深入了解该框架并学习最佳实践，你可以阅读[官方文档](https://distilabel.argilla.io/latest/)。
-
-## 章节概览
-
-语言模型领域的合成数据可以分为三类：指令（instructions）、偏好（preferences）和评论（critiques）。我们将重点关注前两类，因为它们涉及为指令微调和偏好对齐生成人工数据。在学习这两类的同时，我们也会涉及第三类某些方面的内容，因为这关乎借助模型评论和重写，来提升现有数据的质量。
-
-![Synthetic Data Taxonomies](./images/taxonomy-synthetic-data.png)
-
-## 内容目录
-
-### 1. [指令数据集](./instruction_datasets_cn.md)
-
-你将学习如何为指令微调（instruction tuning）生成指令数据集，这包括基本的提示语方式以及最近论文提出的更精细的方式。这里面，通过上下文学习并借助种子数据来生成指令微调数据集的方法包括 SelfInstruct 和 Magpie；进化改进指令微调数据的方法包括 EvolInstruct。你可以点击[这里](./instruction_datasets_cn.md)开始学习。
-
-### 2. [偏好数据集](./preference_datasets_cn.md)
-
-你讲学习如何为偏好对齐（preference alignment）去生成偏好数据集。我们将在上一节的基础上，额外生成别的回答。接下来，我们还会学习如何通过 EvolQuality 来优化这些回答。最终，我们会学习如何用 UltraFeedback 生成分数和评论，来评估这些回答，以此来获取偏好数据对。你可以点击[这里](./preference_datasets_cn.md)开始学习。
-
-### Exercise Notebooks
-
-| 标题 | 概述 | 练习 | 链接 | Colab |
-|-------|-------------|----------|------|-------|
-| 指令微调数据集 | 为指令微调生成数据集 | 🐢 为指令微调生成数据集 <br> 🐕 借助种子数据为指令微调生成数据集 <br> 🦁 借助种子数据和 EvolInstruct 为指令微调生成数据集 | [Link](./notebooks/instruction_sft_dataset_cn.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb) |
-| 偏好数据集 | 为偏好对齐生成数据集 | 🐢 为偏好对齐生成数据集 <br> 🐕 为偏好对齐生成数据集，对回答进行迭代优化 <br> 🦁 为偏好对齐生成数据集，对回答进行迭代优化，并借助对回答的评价 | [Link](./notebooks/preference_alignment_dataset_cn.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/6_synthetic_datasets/notebooks/preference_alignment_dataset.ipynb) |
-
-## Resources
-
-- [Distilabel 官方文档](https://distilabel.argilla.io/latest/)
-- [Synthetic Data Generator is UI app](https://huggingface.co/blog/synthetic-data-generator)
-- [SmolTalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk)
-- [Self-instruct 论文](https://arxiv.org/abs/2212.10560)
-- [Evol-Instruct 论文](https://arxiv.org/abs/2304.12244)
-- [Magpie 论文](https://arxiv.org/abs/2406.08464)
-- [UltraFeedback 论文](https://arxiv.org/abs/2310.01377)
-- [Deita 论文](https://arxiv.org/abs/2312.15685)
diff --git a/zh-cn/6_synthetic_datasets/images/pipeline.png b/zh-cn/6_synthetic_datasets/images/pipeline.png
deleted file mode 100644
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diff --git a/zh-cn/6_synthetic_datasets/instruction_datasets.md b/zh-cn/6_synthetic_datasets/instruction_datasets.md
deleted file mode 100644
index f402081a..00000000
--- a/zh-cn/6_synthetic_datasets/instruction_datasets.md
+++ /dev/null
@@ -1,204 +0,0 @@
-# 合成指令微调数据集
-
-在[指令微调](../1_instruction_tuning/README_CN.md)这一章节，我们学习了通过 SFT 的方法来微调模型。在本文中，我们将学习如何为 SFT 合成指令数据集，这涵盖：使用基本的提示语还生成指令微调数据集，以及使用一些论文中提出的更精细的方法。我们还可以使用种子数据进行上下文学习（in-context learning）来合成数据，相关的方法包括 SelfInstruct 和 Magpie。此外我们还会学习使用 EvolInstruct 来改进现有数据。最后，我们将学习如何使用 `distilabel` 的 `pipeline` 来规范化地进行指令微调数据集的生成。
-
-## 从提示语到数据
-
-合成数据听起来很高级，但它可以简单理解为通过有效的提示语从模型中提取知识来创建数据。反过来，你可以将此视为为特定任务生成数据的一种方式。挑战在于要进行有效提示，同时确保数据具有多样性和代表性。幸运的是，已有许多论文探讨了这个问题，在本文中，我们将研究其中一些有价值的论文。首先，我们将探索如何通过手动地提供提示语来生成合成数据。
-
-### 基本的提示语方式
-
-我们用一个基础的例子开始合成数据的学习。这里我们使用 [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) 模型，并使用 `distilabel` 中已经集成的 `transformers` 库来载入这个模型。在示例中，我们用 `TextGeneration` 类来生成一个 `prompt` 并基于此再生成 `completion`，这样一个问答就生成完毕了。
-
-首先，通过 `distilabel` 库来加载模型：
-
-```python
-from distilabel.llms import TransformersLLM
-from distilabel.steps.tasks import TextGeneration
-
-llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
-gen = TextGeneration(llm=llm)
-gen.load()
-```
-
-!!! 注意
-    `distilabel` 会把 `llm` 加载进内存。当我们在一个 notebook 中跑该代码时，我们还需要在结束时执行 `gen.unload()` 来防止内存持续占用。
-
-接下来，我们开始用 `llm` 来生成一个 `prompt` 作为问题。
-
-```python
-next(gen.process([{"instruction": "Generate a questions about the Hugging Face Smol-Course on small AI models."}]))
-# What is the purpose of Smol-Course?
-```
-
-最后，用 `prompt` 作为问题输入，来生成一个 `completion` 作为回答。
-
-```python
-next(gen.process([{"instruction": "What is the purpose of Smol-Course?"}]))
-# The Smol-Course is a platform designed to learning computer science concepts.
-```
-
-这样我们就得到了一个 `prompt` 和一个对应的 `completion`。重复这一简单步骤，就可以得到更多的类似数据。然而，这些数据的质量并不高，且没有考虑到不同任务领域的微妙差别。此外，重复这个过程使得我们的数据集缺乏多样性。不过，我们有办法解决这个问题。
-
-### SelfInstruct
-
-SelfInstruct 基于一个种子数据集生成新的指令数据。一条种子数据可以是一个单一的指令或者一段上下文。该过程通常从一系列初始的种子数据开始。然后语言模型被要求基于种子数据进行上下文学习，针对上下文提出新的问题。在 [distilabel 中的模板](https://github.com/argilla-io/distilabel/blob/main/src/distilabel/steps/tasks/templates/self-instruct.jinja2) 中包含了要求模型执行这一任务的提示语，大致是这样：
-
-```
-# Task Description
-Develop {{ num_instructions }} user queries that can be received by the given AI application and applicable to the provided context. Emphasize diversity in verbs and linguistic structures within the model's textual capabilities.
-
-# Context
-{{ input }}
-
-# Output
-```
-
-具体到代码实现上，我们首先要将 `llm` 传入 [SelfInstruct](https://distilabel.argilla.io/dev/components-gallery/tasks/selfinstruct/) 这个类的构造函数。这里我们假设用一段描述如何合成人工数据集的英文文字（就是代码中的`text`）作为上下文，来生成新的指令：
-
-```python
-from distilabel.steps.tasks import SelfInstruct
-
-self_instruct = SelfInstruct(llm=llm)
-self_instruct.load()
-
-text = "..."
-
-next(self_instruct.process([{"input": text}]))["instructions"][0]
-# What is the process of generating synthetic data through manual prompting?
-```
-
-这样生成的指令数据会好很多，且符合我们的实际任务领域。不过，我们还可以做得更好，方法是通过不断进化改进提示语。
-
-### EvolInstruct
-
-EvolInstruct 是一种提示语技术，它以已有的指令数据作为输入，不断改进，使得原数据进化成为更好的版本。这里，“更好”是因为我们对数据生成过程添加了新的标准和限制条件，且进行了深化、具体化、推理或复杂化处理。该过程可以重复多次，针对相同的指令数据生成不同的进化版本，获取比原始数据更好的数据。相关的系统提示语[在 distilabel 中也有实现](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/evol_instruct)，大致是这样：
-
-```
-I want you act as a Prompt Rewriter.
-Given prompt a prompt, rewrite it into a more complex version.
-Complicate the prompt based on the following criteria:
-{{ criteria }}
-
-# Prompt
-{{ input }}
-
-# Output
-```
-
-具体到代码实现上，我们首先要将 `llm` 传入 [SelfInstruct](https://distilabel.argilla.io/dev/components-gallery/tasks/evolinstruct/) 这个类的构造函数。这里我们使用在 [SelfInstruct](#selfinstruct) 这一小节生成的 `prompt` 来作为输入（就是代码中的 `text`），利用它得到一个进化过的版本。本示例中我们只进化一次：
-
-```python
-from distilabel.steps.tasks import EvolInstruct
-
-evol_instruct = EvolInstruct(llm=llm, num_evolutions=1)
-evol_instruct.load()
-
-text = "What is the process of generating synthetic data through manual prompting"
-
-next(evol_instruct.process([{"instruction": text}]))
-# What is the process of generating synthetic data through manual prompting?
-# And, how does the artificial intelligence system, GPT4, use machine learning algorithms to manipulate the input data into synthetic data?
-```
-
-经过进化，指令数据变得更复杂了，不过也丧失了一些原有数据的信息。所以我们也要知道，该方法也是一个双刃剑，我们需要对进化过的数据的质量格外关注。
-
-### Magpie
-
-Magpie 技术利用了语言模型自回归的特性以及前面学习到过的[聊天模板](../1_instruction_tuning/chat_templates_cn.md)。由于聊天模板在指令微调过程中为对话设定了固定的格式，清晰指出了话语是哪个角色说出来的（系统、用户、助手），在预测过程中，模型的输出也会遵循这个格式去走。这正是 Magpie 所利用的点。一开始，Magpie 直接输入给大语言模型一个聊天模板，但这个聊天模板只包含系统信息，结束于用户开始提问的地方（例如在 `<|im_start|>user\n` 这里结束）。然后利用大语言模型的自回归特性，模型会自己不停输出文本，直到输出了助手回答结束符那里（比如输出到 `<|im_end|>` 终止）。这样一段完整的聊天模板就生成了。这种方法可以让我们高效生成数据，并可以扩展到多轮对话情况。很多人也认为这个方法是可以让大语言模型吐出自己指令微调时的训练数据的。
-
-聊天模板因不同的大语言模型而异，这里我们可以通过下面一个简单的模板，来了解这一技术的具体做法。
-
-```bash
-# Step 1: provide the pre-query-prompt
-<|im_start|>user\n
-
-# Step 2: the language model generates the user prompt
-<|im_start|>user\n
-What is the purpose of Smol-Course?
-
-# Step 3: stop the generation
-<|im_end|>
-```
-
-具体到代码实现上，我们首先要将 `llm` 传入 [Magpie class](https://distilabel.argilla.io/dev/components-gallery/tasks/magpie/) 这个类的构造函数。
-
-```python
-from distilabel.steps.tasks import Magpie
-
-magpie = Magpie(llm=llm)
-magpie.load()
-
-next(magpie.process([{"system_prompt": "You are a helpful assistant."}]))
-# [{
-#   "role": "user",
-#   "content": "Can you provide me with a list of the top 3 universities?"
-# },
-# {
-#   "role": "assistant",
-#   "content": "The top 3 universities are: MIT, Yale, Stanford."
-# }]
-```
-
-通过上述代码，我们可以马上生成一个包含 `prompt` 和 `completion` 的数据集。但如果我们想在特定领域提升数据质量，我们还可以在 `system_prompt` 加入额外的上下文信息。这样可以使得大语言模型在生成数据时，知道用户会接下来问什么问题。具体来说，特定领域信息加在系统提示语中，多多少少可以让模型产出的数据更偏向这一领域。
-
-```
-You're an AI assistant that will help users solving math problems.
-```
-
-例如我们应该按上述方法添加系统信息的内容，而不是下面这种写法。
-
-```
-You're an AI assistant that generates math problems
-```
-
-但总的来说，在训练时，语言模型一般也较少使用包含上下文信息的 `system_prompt`，所以这个方法在自定义情形下也并不是都适用。
-
-
-### 从 Prompts 到 Pipelines
-
-上面的技术都是通过 `distilabel` 里特定的类去实现的。但为了更规范，我们还可以用  `distilabel` 里的 `Pipeline` 生成数据集。在下面的例子中，我们将使用 `Pipeline` 来为指令为题生成数据集。这个 `Pipeline` 包含 `LoadDataFromDicts` 用以载入种子数据，以及 `TextGeneration` 用以生成 `prompt` 和 `completion`。从种子数据到问题（`prompt`）再到回答（`completion`）的流程，可以用 `>>` 这个运算符串联起来。
-
-```python
-from distilabel.llms import TransformersLLM
-from distilabel.pipeline import Pipeline
-from distilabel.steps import LoadDataFromDicts
-from distilabel.steps.tasks import TextGeneration
-
-with Pipeline() as pipeline:
-    data = LoadDataFromDicts(data=[{"instruction": "Generate a short question about the Hugging Face Smol-Course."}])
-    llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
-    gen_a = TextGeneration(llm=llm, output_mappings={"generation": "instruction"})
-    gen_b = TextGeneration(llm=llm, output_mappings={"generation": "response"})
-    data >> gen_a >> gen_b
-
-if __name__ == "__main__":
-    distiset = pipeline.run(use_cache=False)
-    print(distiset["default"]["train"][0])
-# [{
-#   "instruction": "What is the purpose of Smol-Course?",
-#   "response": "The Smol-Course is a platform designed to learning computer science concepts."
-# }]
-```
-
-在 pipeline 的内部，其实也有很多好用的功能。它可以自动缓存生成结果，所以有些时候我们可以不去重新跑生成步骤。它也有容错机制，某一步生成失败了，也会继续跑。此外，pipeline 的数据生成是并行运行，所以速度也快。我们还可以用 `draw` 方法对 pipeline 进行可视化。下图就展示了数据流的走向，也展示了 `output_mappings` 是如何将当前环节的输出作为输入送给下一环节的。
-
-![Pipeline](./images/pipeline.png)
-
-## 最佳实践
-
-- 需要确保你的种子数据足够多样，来覆盖足够多的对话场景
-- 定时检查数据集，确保生成的数据足够多样、质量足够好
-- 对系统提示词进行优化，可以改善生成数据的质量
-
-## 接下来
-
-👨🏽‍💻 代码 - 通过[练习](./notebooks/instruction_sft_dataset_cn.ipynb)生成一个指令微调的数据集
-🧑‍🏫 阅读 - 学习[合成偏好数据集](./preference_datasets.md)部分
-
-## References
-
-- [Distilabel 官方文档](https://distilabel.argilla.io/latest/)
-- [Self-instruct 论文](https://arxiv.org/abs/2212.10560)
-- [Evol-Instruct 论文](https://arxiv.org/abs/2304.12244)
-- [Magpie 论文](https://arxiv.org/abs/2406.08464)
\ No newline at end of file
diff --git a/zh-cn/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb b/zh-cn/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb
deleted file mode 100644
index 7a88d00a..00000000
--- a/zh-cn/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb
+++ /dev/null
@@ -1,123 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 合成指令微调数据集\n",
-    "\n",
-    "\n",
-    "本文将介绍如何为指令微调构建人造的数据集。我们将使用 `distilabel` 这个库。\n",
-    "\n",
-    "接下来我们开始实践学习。\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>练习：为指令微调生成数据集\t</h2>\n",
-    "    <p>通过学习，你可以尝试为指令微调生成数据集</p>\n",
-    "    <p><b>难度等级</b></p>\n",
-    "    <p>🐢 为指令微调生成数据集</p>\n",
-    "    <p>🐕 借助种子数据为指令微调生成数据集</p>\n",
-    "    <p>🦁 借助种子数据和 EvolInstruct 为指令微调生成数据集</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "vscode": {
-     "languageId": "plaintext"
-    }
-   },
-   "source": [
-    "## 安装依赖\n",
-    "\n",
-    "在 `transformers` 之外，你也可以安装 `vllm` 或 `hf-inference-endpoints`。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install \"distilabel[hf-transformers,outlines,instructor]\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 开始合成数据\n",
-    "\n",
-    "前面已经学过了，我们可以用 `distilabel` 的 pipeline 去执行指令微调数据集的生成。一个最小化的 pipeline 已经提供过了。你可以扩展这个 pipeline 生成更大的数据集。你可以试验一下改变模型、修改生成参数等操作如何影响合成数据集的质量。先在小规模上试验，得出结论后再扩展到大规模生成上。\n",
-    "\n",
-    "\n",
-    "\n",
-    "你可以阅读 [distilabel](https://distilabel.argilla.io/latest/components-gallery/) 相关文档学习更多处理方法。\n",
-    "\n",
-    "你可以从 Hugging Face Hub 载入种子数据集，而不是像之前提供一个字典类数据。\n",
-    "\n",
-    "```python\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "with Pipeline(...) as pipeline:\n",
-    "    ...\n",
-    "\n",
-    "if __name__ == \"__main__:\n",
-    "    dataset = load_dataset(\"my-dataset\", split=\"train\")\n",
-    "    distiset = pipeline.run(dataset=dataset)\n",
-    "```\n",
-    "\n",
-    "跑完后，你也可以将你生成的数据上传到 Hugging Face Hub。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from distilabel.llms import TransformersLLM\n",
-    "from distilabel.pipeline import Pipeline\n",
-    "from distilabel.steps import LoadDataFromDicts\n",
-    "from distilabel.steps.tasks import TextGeneration\n",
-    "\n",
-    "with Pipeline() as pipeline:\n",
-    "    data = LoadDataFromDicts(data=[{\"instruction\": \"Generate a short question about the Hugging Face Smol-Course.\"}])\n",
-    "    llm = TransformersLLM(model=\"HuggingFaceTB/SmolLM2-1.7B-Instruct\")\n",
-    "    gen_a = TextGeneration(llm=llm, output_mappings={\"generation\": \"instruction\"})\n",
-    "    gen_b = TextGeneration(llm=llm, output_mappings={\"generation\": \"response\"})\n",
-    "    data >> gen_a >> gen_b\n",
-    "\n",
-    "if __name__ == \"__main__\":\n",
-    "    distiset = pipeline.run(use_cache=False)\n",
-    "    distiset.push_to_hub(\"huggingface-smol-course-instruction-tuning-dataset\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 🌯 完成\n",
-    "\n",
-    "现在你已经学会如何为指令微调生成数据集了，你可以用你学到的东西来：\n",
-    "\n",
-    "- 为指令微调生成训练数据集\n",
-    "- 为指令微调生成评测数据集\n",
-    "\n",
-    "接下来\n",
-    "\n",
-    "🧑‍🏫 阅读 - 学习[合成偏好数据集](./preference_datasets_cn.md)这一节\n",
-    "\n",
-    "🏋️‍♂️ 参考[第一章的内容](../../1_instruction_tuning_cn/README.md)，用合成数据集对一个模型进行指令微调\n"
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/zh-cn/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb b/zh-cn/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb
deleted file mode 100644
index 2e84619f..00000000
--- a/zh-cn/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb
+++ /dev/null
@@ -1,118 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 为偏好对齐生成数据集\n",
-    "\n",
-    "本文将介绍如何为偏好对齐构建人造的数据集。我们将使用 `distilabel` 这个库。\n",
-    "\n",
-    "接下来我们开始实践学习。\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>练习：为偏好对齐生成数据集</h2>\n",
-    "    <p>通过学习，你可以尝试为偏好对齐生成数据集</p>\n",
-    "    <p><b>难度等级</b></p>\n",
-    "    <p>🐢 为偏好对齐生成数据集</p>\n",
-    "    <p>🐕 为偏好对齐生成数据集，对回答进行迭代优化</p>\n",
-    "    <p>🦁 为偏好对齐生成数据集，对回答进行迭代优化，并使用 model pooling 技术</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "vscode": {
-     "languageId": "plaintext"
-    }
-   },
-   "source": [
-    "## 安装依赖\n",
-    "\n",
-    "在 `transformers` 之外，你也可以安装 `vllm` 或 `hf-inference-endpoints`。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install \"distilabel[hf-transformers,outlines,instructor]\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 开始合成数据\n",
-    "\n",
-    "前面已经学过了，我们可以用 `distilabel` 的 pipeline 去执行偏好数据集的生成。一个最小化的 pipeline 已经提供过了。你可以扩展这个 pipeline 生成更大的数据集。你可以试验一下改变模型、修改生成参数等操作如何影响合成数据集的质量。先在小规模上试验，得出结论后再扩展到大规模生成上。\n",
-    "\n",
-    "你可以阅读 [distilabel](https://distilabel.argilla.io/latest/components-gallery/) 相关文档学习更多处理方法。\n",
-    "\n",
-    "你可以从 Hugging Face Hub 载入种子数据集，而不是像之前提供一个字典类数据。\n",
-    "\n",
-    "```python\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "with Pipeline(...) as pipeline:\n",
-    "    ...\n",
-    "\n",
-    "if __name__ == \"__main__:\n",
-    "    dataset = load_dataset(\"my-dataset\", split=\"train\")\n",
-    "    distiset = pipeline.run(dataset=dataset)\n",
-    "```\n",
-    "\n",
-    "跑完后，你也可以将你生成的数据上传到 Hugging Face Hub。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from distilabel.llms import TransformersLLM\n",
-    "from distilabel.pipeline import Pipeline\n",
-    "from distilabel.steps import GroupColumns, LoadDataFromDicts\n",
-    "from distilabel.steps.tasks import TextGeneration\n",
-    "\n",
-    "with Pipeline() as pipeline:\n",
-    "    data = LoadDataFromDicts(data=[{\"instruction\": \"What is synthetic data?\"}])\n",
-    "    llm_a = TransformersLLM(model=\"HuggingFaceTB/SmolLM2-1.7B-Instruct\")\n",
-    "    gen_a = TextGeneration(llm=llm_a)\n",
-    "    llm_b = TransformersLLM(model=\"Qwen/Qwen2.5-1.5B-Instruct\")\n",
-    "    gen_b = TextGeneration(llm=llm_b)\n",
-    "    group = GroupColumns(columns=[\"generation\"])\n",
-    "    data >> [gen_a, gen_b] >> group\n",
-    "\n",
-    "if __name__ == \"__main__\":\n",
-    "    distiset = pipeline.run()\n",
-    "    distiset.push_to_hub(\"huggingface-smol-course-preference-tuning-dataset\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 🌯 完成\n",
-    "\n",
-    "现在你已经学会如何为指令微调生成数据集了，你可以用你学到的东西来：\n",
-    "\n",
-    "- 为偏好对齐生成训练数据集\n",
-    "- 为偏好对齐生成评测数据集\n",
-    "\n",
-    "🏋️‍♂️ 参考[第二章的内容](../../2_preference_alignment/README.md)，用合成数据集对一个模型进行偏好对齐\n"
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/zh-cn/6_synthetic_datasets/preference_datasets.md b/zh-cn/6_synthetic_datasets/preference_datasets.md
deleted file mode 100644
index a1f3a4f8..00000000
--- a/zh-cn/6_synthetic_datasets/preference_datasets.md
+++ /dev/null
@@ -1,155 +0,0 @@
-# 合成偏好数据集
-
-在[前面章节](../2_preference_alignment/README_CN.md)，我们学习了直接偏好优化（DPO）。这里我们将学习如何为 DPO 之类的偏好优化算法生成训练数据集。我们的方法将基于前面[合成指令微调数据集](./instruction_datasets_cn.md)部分。此外，我们将展示如何通过基本提示或使用 EvolQuality 来添加额外的回答，以提高回答质量。最后，我们将展示如何使用 UltraFeedback 生成评分和评论。
-
-## 生成多个回答
-
-偏好数据集需要针对一个问题（`instruction`）的多个回答（`completions`）。通过提示语让模型生成两个回答当然是可行的，但我们要确保第二个回答不要和第一个过于相似（从质量和词汇上说）。这对训练非常重要，因为模型需要通过明显的差异来区分人类偏好。这里我们还需要判断哪个回答是我们倾向的（`chosen`）、哪个是我们不倾向的（`rejected`）。在[生成分数](#生成分数)部分我们会讲解。 
-
-### Model pooling
-
-Model pooling 是一个很简单的方法：你可以使用不同的模型针对同意问题生成不同的回答。如果想进一步改进第二个回答的质量，你还可以调节不同的生成参数，如 `temperature`。通过不同的提示语模板或系统提示语，你也可以生成有多样性的回答。理论上讲，用两个不同质量的模型生成两个回答，选择质量好的那个回答就可以完成这件事情。
-
-这里我们通过 [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) 和 [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) 这两个模型进行 model pooling。针对每一个问题，我们都可以生成两个回答。同样地，我们首先用 `LoadDataFromDicts` 载入种子数据，然后用 `>>` 运算符串联 pipeline 的数据流，用 `[]` 取结果。
-
-```python
-from distilabel.llms import TransformersLLM
-from distilabel.pipeline import Pipeline
-from distilabel.steps import GroupColumns, LoadDataFromDicts
-from distilabel.steps.tasks import TextGeneration
-
-with Pipeline() as pipeline:
-    data = LoadDataFromDicts(data=[{"instruction": "What is synthetic data?"}])
-    llm_a = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
-    gen_a = TextGeneration(llm=llm_a)
-    llm_b = TransformersLLM(model="Qwen/Qwen2.5-1.5B-Instruct")
-    gen_b = TextGeneration(llm=llm_b)
-    group = GroupColumns(columns=["generation"])
-    data >> [gen_a, gen_b] >> group
-
-if __name__ == "__main__":
-    distiset = pipeline.run()
-    print(distiset["default"]["train"]["grouped_generation"][0])
-# {[
-#   'Synthetic data is artificially generated data that mimics real-world usage.',
-#   'Synthetic data refers to data that has been generated artificially.'
-# ]}
-```
-
-可以看到，我们这里获取到了两个回答。如果想提升回答的多样性，还可以在 `TextGeneration` 这一步提供 `system_prompt` 或给 `TransformersLLM` 传递生成参数。接下来我们看看怎样用 EvolQuality 进一步提升回答的质量。
-
-### EvolQuality
-
-EvolQuality 和 [EvolInstruct](./instruction_datasets_cn.md#evolinstruct) 类似，都是提示语技术。但 EvolQuality 改进的是回答的质量，而不是输入的问题。EvolQuality 会把问题和回答都作为输入，然后根据提供的标准不断修改进化回答。根据有帮助度、相关性、深度、创造性和其它细节，我们定义一个更好的回答。这个更好的回答就可以加入数据集。理论上，我们可以认为这个“更好的回答”就是我们偏好优化时倾向的回答。
-
-具体的提示语[在 distilabel 已有实现](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/evol_quality)，这里我们展示一个简化版本：
-
-```bash
-I want you act as a Response Rewriter.
-Given prompt a and a response, rewrite the response into a better version.
-Complicate the prompt based on the following criteria:
-{{ criteria }}
-
-# Prompt
-{{ input }}
-
-# Response
-{{ output }}
-
-# Improved Response
-```
-
-代码实现上，我们需要用 [EvolQuality](https://distilabel.argilla.io/dev/components-gallery/tasks/evolquality/) 这个类。这里代码只进化迭代一次。
-
-```python
-from distilabel.llms import TransformersLLM
-from distilabel.steps.tasks import EvolQuality
-
-llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
-evol_quality = EvolQuality(llm=llm, num_evolutions=1)
-evol_quality.load()
-
-instruction = "What is synthetic data?"
-completion = "Synthetic data is artificially generated data that mimics real-world usage."
-
-next(evol_quality.process([{
-    "instruction": instruction,
-    "response": completion
-}]))
-# The process of generating synthetic data through manual prompting involves creating artificial data sets that mimic real-world usage patterns.
-```
-
-可以看到，回答与问题对应，且已经更复杂了。这是个好的开端，但如果你做过 `EvolInstruct` 的实验，就会知道这种算法得到的“更好”回答有时并没有变好。所以我们还需要加入额外的测评技术，确保数据集不出现错误标注。接下来部分就会讨论这一技术。
-
-## 生成分数
-
-分数是衡量一个回答相较于另一个回答受偏好程度的指标。一般来说，这些分数可以是绝对的、主观的或相对的。在本课程中，我们将重点关注前两种，因为它们对于创建偏好数据集最为有用。这种评分方式是一种利用语言模型进行判断和评估的方法，因此与我们在[评测](../4_evaluation/README_CN.md)这一章中看到的评估技术存在一些重叠。与其他评估技术一样，分数和评估通常需要更大的模型，以便更好地契合人类偏好。
-
-### UltraFeedback
-
-UltraFeedback 可以对给出的问题和回答生成分数和评价。
-
-回答的分数一般基于一定的标准给出。有四类细化的标准：有用性、相关性、深度、创造性。借助这些标准，可以简化评分过程。得到分数后，我们就可以决定哪个回答是倾向的（`chosen`），哪个是不倾向的（`rejected`）。因为分数是绝对的，它也可以被用来过滤质量极低或区分度不高的数据。
-
-此外，评价信息可以为得分提供一些逻辑道理，可以用来理解分数造成差异的原因。
-
-UltraFeedback 对应的提示语在 [distilabel 中也有实现](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/templates/ultrafeedback)，这里提供简化版本：
-
-```bash
-Evaluate the model's outputs based on various criteria: Helpfulness, Relevance, Deepening, Creativity
-Your role is to provide a holistic assessment based on the above factors.
-Score the output from 1 to 5 on overall quality.
-
-Answer with the following format: score - rationale
-
-# Input
-{{ input }}
-
-# Response
-{{ output }}
-
-# Score - Rationale
-```
-
-在代码层面，我们使用 [UltraFeedback](https://distilabel.argilla.io/dev/components-gallery/tasks/ultrafeedback/) 类，评估取自 [Model Pooling](#model-pooling) 这一部分的合成的 `prompt` 和 `completion`。
-
-```python
-from distilabel.llms import TransformersLLM
-from distilabel.steps.tasks import UltraFeedback
-
-llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
-ultrafeedback = UltraFeedback(llm=llm)
-ultrafeedback.load()
-
-instruction = "What is synthetic data?"
-completion_a = "Synthetic data is artificially generated data that mimics real-world usage."
-completion_b = "Synthetic data refers to data that has been generated artificially."
-
-next(ultrafeedback.process([{
-    "instruction": instruction,
-    "generations": [completion_a, completion_b]
-}]))
-# [
-#     {
-#         'ratings': [4, 5],
-#         'rationales': ['could have been more specific', 'good definition'],
-#     }
-# ]
-```
-
-## 最佳实践
-
-- 相比于生成分数和评价，只生成分数一般会更简单、更少耗费资源
-- 用更大的模型去生成分数和评价
-- 建议使用一系列有多样性的模型，去生成分数和评价
-- 不断优化 `system_prompt` 和模型
-
-## 接下来
-
-👨🏽‍💻 代码 - 通过[练习](./notebooks/preference_dpo_dataset.ipynb) 去生成一个偏好对齐的数据集
-
-## 参考资料
-
-- [Distilabel 官方文档](https://distilabel.argilla.io/latest/)
-- [Deita 论文](https://arxiv.org/abs/2312.15685)
-- [UltraFeedback 论文](https://arxiv.org/abs/2310.01377)
diff --git a/zh-cn/7_inference/README.md b/zh-cn/7_inference/README.md
deleted file mode 100644
index d05ff2fc..00000000
--- a/zh-cn/7_inference/README.md
+++ /dev/null
@@ -1,36 +0,0 @@
-# 大模型推理
-
-推理是指用经过训练的语言模型来进行预测或生成回答的过程。尽管推理看似简单直接，但要大规模高效部署模型，就需要仔细考量诸如性能、成本和可靠性等多种因素。由于大语言模型（LLMs）的规模和计算需求，模型推理存在着一些独特的挑战。
-
-这里，我们借助 [`transformers`](https://huggingface.co/docs/transformers/index) 和 [`text-generation-inference`](https://github.com/huggingface/text-generation-inference) 来探索简单的推理方法以及适用于生产环境的推理方法。针对生产环境的模型部署，我们将重点关注 Text Generation Inference 这个工具包（简称 TGI），它提供了优化过的服务能力。
-
-## 章节概览
-
-大语言模型的推理可以分为两类：简单的使用 pipeline 进行推理，这适用于开发和测试阶段；优化过的服务级推理方案，者适用于生产环境的部署。我们将会讲解这两类方法，从简单的 pipeline 方法开始，再逐步深入到生产环境部署方案。
-
-## 内容目录
-
-### 1. [基本的 Pipeline 推理](./inference_pipeline_cn.md)
-
-这一节你将了解使用 Hugging Face Transformers 的 pipeline 进行基本的推理方法。我们将讲解 pipeline 的设置、生成参数的配置，以及本地部署的最佳实践。该方法适用于原型设计和小规模应用。
-
-### 2. [使用 TGI 进行生产环境部署](./text_generation_inference_cn.md)
-
-这一节你讲学习如何使用 Text Generation Inference 这个工具包进行生产环境的模型部署。我们将探索服务端模型部署的优化技术、组 batch 推理的策略，以及如何监控。TGI 提供了很多适用于生产环境的功能，如健康监测、评估指标、Docker 部署等。
-
-### 练习
-
-| 标题 | 概述 | 练习 | 链接 | Colab |
-|-------|-------------|----------|------|-------|
-| 使用 Pipeline 推理 | 使用 transformers 的 pipeline 进行推理 | 🐢 创建一个 pipeline <br> 🐕 配置生成参数 <br> 🦁 创建一个简单的网页端服务 | [Link](./notebooks/basic_pipeline_inference_cn.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/7_inference/notebooks/basic_pipeline_inference.ipynb) |
-| 使用 TGI 部署 | 在生产环境进行 TGI 部署 | 🐢 用 TGI 部署一个模型 <br> 🐕 调节参数，优化性能 <br> 🦁 监控和扩展 | [Link](./notebooks/tgi_deployment_cn.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/7_inference/notebooks/tgi_deployment.ipynb) |
-
-## 参考资料
-
-- [Hugging Face 的 Pipeline 教程](https://huggingface.co/docs/transformers/en/pipeline_tutorial)
-- [Text Generation Inference 官方文档](https://huggingface.co/docs/text-generation-inference/en/index)
-- [基于 Pipeline 创建 WebServer 指南](https://huggingface.co/docs/transformers/en/pipeline_tutorial#using-pipelines-for-a-webserver)
-- [TGI GitHub 代码仓库](https://github.com/huggingface/text-generation-inference)
-- [Hugging Face 模型部署文档](https://huggingface.co/docs/inference-endpoints/index)
-- [vLLM: High-throughput LLM Serving](https://github.com/vllm-project/vllm)
-- [优化 Transformer 模型推理](https://huggingface.co/blog/optimize-transformer-inference)
diff --git a/zh-cn/7_inference/inference_pipeline.md b/zh-cn/7_inference/inference_pipeline.md
deleted file mode 100644
index f9d72fd3..00000000
--- a/zh-cn/7_inference/inference_pipeline.md
+++ /dev/null
@@ -1,170 +0,0 @@
-# 使用 transformers 的 pipeline 进行基本的模型推理
-
-🤗 Transformers 中的 `pipeline` 抽象，为使用 Hugging Face 模型库中的任何模型进行推理提供了一种简单的方法。它处理了所有的预处理和后处理步骤，使得在无需深入了解模型架构或要求的情况下就能轻松使用这些模型。
-
-## pipeline 的工作原理
-
-Hugging Face 的 pipeline 通过将原始输入和人类可读输出之间的三个关键阶段自动化，简化了机器学习工作流程：
-
-**预处理阶段**
-pipeline 首先将你的原始输入为模型做好准备。这会因输入类型而异：
-- 文本输入会经过分词处理，将单词转换为对模型友好的 token ID
-- 图像会被调整大小并进行归一化处理，以符合模型要求
-- 音频会通过特征提取进行处理，以创建频谱图或其他表示形式
-
-**模型推理**
-在正向传播过程中，pipeline 实现了这些事情：
-- 自动进行输入的 batch 处理，以实现高效处理
-- 选择最优计算设备（CPU/GPU）进行计算
-- 如果硬件可以支持，会使用诸如半精度（FP16）推理等技术进行性能优化
-
-**后处理阶段**
-最后，pipeline 将原始的模型输出转换为有用的结果：
-- 将 token ID 解码回可读文本
-- 将 logits 值转换为概率值
-- 根据具体任务（例如分类标签、生成文本），对输出进行格式化
-
-这种抽象让你可以专注于应用程序逻辑，而管道会处理模型推理的技术复杂性。
-
-## 基本用法
-
-下面示例展示如何使用 pipeline 进行文本生成：
-
-```python
-from transformers import pipeline
-
-# Create a pipeline with a specific model
-generator = pipeline(
-    "text-generation",
-    model="HuggingFaceTB/SmolLM2-1.7B-Instruct",
-    torch_dtype="auto",
-    device_map="auto"
-)
-
-# Generate text
-response = generator(
-    "Write a short poem about coding:",
-    max_new_tokens=100,
-    do_sample=True,
-    temperature=0.7
-)
-print(response[0]['generated_text'])
-```
-
-## 关键配置
-
-### 载入模型
-```python
-# CPU inference
-generator = pipeline("text-generation", model="HuggingFaceTB/SmolLM2-1.7B-Instruct", device="cpu")
-
-# GPU inference (device 0)
-generator = pipeline("text-generation", model="HuggingFaceTB/SmolLM2-1.7B-Instruct", device=0)
-
-# Automatic device placement
-generator = pipeline(
-    "text-generation",
-    model="HuggingFaceTB/SmolLM2-1.7B-Instruct",
-    device_map="auto",
-    torch_dtype="auto"
-)
-```
-
-### 生成相关的参数
-
-```python
-response = generator(
-    "Translate this to French:",
-    max_new_tokens=100,     # 生成文本的最大长度
-    do_sample=True,         # 解码时用采样的策略，而不是贪心策略
-    temperature=0.7,        # 这个参数可以控制随机性，值越大越随机
-    top_k=50,               # 采样时，只考虑最靠前的前 k 个 token
-    top_p=0.95,             # 采样时，概率值的阈值
-    num_return_sequences=1  # 针对一个输入输出几个输出
-)
-```
-
-## 同时处理多个输入
-
-Pipeline 可以借助 batch 的技术，高效地同时处理多个输入：
-
-```python
-# Prepare multiple prompts
-prompts = [
-    "Write a haiku about programming:",
-    "Explain what an API is:",
-    "Write a short story about a robot:"
-]
-
-# Process all prompts efficiently
-responses = generator(
-    prompts,
-    batch_size=4,              # Number of prompts to process together
-    max_new_tokens=100,
-    do_sample=True,
-    temperature=0.7
-)
-
-# Print results
-for prompt, response in zip(prompts, responses):
-    print(f"Prompt: {prompt}")
-    print(f"Response: {response[0]['generated_text']}\n")
-```
-
-## 集成入网页端服务
-
-下面示例展示了如何将 pipeline 集成入 FastAPI 应用：
-
-```python
-from fastapi import FastAPI, HTTPException
-from transformers import pipeline
-import uvicorn
-
-app = FastAPI()
-
-# Initialize pipeline globally
-generator = pipeline(
-    "text-generation",
-    model="HuggingFaceTB/SmolLM2-1.7B-Instruct",
-    device_map="auto"
-)
-
-@app.post("/generate")
-async def generate_text(prompt: str):
-    try:
-        if not prompt:
-            raise HTTPException(status_code=400, detail="No prompt provided")
-            
-        response = generator(
-            prompt,
-            max_new_tokens=100,
-            do_sample=True,
-            temperature=0.7
-        )
-        
-        return {"generated_text": response[0]['generated_text']}
-        
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=5000)
-```
-
-## 局限性
-
-虽然 Pipeline 对于原型设计和小规模部署很有用，但它们存在一些局限性：
-
-- 与专用服务解决方案相比，优化选项有限。
-- 不支持动态 batch 处理等高级特性。
-- 可能不适合高吞吐量的生产工作负载。
-
-对于有高吞吐量要求的生产部署，可考虑使用 TGI 或其他专门的服务解决方案。
-
-
-## 参考资料
-
-- [Hugging Face 的 Pipeline 教程](https://huggingface.co/docs/transformers/en/pipeline_tutorial)
-- [Pipeline API 参考资料](https://huggingface.co/docs/transformers/en/main_classes/pipelines)
-- [Text Generation 参数文档](https://huggingface.co/docs/transformers/en/main_classes/text_generation)
-- [模型量化指南](https://huggingface.co/docs/transformers/en/perf_infer_gpu_one)
\ No newline at end of file
diff --git a/zh-cn/7_inference/text_generation_inference.md b/zh-cn/7_inference/text_generation_inference.md
deleted file mode 100644
index 01f6cb41..00000000
--- a/zh-cn/7_inference/text_generation_inference.md
+++ /dev/null
@@ -1,138 +0,0 @@
-# Text Generation Inference (TGI)
-
-Text Generation Inference（简称 TGI）是一个由 Hugging Face 开发的工具包，主要用于对大语言模型进行部署和服务搭建。它旨在为常用的开源大语言模型（LLMs）实现高性能文本生成。TGI 被 Hugging Chat 用于实际生产，Hugging Chat 是一个面向开源模型的开源交互界面。
-
-
-## 为什么使用 TGI？
-
-TGI 解决了在生产环境中部署大语言模型的关键难题。虽然许多框架在模型开发方面表现出色，但 TGI 专门针对生产部署和扩展进行了优化。它的一些关键特性包括：
-
-- **张量并行**：TGI 能够通过张量并行技术将模型分割到多个 GPU 上，这对于高效部署更大的模型至关重要。
-- **连续 batch 处理**：连续 batch 处理系统通过动态处理请求，最大限度地提高 GPU 利用率，同时诸如 Flash Attention 和 Paged Attention 等优化方法显著降低了内存使用并提高了速度。
-- **token streaming**：实时应用程序受益于通过服务器发送事件（Server-Sent Events）实现的 token streaming 技术，以最低延迟提供响应。
-
-## 怎样使用 TGI
-
-### 基本 Python 用法
-
-TGI 采用了一种简洁而强大的 REST API 集成方式，这使得它能轻松与你的应用程序整合。
-
-### 使用 REST API
-
-TGI 提供了一个接受 JSON 数据的 RESTful API 。这使得任何能够发出 HTTP 请求的编程语言或工具都可以访问它。以下是一个使用 curl 的基本示例：
-
-```bash
-# Basic generation request
-curl localhost:8080/v1/chat/completions \
-    -X POST \
-    -d '{
-  "model": "tgi",
-  "messages": [
-    {
-      "role": "system",
-      "content": "You are a helpful assistant."
-    },
-    {
-      "role": "user",
-      "content": "What is deep learning?"
-    }
-  ],
-  "stream": true,
-  "max_tokens": 20
-}' \
-    -H 'Content-Type: application/json'
-```
-
-### 使用 `huggingface_hub` 的 Python 客户端
-
-`huggingface_hub` 的 Python 客户端可以处理连接管理、请求格式化以及响应解析。示例如下：
-
-
-```python
-from huggingface_hub import InferenceClient
-
-client = InferenceClient(
-    base_url="http://localhost:8080/v1/",
-)
-
-output = client.chat.completions.create(
-    model="tgi",
-    messages=[
-        {"role": "system", "content": "You are a helpful assistant."},
-        {"role": "user", "content": "Count to 10"},
-    ],
-    stream=True,
-    max_tokens=1024,
-)
-
-for chunk in output:
-    print(chunk.choices[0].delta.content)
-```
-
-
-### 使用 OpenAI 格式的 API
-
-很多代码库支持 OpenAI API，你也可以用对应的客户端请求去和 TGI 进行互动。
-
-```python
-from openai import OpenAI
-
-# init the client but point it to TGI
-client = OpenAI(
-    base_url="http://localhost:8080/v1/",
-    api_key="-"
-)
-
-chat_completion = client.chat.completions.create(
-    model="tgi",
-    messages=[
-        {"role": "system", "content": "You are a helpful assistant." },
-        {"role": "user", "content": "What is deep learning?"}
-    ],
-    stream=True
-)
-
-# iterate and print stream
-for message in chat_completion:
-    print(message)
-```
-
-## 为 TGI 部署准备模型
-
-如果想借助 TGI 给模型构建服务端，你还需要执行以下步骤：
-
-1. **支持的模型架构**：检查你的模型结构，看看是否受 TGI 支持（目前支持 Llama、BLOOM、T5 等)
-
-2. **模型格式**：将模型权重转换为 safetensors 格式，加速模型参数加载
-
-```python
-from safetensors.torch import save_file
-from transformers import AutoModelForCausalLM
-
-model = AutoModelForCausalLM.from_pretrained("your-model")
-state_dict = model.state_dict()
-save_file(state_dict, "model.safetensors")
-```
-
-1. **量化**（可选步骤）：对模型进行量化，以减少内存使用：
-
-```python
-from transformers import BitsAndBytesConfig
-
-quantization_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_compute_dtype="float16"
-)
-
-model = AutoModelForCausalLM.from_pretrained(
-    "your-model",
-    quantization_config=quantization_config
-)
-```
-
-## 参考资料
-
-- [Text Generation Inference 官方文档](https://huggingface.co/docs/text-generation-inference)
-- [TGI 在 GitHub 上的代码仓库](https://github.com/huggingface/text-generation-inference)
-- [Hugging Face Model Hub](https://huggingface.co/models)
-- [TGI 的 API 参考文档](https://huggingface.co/docs/text-generation-inference/api_reference)
\ No newline at end of file
diff --git a/zh-cn/8_agents/README.md b/zh-cn/8_agents/README.md
deleted file mode 100644
index 3c5b3ed8..00000000
--- a/zh-cn/8_agents/README.md
+++ /dev/null
@@ -1,36 +0,0 @@
-# 智能体
-
-AI 智能体（AI Agents）是一种自主系统，能够理解用户请求、将请求拆解为多个步骤，然后执行相应行动以完成任务。它们将语言模型与工具及外部功能相结合，从而与所处环境进行交互。本模章将讲解如何使用 [`smolagents`](https://github.com/huggingface/smolagents) 库构建高效的智能体，该库为创建强大的 AI 智能体提供了一个轻量级框架。
-
-## 章节概览
-
-高效的智能体通常具备三种关键能力。一是检索能力，智能体需要能够从各种信息来源获取和使用相关信息。二是函数调用，这使得智能体能够在所处环境中采取具体行动。最后是特定领域的知识和工具，这能让智能体可以执行诸如代码操作这类专业任务。
-
-## 内容目录
-
-### 1️⃣ [检索智能体](./retrieval_agents_cn.md)
-
-检索型智能体将模型与知识库相结合。这些智能体能够从多个来源搜索并整合信息，借助向量数据库实现高效检索，并采用检索增强生成（Retrieval Augmented Generation 或 RAG）模式。它们擅长将网页搜索与自定义知识库相融合，同时通过记忆系统维持对话上下文。这部分教程主要讲解其实现策略，包括用于稳健信息检索的回退机制。
-
-### 2️⃣ [代码智能体](./code_agents_cn.md)
-
-代码智能体是为软件开发任务而设计的专门化自主系统。这些智能体擅长分析和生成代码、执行自动重构，并与开发工具相集成。这部分教程主要讲解如何构建以代码为核心的智能体，以及最佳实践。这些智能体能够理解编程语言、与构建系统（build systems）协作，并与版本控制系统交互，同时保持较高的代码质量标准。
-
-### 3️⃣ [自定义函数](./custom_functions_cn.md)
-
-自定义函数智能体通过专门的函数调用扩展了基本的人工智能能力。这部分教程将探讨如何设计模块化和可扩展的函数接口，使其直接与应用程序的逻辑相集成。你将学习在创建可靠的函数驱动的工作流时，如何实施适当的验证和错误处理。重点在于构建简单的系统，在该系统中智能体可以以可预测的方式与外部工具和服务进行交互。
-
-### 练习
-
-| 标题 | 概述 | 练习 | 链接 | Colab |
-|-------|-------------|----------|------|-------|
-| 构建科研智能体 | 构建一个借助检索和自定义函数可以执行科研任务的智能体 | 🐢 构建简单的 RAG 智能体 <br> 🐕 加入自定义的搜索函数 <br> 🦁 创建完整的科研助手 | [Notebook](./notebooks/agents_cn.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/8_agents/notebooks/agents.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-
-## Resources
-
-- [smolagents Documentation](https://huggingface.co/docs/smolagents) - smolagents 代码库的官方文档
-- [Building Effective Agents](https://www.anthropic.com/research/building-effective-agents) - 智能体架构方面的研究性论文
-- [Agent Guidelines](https://huggingface.co/docs/smolagents/tutorials/building_good_agents) - 构建可靠智能体的最佳实践
-- [LangChain Agents](https://python.langchain.com/docs/how_to/#agents) - 智能体实现的一些示例
-- [Function Calling Guide](https://platform.openai.com/docs/guides/function-calling) - 理解 LLM 中的函数调用
-- [RAG Best Practices](https://www.pinecone.io/learn/retrieval-augmented-generation/) - 高效 RAG 实现指南
diff --git a/zh-cn/8_agents/code_agents.md b/zh-cn/8_agents/code_agents.md
deleted file mode 100644
index ed79de98..00000000
--- a/zh-cn/8_agents/code_agents.md
+++ /dev/null
@@ -1,68 +0,0 @@
-# 代码智能体
-
-代码智能体是专门的自主系统，可处理诸如分析、生成、重构和测试之类的代码任务。这些智能体利用关于编程语言、构建系统（build systems）和版本控制的领域知识来增强软件开发工作流程。
-
-## 为什么我们需要代码智能体？
-
-代码智能体通过将重复性任务自动化，同时保持代码质量，从而加速开发过程。它们擅长生成样板代码、进行系统性重构，并通过静态分析识别潜在问题。这些智能体将检索能力（访问外部文档和代码库）与函数调用相结合，以执行创建文件或运行测试等具体操作。
-
-## 代码智能体的组成
-
-代码智能体建立在为代码理解而微调过的专门语言模型之上。这些模型辅以诸如代码检查工具、格式化工具和编译器等开发工具，以便与现实世界环境进行交互。通过检索技术，智能体通过访问文档和代码历史记录来保持上下文感知，以符合组织的模式和标准。以行动为导向的函数使智能体能够执行具体任务，例如提交变更或发起合并请求。
-
-在以下示例中，我们创建一个代码智能体，它可以像我们之前构建的检索智能体那样使用 DuckDuckGo 进行网络搜索。
-
-```python
-from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
-
-agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=HfApiModel())
-
-agent.run("How many seconds would it take for a leopard at full speed to run through Pont des Arts?")
-```
-
-在以下示例中，我们创建了一个代码智能体，它能够获取两个位置之间的运动时间。这里，我们使用 `@tool` 装饰器来定义一个可用作工具的自定义函数。
-
-```python
-from smolagents import CodeAgent, HfApiModel, tool
-
-@tool
-def get_travel_duration(start_location: str, destination_location: str, departure_time: Optional[int] = None) -> str:
-    """Gets the travel time in car between two places.
-    
-    Args:
-        start_location: the place from which you start your ride
-        destination_location: the place of arrival
-        departure_time: the departure time, provide only a `datetime.datetime` if you want to specify this
-    """
-    import googlemaps # All imports are placed within the function, to allow for sharing to Hub.
-    import os
-
-    gmaps = googlemaps.Client(os.getenv("GMAPS_API_KEY"))
-
-    if departure_time is None:
-        from datetime import datetime
-        departure_time = datetime(2025, 1, 6, 11, 0)
-
-    directions_result = gmaps.directions(
-        start_location,
-        destination_location,
-        mode="transit",
-        departure_time=departure_time
-    )
-    return directions_result[0]["legs"][0]["duration"]["text"]
-
-agent = CodeAgent(tools=[get_travel_duration], model=HfApiModel(), additional_authorized_imports=["datetime"])
-
-agent.run("Can you give me a nice one-day trip around Paris with a few locations and the times? Could be in the city or outside, but should fit in one day. I'm travelling only via public transportation.")
-
-```
-
-当然，这两个例子仅仅是你开发代码智能体的开始。你需要阅读 [smolagents 文档](https://huggingface.co/docs/smolagents)来了解更多构建代码智能体的方法。
-
-`smolagents` 为构建代码智能体提供了一个轻量级的框架，其核心代码实现仅有 1000 行左右。该框架专注于构建能够编写和执行 Python 代码片段的智能体，并提供沙盒式执行环境以确保安全性。它同时支持开源和专有语言模型，使其能够适配各种开发环境。
-
-## 延伸阅读
-
-- [smolagents Blog](https://huggingface.co/blog/smolagents) - 介绍 smolagents 的博客
-- [smolagents: Building Good Agents](https://huggingface.co/docs/smolagents/tutorials/building_good_agents) - 构建可靠智能体的最佳实践
-- [Building Effective Agents - Anthropic](https://www.anthropic.com/research/building-effective-agents) - 智能体设计原则
diff --git a/zh-cn/8_agents/custom_functions.md b/zh-cn/8_agents/custom_functions.md
deleted file mode 100644
index 57b80298..00000000
--- a/zh-cn/8_agents/custom_functions.md
+++ /dev/null
@@ -1,71 +0,0 @@
-# 自定义函数智能体
-
-自定义函数智能体是一类借助专门的函数调用（或称为 tools）来执行任务的人工智能智能体。与通用智能体不同，自定义函数智能体专注于通过直接与应用程序的逻辑相集成，为高级工作流程提供支持。例如，你可以将数据库查询、系统命令或任何自定义实用程序作为独立函数开放，以供智能体调用。
-
-## 为什么需要自定义函数智能体？
-
-- **模块化与可扩展性**：无需构建一个庞大的单体智能体，你可以设计代表不同独立功能的单个函数，使你的架构更具可扩展性。
-
-- **细粒度控制**：开发人员可以通过明确指定哪些函数可用以及它们接受哪些参数，来精细地控制智能体的行为。
-
-- **增强可靠性**：通过为每个函数构建清晰的模式和验证机制，可减少错误和意外行为。
-
-## 基本工作流程
-
-1. **确定函数**
-   确定哪些任务可以转化为自定义函数（例如，文件输入输出、数据库查询、流数据处理）。
-
-2. **定义接口**
-   使用函数签名或模式精确概述每个函数的输入、输出以及预期行为。这在智能体与其运行环境之间建立了严格的契约。
-
-3. **向智能体注册**
-   智能体需要 “了解” 哪些函数可用。通常，你将描述每个函数接口的元数据传递给语言模型或智能体框架。
-
-4. **调用与验证**
-   一旦智能体选择要调用的函数，使用提供的参数运行该函数并验证结果。如果结果有效，将结果反馈给智能体作为上下文，以推动后续决策。
-
-## 示例
-
-以下是一个简化示例，以伪代码展示自定义函数调用可能的形式。目标是执行用户定义的搜索并检索相关内容：
-
-```python
-# Define a custom function with clear input/output types
-def search_database(query: str) -> list:
-    """
-    Search the database for articles matching the query.
-    
-    Args:
-        query (str): The search query string
-        
-    Returns:
-        list: List of matching article results
-    """
-    try:
-        results = database.search(query)
-        return results
-    except DatabaseError as e:
-        logging.error(f"Database search failed: {e}")
-        return []
-
-# Register the function with the agent
-agent.register_function(
-    name="search_database",
-    function=search_database,
-    description="Searches database for articles matching a query"
-)
-
-# Example usage
-def process_search():
-    query = "Find recent articles on AI"
-    results = agent.invoke("search_database", query)
-    
-    if results:
-        agent.process_results(results)
-    else:
-        logging.info("No results found for query")
-```
-
-## 延伸阅读
-
-- [smolagents Blog](https://huggingface.co/blog/smolagents) - 介绍 smolagents 的博客
-- [Building Good Agents](https://huggingface.co/docs/smolagents/tutorials/building_good_agents) - 构建可靠智能体的最佳实践
\ No newline at end of file
diff --git a/zh-cn/8_agents/notebooks/agents.ipynb b/zh-cn/8_agents/notebooks/agents.ipynb
deleted file mode 100644
index 54de1d6e..00000000
--- a/zh-cn/8_agents/notebooks/agents.ipynb
+++ /dev/null
@@ -1,197 +0,0 @@
-{
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# 构建 AI 智能体\n",
-                "\n",
-                "\n",
-                "本文包含一些练习，旨在帮助你学习如何使用 `smolagents` 库构建不同类型的智能体。我们将从基础实现逐步过渡到更复杂的实现。\n",
-                "\n",
-                "## 准备\n",
-                "\n",
-                "首先我们要安装需要用到的包："
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "!pip install smolagents\n",
-                "\n",
-                "# Install the requirements in Google Colab\n",
-                "# !pip install transformers datasets trl huggingface_hub\n",
-                "\n",
-                "# Authenticate to Hugging Face\n",
-                "from huggingface_hub import login\n",
-                "\n",
-                "login()"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## 🐢 练习 1：基本的代码智能体\n",
-                "\n",
-                "我们从创建一个简单的代码智能体开始，它可以通过网页搜索回答一些编程相关的问题。"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel\n",
-                "\n",
-                "# Initialize the agent\n",
-                "agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=HfApiModel())\n",
-                "\n",
-                "# Test the agent\n",
-                "response = agent.run(\"What's the difference between a list and a tuple in Python?\")\n",
-                "print(response)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### 🤔 练习 1 挑战项\n",
-                "试着让智能体解释不同的编程概念，并评估它的回复。它在以下方面的表现如何：\n",
-                "1. 基本的语法问题\n",
-                "2. 语言特定的特性\n",
-                "3. 代码示例"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## 🐕 练习 2：带有自定义函数的智能体\n",
-                "\n",
-                "现在我们创建一个可以通过自定义的函数去执行特定任务的智能体。这里我们创建一个简单的计算器，让它作为自定义的工具。"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "from smolagents import CodeAgent, tool\n",
-                "from typing import Union\n",
-                "\n",
-                "\n",
-                "@tool\n",
-                "def calculate(operation: str, numbers: object) -> float:\n",
-                "    \"\"\"Performs basic mathematical operations on a list of numbers.\n",
-                "\n",
-                "    Args:\n",
-                "        operation: One of 'sum', 'average', 'multiply', 'min', 'max'\n",
-                "        numbers: List of numbers to operate on\n",
-                "\n",
-                "    Returns:\n",
-                "        float: Result of the operation\n",
-                "    \"\"\"\n",
-                "    if operation == \"sum\":\n",
-                "        return sum(numbers)\n",
-                "    elif operation == \"average\":\n",
-                "        return sum(numbers) / len(numbers)\n",
-                "    elif operation == \"multiply\":\n",
-                "        result = 1\n",
-                "        for n in numbers:\n",
-                "            result *= n\n",
-                "        return result\n",
-                "    elif operation == \"min\":\n",
-                "        return min(numbers)\n",
-                "    elif operation == \"max\":\n",
-                "        return max(numbers)\n",
-                "    else:\n",
-                "        raise ValueError(f\"Unknown operation: {operation}\")\n",
-                "\n",
-                "\n",
-                "# Create agent with custom tool\n",
-                "math_agent = CodeAgent(tools=[calculate], model=HfApiModel())\n",
-                "\n",
-                "# Test the agent\n",
-                "response = math_agent.run(\"What is the average of 10, 15, 20, 25, and 30?\")\n",
-                "print(response)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### 🤔 练习 2 挑战项\n",
-                "1. 往计算器这个工具多加入点数学运算功能\n",
-                "2. 创建一个新的自定义工具（例如，字符串处理或日期计算）\n",
-                "3. 把多个工具结合入一个智能体中"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## 🦁 练习 3：改进检索智能体\n",
-                "\n",
-                "最后，让我们构建一个更复杂的智能体，它将网络搜索和记忆相结合，以便在对话中保持上下文连贯。"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "from smolagents import CodeAgent, DuckDuckGoSearchTool\n",
-                "\n",
-                "# Initialize the agent with memory\n",
-                "research_agent = CodeAgent(...)  # TODO: Define the agent\n",
-                "\n",
-                "# Test with a multi-turn conversation\n",
-                "questions = [\n",
-                "    \"What are the main types of machine learning?\",\n",
-                "    \"Can you explain supervised learning in more detail?\",\n",
-                "    \"What are some popular algorithms for this type?\",\n",
-                "]\n",
-                "\n",
-                "# TODO: Test the agent"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### 🤔 Exercise 3 Challenge\n",
-                "1. 测试智能体在不同主题下保持上下文的能力如何。\n",
-                "2. 实现一个自定义知识库工具（如 [这里](retrieval_agents_cn.md) 所示）。\n",
-                "3. 创建一个混合智能体，将代码理解能力与研究能力相结合。"
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "Python 3",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.12.8"
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 2
-}
diff --git a/zh-cn/8_agents/retrieval_agents.md b/zh-cn/8_agents/retrieval_agents.md
deleted file mode 100644
index 523eb30d..00000000
--- a/zh-cn/8_agents/retrieval_agents.md
+++ /dev/null
@@ -1,122 +0,0 @@
-# 构建自主性检索增强生成（RAG）系统
-
-自主性检索增强生成（RAG 或 Retrieval Augmented Generation）将有自主性的智能体的能力与知识检索能力相结合。传统的 RAG 系统只是利用大语言模型（LLM），基于检索到的信息来回答询问。而自主性 RAG 更进一步，允许系统智能地控制自身的检索和回答过程。
-
-传统 RAG 存在关键局限 —— 它仅执行单一的检索步骤，且依赖与用户询问的直接语义相似性，这可能会遗漏相关信息。自主性 RAG 通过让智能体能够自行制定搜索查询、评估结果，并根据需要执行多个检索步骤，来应对这些挑战。
-
-## 使用 DuckDuckGo 实现基本检索
-
-我们首先构建一个简单的智能体，它可以用 DuckDuckGo 去搜索网页。这个智能体可以通过检索相关信息并整合，来回答问题。
-
-```python
-from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
-
-# Initialize the search tool
-search_tool = DuckDuckGoSearchTool()
-
-# Initialize the model
-model = HfApiModel()
-
-agent = CodeAgent(
-    model = model,
-    tools=[search_tool]
-)
-
-# Example usage
-response = agent.run(
-    "What are the latest developments in fusion energy?"
-)
-print(response)
-```
-
-这个智能体背后所做的事情是：
-1. 分析这个问题，确定需要哪些参考信息
-2. 使用 DuckDuckGo 这个搜索引擎寻找相关内容
-3. 整合检索到的信息，形成条理清晰的回答
-4. 在内存中保存本次交互信息，以供将来参考
-
-## 自定义知识库
-
-针对特定领域的应用，我们通常希望在网络搜索之外再加入特有的知识库作为参考。接下来我们就创建一个自定义的工具，用以从一个技术文档的向量数据库中进行查询。
-
-```python
-from smolagents import Tool
-
-class RetrieverTool(Tool):
-    name = "retriever"
-    description = "Uses semantic search to retrieve the parts of transformers documentation that could be most relevant to answer your query."
-    inputs = {
-        "query": {
-            "type": "string",
-            "description": "The query to perform. This should be semantically close to your target documents. Use the affirmative form rather than a question.",
-        }
-    }
-    output_type = "string"
-
-    def __init__(self, docs, **kwargs):
-        super().__init__(**kwargs)
-        self.retriever = BM25Retriever.from_documents(
-            docs, k=10
-        )
-
-    def forward(self, query: str) -> str:
-        assert isinstance(query, str), "Your search query must be a string"
-
-        docs = self.retriever.invoke(
-            query,
-        )
-        return "\nRetrieved documents:\n" + "".join(
-            [
-                f"\n\n===== Document {str(i)} =====\n" + doc.page_content
-                for i, doc in enumerate(docs)
-            ]
-        )
-
-retriever_tool = RetrieverTool(docs_processed)
-```
-
-这个增强版的智能体背后所做的事情是：
-1. 首先检查文档，看有没有相关信息First check the documentation for relevant information
-2. 如有需要，也进行网页搜索
-3. 集成两个信息源的信息
-4. 在内存中保存对话的上下文
-
-## 增强检索能力
-
-当构建自主性检索增强生成（RAG）系统时，智能体还可以借鉴以下复杂策略：
-
-1. 查询重构：智能体并非直接使用用户的原始查询，而是精心设计优化后的搜索词，使其与目标文档更匹配。
-2. 多步检索：智能体可以执行多次搜索，依据初始检索结果来调整后续的查询。
-3. 来源整合：整合来自多个渠道的信息，如网页搜索和本地文档。
-4. 结果验证：在将检索到的内容纳入回复之前，会对其相关性和准确性进行分析。
-
-要构建有效的自主性检索增强生成（RAG）系统，需要仔细考量几个关键方面。智能体应依据查询类型和上下文，在可用工具中做出选择。记忆系统有助于保存对话历史，避免重复检索。制定回退策略可确保即便首选的检索方法失效，系统仍能发挥作用。此外，实施验证步骤有助于保证检索信息的准确性和相关性。
-
-
-```python
-import datasets
-from langchain.docstore.document import Document
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.retrievers import BM25Retriever
-
-knowledge_base = datasets.load_dataset("m-ric/huggingface_doc", split="train")
-knowledge_base = knowledge_base.filter(lambda row: row["source"].startswith("huggingface/transformers"))
-
-source_docs = [
-    Document(page_content=doc["text"], metadata={"source": doc["source"].split("/")[1]})
-    for doc in knowledge_base
-]
-
-text_splitter = RecursiveCharacterTextSplitter(
-    chunk_size=500,
-    chunk_overlap=50,
-    add_start_index=True,
-    strip_whitespace=True,
-    separators=["\n\n", "\n", ".", " ", ""],
-)
-docs_processed = text_splitter.split_documents(source_docs)
-```
-
-## 接下来
-
-⏩ 学习[代码智能体](./code_agents_cn.md)部分，了解如何构建一个可以处理代码任务的智能体。

From 1687850070076b0a2b06a720c7394a3a51a51b52 Mon Sep 17 00:00:00 2001
From: HoiM <hoiming0730@gmail.com>
Date: Fri, 28 Mar 2025 15:51:54 +0800
Subject: [PATCH 3/3] remove other lang

---
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diff --git a/vi/1_instruction_tuning/README.md b/vi/1_instruction_tuning/README.md
deleted file mode 100644
index 4b642e18..00000000
--- a/vi/1_instruction_tuning/README.md
+++ /dev/null
@@ -1,30 +0,0 @@
-# Tinh chỉnh theo chỉ thị (Instruction Tuning)
-
-Trong chương này chúng ta sẽ học về quá trình tinh chỉnh mô hình ngôn ngữ theo chỉ thị. Tinh chỉnh theo chỉ thị là quá trình điều chỉnh *pre-trained models* cho các tác vụ cụ thể bằng cách tiếp tục huấn luyện chúng trên các tập dữ liệu đặc thù cho tác vụ. Quá trình này giúp các mô hình cải thiện hiệu suất trên những tác vụ đó.
-
-Chúng ta sẽ cùng khám phá hai chủ đề chính: 1) Định dạng Chat (Chat Templates) và 2) Tinh chỉnh có giám sát (Supervised Fine-Tuning).
-
-## 1️⃣ Định dạng Chat (Chat Templates)
-
-Định dạng Chat là cấu trúc giữa các tương tác giữa người dùng và mô hình ngôn ngữ, đảm bảo các phản hồi nhất quán và phù hợp với từng ngữ cảnh. Chúng bao gồm các thành phần như `system prompts` và các `message` theo vai trò (người dùng - `user` hoặc trợ lý - `assistant`). Để biết thêm thông tin chi tiết, hãy tham khảo phần [Chat Templates](./chat_templates.md).
-
-## 2️⃣ Huấn luyện có giám sát (Supervised Fine-Tuning)
-
-Huấn luyện có giám sát (SFT) là một quá trình cốt lõi để điều chỉnh các mô hình ngôn ngữ đã *pre-trained* cho các tác vụ cụ thể. Quá trình này bao gồm việc huấn luyện mô hình trên tập dữ liệu có gán nhãn theo tác vụ cụ thể. Để đọc hướng dẫn chi tiết về SFT, bao gồm các bước quan trọng và các phương pháp thực hành tốt nhất, hãy xem tại trang [Supervised Fine-Tuning](./supervised_fine_tuning.md).
-
-## Bài tập
-
-| Tiêu đề | Mô tả | Bài tập | Đường dẫn | Google Colab |
-|-------|-------------|----------|------|-------|
-| Định dạng Chat | Học cách sử dụng *định dạng chat* với SmolLM2 và xử lý dữ liệu thành định dạng *chatml* | 🐢 Chuyển đổi tập dữ liệu `HuggingFaceTB/smoltalk` sang định dạng *chatml* <br> 🐕 Chuyển đổi tập dữ liệu `openai/gsm8k` sang định dạng *chatml* | [Notebook](./notebooks/chat_templates_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/1_instruction_tuning/notebooks/chat_templates_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Sử dụng Colab"/></a> |
-| Tinh chỉnh có giám sát | Học cách tinh chỉnh SmolLM2 sử dụng SFTTrainer | 🐢 Sử dụng tập dữ liệu `HuggingFaceTB/smoltalk` <br>🐕 Thử nghiệm với tập dữ liệu `bigcode/the-stack-smol` <br>🦁 Chọn một tập dữ liệu cho trường hợp sử dụng thực tế | [Notebook](./notebooks/sft_finetuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Sử dụng Colab"/></a> |
-
-## Tài liệu tham khảo
-
-- [Tài liệu Transformers về định dạng chat](https://huggingface.co/docs/transformers/main/en/chat_templating)
-- [Script cho huấn luyện có giám sát bằng thư viện TRL](https://github.com/huggingface/trl/blob/main/examples/scripts/sft.py)
-- [`SFTTrainer` trong thư viện TRL](https://huggingface.co/docs/trl/main/en/sft_trainer)
-- [Bài báo Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290)
-- [Huấn luyện có giám sát bằng thư viện TRL](https://huggingface.co/docs/trl/main/en/tutorials/supervised_finetuning)
-- [Cách fine-tune Google Gemma với ChatML và Hugging Face TRL](https://www.philschmid.de/fine-tune-google-gemma)
-- [Huấn luyện LLM để tạo danh mục sản phẩm tiếng Ba Tư ở định dạng JSON](https://huggingface.co/learn/cookbook/en/fine_tuning_llm_to_generate_persian_product_catalogs_in_json_format)
diff --git a/vi/1_instruction_tuning/chat_templates.md b/vi/1_instruction_tuning/chat_templates.md
deleted file mode 100644
index 62733731..00000000
--- a/vi/1_instruction_tuning/chat_templates.md
+++ /dev/null
@@ -1,114 +0,0 @@
-# Định dạng Chat
-
-Định dạng Chat (Chat templates) là yếu tố thiết yếu trong cấu trúc các tương tác giữa mô hình ngôn ngữ và người dùng. Chúng cung cấp một định dạng nhất quán cho các cuộc hội thoại, đảm bảo rằng các mô hình hiểu được ngữ cảnh và vai trò của mỗi tin nhắn trong khi duy trì các mẫu phản hồi phù hợp.
-
-## Mô hình gốc (Base Models) và Mô hình chỉ thị (Instruct Models)
-
-Mô hình gốc được huấn luyện trên dữ liệu văn bản thô để dự đoán *token* tiếp theo, trong khi Mô hình chỉ thị được tiếp tục tinh chỉnh đặc biệt để tuân theo chỉ thị và tham gia vào hội thoại. Ví dụ, `SmolLM2-135M` là một mô hình gốc, trong khi `SmolLM2-135M-Instruct` là phiên bản đã được điều chỉnh.
-
-Để làm cho Mô hình gốc hoạt động như một Mô hình chỉ thị, chúng ta cần *định dạng prompt* của mình theo cách nhất quán mà mô hình có thể hiểu được. Đây là lúc *định dạng chat* phát huy tác dụng. **ChatML** là một định dạng template như vậy, với cấu trúc các cuộc hội thoại có chỉ định vai trò rõ ràng (system, user, assistant).
-
-Điều quan trọng cần lưu ý là một Mô hình gốc có thể được huấn luyện với các *định dạng chat* khác nhau, vì vậy khi chúng ta sử dụng một Mô hình chỉ thị, chúng ta cần đảm bảo đang sử dụng đúng *định dạng chat*.
-
-## Tìm hiểu về Định dạng Chat
-
-Về cốt lõi, *định dạng chat* định nghĩa cách các cuộc hội thoại nên được định dạng khi giao tiếp với một mô hình ngôn ngữ. Chúng bao gồm các hướng dẫn hệ thống (system), tin nhắn người dùng (user) và phản hồi của trợ lý (assistant) trong một định dạng có cấu trúc mà mô hình có thể hiểu được. Cấu trúc này giúp duy trì tính nhất quán trong các tương tác và đảm bảo mô hình phản hồi phù hợp với các loại đầu vào khác nhau. Dưới đây là một ví dụ về chat template:
-
-```sh
-<|im_start|>user
-Hi there!<|im_end|>
-<|im_start|>assistant
-Nice to meet you!<|im_end|>
-<|im_start|>user
-Can I ask a question?<|im_end|>
-<|im_start|>assistant
-```
-
-Thư viện `transformers` sẽ xử lý *định dạng chat* cho bạn liên quan đến tokenizer của mô hình. Đọc thêm về cách transformers xây dựng *định dạng chat* [tại đây](https://huggingface.co/docs/transformers/en/chat_templating#how-do-i-use-chat-templates). Tất cả những gì chúng ta cần làm là cấu trúc tin nhắn của mình theo cách chính xác và *tokenizer() sẽ xử lý phần còn lại. Đây là một ví dụ cơ bản về một cuộc hội thoại:
-
-```python
-messages = [
-    {"role": "system", "content": "You are a helpful assistant focused on technical topics."},
-    {"role": "user", "content": "Can you explain what a chat template is?"},
-    {"role": "assistant", "content": "A chat template structures conversations between users and AI models..."}
-]
-```
-
-Hãy cùng nhau phân tích ví dụ trên để hiểu hơn về *định dạng chat*
-
-## Mệnh lệnh hệ thống (System Prompt)
-
-Mệnh lệnh hệ thống thiết lập nền tảng cho cách mô hình nên hoạt động. Chúng đóng vai trò như các hướng dẫn ảnh hưởng liên tục đến tất cả các tương tác tiếp theo. Ví dụ:
-
-```python
-system_message = {
-    "role": "system",
-    "content": "You are a professional customer service agent. Always be polite, clear, and helpful."
-}
-```
-
-## Cuộc hội thoại
-
-*Định dạng chat* duy trì ngữ cảnh thông qua lịch sử hội thoại, lưu trữ các trao đổi trước đó giữa người dùng và trợ lý. Điều này cho phép các cuộc hội thoại mạch lạc hơn:
-
-```python
-conversation = [
-    {"role": "user", "content": "I need help with my order"},
-    {"role": "assistant", "content": "I'd be happy to help. Could you provide your order number?"},
-    {"role": "user", "content": "It's ORDER-123"},
-]
-```
-
-## Triển khai với thư viện Transformers
-
-Thư viện transformers cung cấp hỗ trợ tích hợp cho *định dạng chat*. Đây là cách sử dụng:
-
-```python
-from transformers import AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-135M-Instruct")
-
-messages = [
-    {"role": "system", "content": "You are a helpful coding assistant."},
-    {"role": "user", "content": "Write a Python function to sort a list"},
-]
-
-# Apply the chat template
-formatted_chat = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-```
-
-## Tuỳ chỉnh Định dạng Chat
-Bạn có thể tùy chỉnh cách định dạng các loại tin nhắn khác nhau. Ví dụ, thêm *special token* hoặc định dạng cho các vai trò khác nhau:
-
-```python
-template = """
-<|system|>{system_message}
-<|user|>{user_message}
-<|assistant|>{assistant_message}
-""".lstrip()
-```
-
-## Hỗ trợ hội thoại đa lượt (multi-turn conversations)
-
-Với *định dạng chat*, mô hình có thể xử lý các cuộc hội thoại phức tạp nhiều lượt trong khi vẫn duy trì ngữ cảnh:
-
-```python
-messages = [
-    {"role": "system", "content": "You are a math tutor."},
-    {"role": "user", "content": "What is calculus?"},
-    {"role": "assistant", "content": "Calculus is a branch of mathematics..."},
-    {"role": "user", "content": "Can you give me an example?"},
-]
-```
-
-⏭️ [Tiếp theo: Huấn luyện có giám sát](./supervised_fine_tuning.md)
-
-## Tài liệu tham khảo
-
-- [Hướng dẫn Định dạng Chat của Hugging Face](https://huggingface.co/docs/transformers/main/en/chat_templating)
-- [Tài liệu về thư viện Transformers](https://huggingface.co/docs/transformers)
-- [Ví dụ về Định dạng Chat](https://github.com/chujiezheng/chat_templates) 
diff --git a/vi/1_instruction_tuning/notebooks/.env.example b/vi/1_instruction_tuning/notebooks/.env.example
deleted file mode 100644
index 6ff34688..00000000
--- a/vi/1_instruction_tuning/notebooks/.env.example
+++ /dev/null
@@ -1,2 +0,0 @@
-# Sao chép file này thành .env và điền các API key
-HF_TOKEN=
\ No newline at end of file
diff --git a/vi/1_instruction_tuning/notebooks/chat_templates_example.ipynb b/vi/1_instruction_tuning/notebooks/chat_templates_example.ipynb
deleted file mode 100644
index b02cdcbf..00000000
--- a/vi/1_instruction_tuning/notebooks/chat_templates_example.ipynb
+++ /dev/null
@@ -1,5743 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "vZAvFVIAtFlq"
-   },
-   "source": [
-    "# Tìm hiểu về Định dạng Chat với SmolLM2\n",
-    "\n",
-    "Trong notebook này, chúng ta sẽ học về cách sử dụng định dạng Chat với mô hình SmolLM2. Định dạng Chat giúp cấu trúc hóa các tương tác giữa người dùng và mô hình ngôn ngữ, đảm bảo các phản hồi nhất quán và phù hợp với ngữ cảnh."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "K-lZu8JvtwUN",
-    "outputId": "c3871418-15bc-4265-ae8d-6d6036036d0e"
-   },
-   "outputs": [],
-   "source": [
-    "# Cài đặt các thư viện cần thiết\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "# Đăng nhập vào Hugging Face\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()\n",
-    "\n",
-    "# Để thuận tiện, bạn có thể tạo một biến môi trường chứa `token hub` của bạn dưới dạng HF_TOKEN"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "tnHzBR7vtFlr"
-   },
-   "outputs": [],
-   "source": [
-    "# Các thư viện cần thiết \n",
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "from trl import setup_chat_format\n",
-    "import torch"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "XTVOqbuetFlr"
-   },
-   "source": [
-    "## Định dạng chat của mô hình SmolLM2\n",
-    "\n",
-    "Hãy khám phá cách sử dụng *định dạng chat* với mô hình `SmolLM2`. Chúng ta sẽ định nghĩa một cuộc hội thoại đơn giản và áp dụng *định dạng chat*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 397,
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-     ]
-    },
-    "id": "Nrxh0oX6tFls",
-    "outputId": "953e1527-8168-4346-9338-6e188ca31a1a"
-   },
-   "outputs": [],
-   "source": [
-    "# Thiết lập phần cứng một cách linh động\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=model_name\n",
-    ").to(device)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
-    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "zkJwILrbtFls"
-   },
-   "outputs": [],
-   "source": [
-    "# Ví dụ về tin nhắn\n",
-    "messages = [\n",
-    "    {\"role\": \"user\", \"content\": \"Hello, how are you?\"},\n",
-    "    {\n",
-    "        \"role\": \"assistant\",\n",
-    "        \"content\": \"I'm doing well, thank you! How can I assist you today?\",\n",
-    "    },\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Ve4dgtjstFls"
-   },
-   "source": [
-    "# Áp dụng định dạng chat mà không tokenize\n",
-    "\n",
-    "Tokenizer sẽ biểu diễn cuộc hội thoại dưới dạng chuỗi với các *token đặc biệt* để mô tả vai trò của người dùng và trợ lý.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "pbAg-5x-tFls",
-    "outputId": "5f9482db-1fcf-4c13-ccaa-ef3f6eff7f76"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Conversation with template: <|im_start|>user\n",
-      "Hello, how are you?<|im_end|>\n",
-      "<|im_start|>assistant\n",
-      "I'm doing well, thank you! How can I assist you today?<|im_end|>\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "input_text = tokenizer.apply_chat_template(messages, tokenize=False)\n",
-    "\n",
-    "print(\"Cuộc hội thoại với định dạng:\", input_text)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "sfvdglOqtFls"
-   },
-   "source": [
-    "# Giải mã cuộc hội thoại\n",
-    "\n",
-    "Lưu ý rằng cuộc hội thoại được biểu diễn như trên nhưng với thêm một tin nhắn trợ lý.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "mXUVdPeytFls",
-    "outputId": "80870e53-7bc1-426e-ac33-ba6748e030fc"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Conversation decoded: <|im_start|>user\n",
-      "Hello, how are you?<|im_end|>\n",
-      "<|im_start|>assistant\n",
-      "I'm doing well, thank you! How can I assist you today?<|im_end|>\n",
-      "<|im_start|>assistant\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "input_text = tokenizer.apply_chat_template(\n",
-    "    messages, tokenize=True, add_generation_prompt=True\n",
-    ")\n",
-    "\n",
-    "print(\"Cuộc hội thoại được giải mã:\", tokenizer.decode(token_ids=input_text))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "UcZQpspEtFlt"
-   },
-   "source": [
-    "# Tokenize cuộc hội thoại\n",
-    "\n",
-    "Tất nhiên, *tokenizer* cũng tokenize cuộc hội thoại và các token đặc biệt thành các id liên quan đến từ vựng của mô hình.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "jc2PLxAMtFlt",
-    "outputId": "d2098780-b3f4-41ec-a1f3-b6da2b593c62"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Conversation tokenized: [1, 4093, 198, 19556, 28, 638, 359, 346, 47, 2, 198, 1, 520, 9531, 198, 57, 5248, 2567, 876, 28, 9984, 346, 17, 1073, 416, 339, 4237, 346, 1834, 47, 2, 198, 1, 520, 9531, 198]\n"
-     ]
-    }
-   ],
-   "source": [
-    "input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)\n",
-    "\n",
-    "print(\"Cuộc hội thoại được tokenize:\", input_text)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "m3eNp9a0tFlt"
-   },
-   "source": [
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Bài tập: Xử lý dataset cho SFT</h2>\n",
-    "    <p>Lấy một dataset từ Hugging Face hub và xử lý nó cho SFT. </p>\n",
-    "    <p><b>Các bài tập</b></p>\n",
-    "    <p>🐢 Chuyển đổi dataset `HuggingFaceTB/smoltalk` sang định dạng <strong>chatml</strong>.</p>\n",
-    "    <p>🐕 Chuyển đổi dataset `openai/gsm8k` sang định dạng <strong>chatml</strong>.</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Định dạng ChatML:**\n",
-    "\n",
-    "```sh\n",
-    "<|im_start|>user\n",
-    "Hi there!<|im_end|>\n",
-    "<|im_start|>assistant\n",
-    "Nice to meet you!<|im_end|>\n",
-    "<|im_start|>user\n",
-    "Can I ask a question?<|im_end|>\n",
-    "<|im_start|>assistant\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 381
-    },
-    "id": "qbkXV2_ItFlt",
-    "outputId": "06deadc3-2c63-4660-d2bd-05096ef07c9f"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<iframe\n",
-       "  src=\"https://huggingface.co/datasets/HuggingFaceTB/smoltalk/embed/viewer/all/train?row=0\"\n",
-       "  frameborder=\"0\"\n",
-       "  width=\"100%\"\n",
-       "  height=\"360px\"\n",
-       "></iframe>\n"
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from IPython.core.display import display, HTML\n",
-    "\n",
-    "display(\n",
-    "    HTML(\n",
-    "        \"\"\"<iframe\n",
-    "  src=\"https://huggingface.co/datasets/HuggingFaceTB/smoltalk/embed/viewer/all/train?row=0\"\n",
-    "  frameborder=\"0\"\n",
-    "  width=\"100%\"\n",
-    "  height=\"360px\"\n",
-    "></iframe>\n",
-    "\"\"\"\n",
-    "    )\n",
-    ")"
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-    },
-    "id": "4p3atw4_tFlu",
-    "outputId": "62ee9812-3819-4a9c-9e24-5687368ffcd8"
-   },
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "ds = load_dataset(\"HuggingFaceTB/smoltalk\", \"everyday-conversations\")\n",
-    "\n",
-    "\n",
-    "def process_dataset(sample):\n",
-    "    # TODO: 🐢 Chuyển đổi dữ liệu sang định dạng ChatML\n",
-    "    # Sử dụng `tokenizer.apply_chat_template` để áp dụng định dạng hội thoại\n",
-    "    return sample\n",
-    "\n",
-    "\n",
-    "ds = ds.map(process_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 381
-    },
-    "id": "81fQeazltFlu",
-    "outputId": "36cf7148-9881-4f13-d0ce-76c82c4ab219"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<iframe\n",
-       "  src=\"https://huggingface.co/datasets/openai/gsm8k/embed/viewer/main/train\"\n",
-       "  frameborder=\"0\"\n",
-       "  width=\"100%\"\n",
-       "  height=\"360px\"\n",
-       "></iframe>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "display(\n",
-    "    HTML(\n",
-    "        \"\"\"<iframe\n",
-    "  src=\"https://huggingface.co/datasets/openai/gsm8k/embed/viewer/main/train\"\n",
-    "  frameborder=\"0\"\n",
-    "  width=\"100%\"\n",
-    "  height=\"360px\"\n",
-    "></iframe>\n",
-    "\"\"\"\n",
-    "    )\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true,
-    "id": "bWUSv7NMtFlu"
-   },
-   "outputs": [],
-   "source": [
-    "ds = load_dataset(\"openai/gsm8k\", \"main\")\n",
-    "\n",
-    "\n",
-    "def process_dataset(sample):\n",
-    "    # TODO: 🐕 Chuyển đổi dữ liệu sang định dạng ChatML\n",
-    "\n",
-    "    # 1. Tạo định dạng tin nhắn với vai trò và nội dung\n",
-    "\n",
-    "    # 2. Áp dụng định dạng chat cho các mẫu bằng tokenizer\n",
-    "\n",
-    "    return sample\n",
-    "\n",
-    "\n",
-    "ds = ds.map(process_dataset)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "qlXCuRKotFlu"
-   },
-   "source": [
-    "## Kết luận\n",
-    "\n",
-    "Trong bài học này, chúng ta đã học được cách áp dụng định dạng chat cho các mô hình khác nhau, cụ thể là `SmolLM2`. Bằng cách cấu trúc các tương tác với định dạng chat, chúng ta có thể đảm bảo rằng các mô hình ngôn ngữ cung cấp các phản hồi nhất quán và phù hợp với ngữ cảnh.\n",
-    "\n",
-    "Trong bài tập, bạn đã thử chuyển đổi một dataset sang định dạng *chatml*. May mắn thay, trong thực tế thư viện TRL sẽ tự động làm điều này. Tuy vậy, việc hiểu những gì đang thực sự diễn ra đằng sau các hàm sẽ hữu ích hơn cho bạn."
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diff --git a/vi/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb b/vi/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb
deleted file mode 100644
index 26335301..00000000
--- a/vi/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb
+++ /dev/null
@@ -1,296 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Huấn luyện có giám sát với SFTTrainer\n",
-    "\n",
-    "Bài học này se dạy bạn các huấn luyện mô hình `HuggingFaceTB/SmolLM2-135M` bằng `SFTTrainer` trong thư viện `trl`.  Các cell trong notebook này sẽ chạy và huấn luyện mô hình. Bạn có thể chọn độ khó bằng cách thử nghiệm với các bộ dữ liệu khác nhau.\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Bài tập: Fine-Tuning SmolLM2 với SFTTrainer</h2>\n",
-    "    <p>Chọn một bộ dự liệu từ Hugging Face hub và huấn luyện một mô hình trên bộ dữ liệu đó. </p> \n",
-    "    <p><b>Các bài tập</b></p>\n",
-    "    <p>🐢 Sử dụng bộ dữ liệu `HuggingFaceTB/smoltalk`</p>\n",
-    "    <p>🐕 Thử nghiệm với bộ dữ liệu `bigcode/the-stack-smol` và huấn luyện một mô hình sinh code trên tập con cụ thể `data/python`.</p>\n",
-    "    <p>🦁 Chọn một bộ dữ liệu liên quan đến một lĩnh vực mà bạn quan tâm</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Cài đặt các thư viện cần thiết\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "# Đăng nhập vào Hugging Face\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()\n",
-    "\n",
-    "# Để thuận tiện, bạn có thể tạo một biến môi trường chứa `token hub` của bạn dưới dạng HF_TOKEN"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Các thư viện cần thiết\n",
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "from datasets import load_dataset\n",
-    "from trl import SFTConfig, SFTTrainer, setup_chat_format\n",
-    "import torch\n",
-    "\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "# Tải mô hình và tokenizer\n",
-    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=model_name\n",
-    ").to(device)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
-    "\n",
-    "# Thiết lập định dạng chat\n",
-    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n",
-    "\n",
-    "# Đặt tên cho mô hình huấn luyện để lưu &/ tải lên\n",
-    "finetune_name = \"SmolLM2-FT-MyDataset\"\n",
-    "finetune_tags = [\"smol-course\", \"module_1\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Sinh văn bản với Mô hình gốc\n",
-    "\n",
-    "Ở đây chúng ta sẽ thử nghiệm mô hình gốc chưa được huấn luyện trên định dạng chat."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Kiểm tra mô hình gốc trước khi huấn luyện\n",
-    "prompt = \"Write a haiku about programming\"\n",
-    "\n",
-    "# Định dạng\n",
-    "messages = [{\"role\": \"user\", \"content\": prompt}]\n",
-    "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n",
-    "\n",
-    "# Tạo phản hồi từ mô hình\n",
-    "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
-    "outputs = model.generate(**inputs, max_new_tokens=100)\n",
-    "print(\"Before training:\")\n",
-    "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Chuẩn bị dữ liệu\n",
-    "\n",
-    "Chúng ta sẽ tải một bộ dữ liệu mẫu và định dạng nó cho việc huấn luyện. Bộ dữ liệu cần được cấu trúc với các cặp đầu vào - đầu ra, trong đó mỗi đầu vào là một chỉ thị và đầu ra là phản hồi mong đợi từ mô hình.\n",
-    "\n",
-    "**TRL sẽ định dạng các tin nhắn đầu vào dựa trên định dạng chat của mô hình** Chúng cần được biểu diễn dưới dạng danh sách các từ điển với các khóa: `role` và `content`.\n",
-    "\n",
-    "**Ví dụ:**\n",
-    "```sh\n",
-    "[\n",
-    "  {\"role\": \"user\", \"content\": \"Hello, how are you?\"},\n",
-    "  {\"role\": \"assistant\", \"content\": \"I'm doing well, thank you! How can I assist you today?\",},\n",
-    "]\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Tải dữ liệu mẫu\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "# TODO: Tải bộ dữ liệu thông qua việc điều chỉnh các tham số path và name\n",
-    "ds = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO: 🦁 Nếu dataset của bạn không ở định dạng mà TRL có thể chuyển đổi thành định dạng chat, bạn sẽ cần xử lý nó.\n",
-    "# Tham khảo [Định dạng Chat](../chat_templates.md)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Điều chỉnh SFTTrainer\n",
-    "\n",
-    "Điều chỉnh `SFTTrainer` với các tham số khác nhau giúp điều khiển quá trình huấn luyện trở nên hiệu quả hơn. Các thông số bao gồm\n",
-    "- Số bước huấn luyện (steps)\n",
-    "- Kích thước batch (batch size)\n",
-    "- Tốc độ học (learning rate)\n",
-    "- Chiến lược đánh giá mô hình (evaluation strategy)\n",
-    "\n",
-    "Ngoài ra, còn rất nhiều thông số khác, bạn có thể tham khảo thêm ở [SFTTrainer](https://huggingface.co/docs/trl/sft_trainer)\n",
-    "\n",
-    "Điều chỉnh các tham số này dựa trên yêu cầu cụ thể và tài nguyên tính toán của bạn."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Điều chỉnh SFTTrainer\n",
-    "sft_config = SFTConfig(\n",
-    "    output_dir=\"./sft_output\",\n",
-    "    max_steps=1000,  # Điều chỉnh dựa trên kích thước dataset và thời lượng huấn luyện mong muốn\n",
-    "    per_device_train_batch_size=4,  # Đặt theo dung lượng bộ nhớ GPU của bạn\n",
-    "    learning_rate=5e-5,  # Phổ biến cho quá trình huấn luyện có giám sát\n",
-    "    logging_steps=10,  # Tần suất ghi log các metrics huấn luyện\n",
-    "    save_steps=100,  # Tần suất lưu các checkpoint mô hình\n",
-    "    evaluation_strategy=\"steps\",  # Đánh giá mô hình theo các khoảng thời gian\n",
-    "    eval_steps=50,  # Tần suất đánh giá\n",
-    "    use_mps_device=(\n",
-    "        True if device == \"mps\" else False\n",
-    "    ),  # Sử dụng MPS cho huấn luyện độ chính xác hỗn hợp\n",
-    "    hub_model_id=finetune_name,  # Đặt tên cho mô hình của bạn\n",
-    ")\n",
-    "\n",
-    "# Khởi tạo SFTTrainer\n",
-    "trainer = SFTTrainer(\n",
-    "    model=model,\n",
-    "    args=sft_config,\n",
-    "    train_dataset=ds[\"train\"],\n",
-    "    tokenizer=tokenizer,\n",
-    "    eval_dataset=ds[\"test\"],\n",
-    ")\n",
-    "\n",
-    "\n",
-    "# TODO: 🦁 🐕 căn chỉnh các tham số SFTTrainer với bộ dữ liệu bạn đã chọn.\n",
-    "# Ví dụ, nếu bạn đang sử dụng bộ `bigcode/the-stack-smol`, bạn sẽ cần chọn cột `content`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Huấn luyện Mô hình\n",
-    "\n",
-    "Với trainer đã được điều chỉnh, chúng ta có thể tiến hành huấn luyện mô hình. Quá trình huấn luyện sẽ bao gồm\n",
-    "- Lặp qua bộ dữ liệu\n",
-    "- Tính toán loss\n",
-    "- Cập nhật các tham số của mô hình để giảm thiểu loss này."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Huấn luyện mô hình\n",
-    "trainer.train()\n",
-    "\n",
-    "# Lưu mô hình\n",
-    "trainer.save_model(f\"./{finetune_name}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Đưa mô hình lên Hugging Face Hub\n",
-    "trainer.push_to_hub(tags=finetune_tags)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Bài tập thêm: Sinh văn bản với mô hình vừa được huấn luyện</h2>\n",
-    "    <p>🐕 Sử dụng mô hình đã được huấn luyện để sinh ra phản hồi, giống như với ví dụ ban đầu.</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Kiểm tra mô hình đã được huấn luyện trên cùng một yêu cầu\n",
-    "\n",
-    "# Kiểm tra mô hình gốc trước khi huấn luyện\n",
-    "prompt = \"Write a haiku about programming\"\n",
-    "\n",
-    "# Định dạng chat\n",
-    "messages = [{\"role\": \"user\", \"content\": prompt}]\n",
-    "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n",
-    "\n",
-    "# Sinh phản hồi từ mô hình\n",
-    "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
-    "\n",
-    "# TODO: sử dụng mô hình đã được huấn luyện để sinh phản hồi, giống như với ví dụ."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 💐 Chúc mừng bạn. Bạn đã hoàn thành!\n",
-    "\n",
-    "Bài tập này đã cung cấp hướng dẫn từng bước để bạn huấn luyện được mô hình `HuggingFaceTB/SmolLM2-135M` sử dụng `SFTTrainer`. Bằng cách làm theo các bước này, bạn có thể điều chỉnh mô hình để thực hiện các tác vụ cụ thể hiệu quả hơn. Nếu bạn muốn tiếp tục làm việc với khóa học này, đây là một số bước bạn có thể thử:\n",
-    "\n",
-    "- Thử notebook này ở mức độ khó hơn\n",
-    "- Review PR của học viên khác\n",
-    "- Cải thiện tài liệu khóa học thông qua Issue hoặc PR."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py310",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.15"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/vi/1_instruction_tuning/supervised_fine_tuning.md b/vi/1_instruction_tuning/supervised_fine_tuning.md
deleted file mode 100644
index 9800fb66..00000000
--- a/vi/1_instruction_tuning/supervised_fine_tuning.md
+++ /dev/null
@@ -1,41 +0,0 @@
-# Tinh chỉnh có giám sát (Supervised Fine-Tuning)
-
-Tinh chỉnh có giám sát (SFT) là một quá trình cốt lõi để điều chỉnh *pre-trained models* cho các tác vụ hoặc lĩnh vực cụ thể. Mặc dù các mô hình đã *pre-trained* có các khả năng tổng quát ấn tượng  ,chúng thường cần được tùy chỉnh để đạt hiệu suất cao trong các trường hợp sử dụng cụ thể. SFT thu hẹp khoảng cách này bằng cách huấn luyện thêm mô hình trên các tập dữ liệu được tuyển chọn kỹ lưỡng cùng với các ví dụ đã được con người đánh giá.
-
-## Tìm hiểu thêm về huấn luyện có giám sát
-
-Về cốt lõi, SFT là về việc dạy một mô hình đã *pre-trained* thực hiện các tác vụ cụ thể thông qua các mẫu của các *token* đã được gán nhãn. Quá trình này bao gồm việc cho mô hình học nhiều mẫu về các hành vi đầu vào-đầu ra mong muốn, cho phép nó học các mẫu cụ thể cho trường hợp sử dụng của bạn.
-
-SFT hiệu quả vì nó sử dụng kiến thức nền tảng thu được trong quá trình *pre-training* trong khi điều chỉnh hành vi của mô hình để phù hợp với nhu cầu cụ thể của bạn.
-
-## Khi nào nên sử dụng huấn luyện có giám sát
-
-Quyết định sử dụng SFT thường phụ thuộc vào khoảng cách giữa khả năng hiện tại của mô hình và yêu cầu cụ thể của bạn. SFT trở nên đặc biệt có giá trị khi bạn cần kiểm soát chính xác đầu ra của mô hình hoặc khi làm việc trong các lĩnh vực chuyên biệt.
-
-Ví dụ, nếu bạn đang phát triển một ứng dụng dịch vụ khách hàng, bạn có thể muốn mô hình của mình liên tục tuân theo hướng dẫn của công ty và xử lý các truy vấn kỹ thuật theo cách chuẩn hóa. Tương tự, trong các ứng dụng y tế hoặc pháp lý, độ chính xác và tuân thủ thuật ngữ chuyên ngành trở nên cực kỳ quan trọng. Trong những trường hợp này, SFT có thể giúp điều chỉnh phản hồi của mô hình phù hợp với tiêu chuẩn chuyên môn và chuyên môn trong lĩnh vực.
-
-## Quy trình huấn luyện
-
-Quy trình huấn luyện có giám sát bao gồm việc huấn luyện trọng số mô hình trên một tập dữ liệu theo tác vụ cụ thể.
-
-Đầu tiên, bạn cần chuẩn bị hoặc lựa chọn một tập dữ liệu đại diện cho tác vụ mục tiêu của bạn. Tập dữ liệu này nên bao gồm các mẫu đa dạng bao quát phạm vi các tình huống mà mô hình của bạn sẽ gặp phải. Chất lượng của dữ liệu này rất quan trọng - mỗi mẫu nên thể hiện loại đầu ra mà bạn muốn mô hình của mình tạo ra. Tiếp theo là giai đoạn huấn luyện thực tế, nơi bạn sẽ sử dụng các framework có sẵn như `transformers` và `trl` của Hugging Face để huấn luyện mô hình trên tập dữ liệu của bạn.
-
-Trong suốt quá trình này, việc đánh giá liên tục là thiết yếu. Bạn sẽ muốn theo dõi hiệu suất của mô hình trên một tập đánh giá (validation set) để đảm bảo nó đang học các hành vi mong muốn mà không mất đi khả năng tổng quát. Trong [bài 4](./4_evaluation), chúng ta sẽ tìm hiểu cách đánh giá mô hình đã được huấn luyện.
-
-## Vai trò của huấn luyện có giám sát trong điều chỉnh theo sự uy tiên
-
-SFT đóng vai trò nền tảng trong việc điều chỉnh các mô hình ngôn ngữ theo ưu tiên của con người. Các kỹ thuật như Học tăng cường từ phản hồi của con người (Reinforcement Learning from Human Feedback - RLHF) và Tối ưu hóa sở thích trực tiếp (Direct Preference Optimization - DPO) dựa vào SFT để xây dựng mức độ hiểu biết cơ bản về tác vụ trước khi tiếp tục điều chỉnh phản hồi của mô hình sao cho phù hợp với kết quả mong muốn. Các mô hình đã *pre-trained*, mặc dù có khả năng ngôn ngữ tổng quát, có thể không phải lúc nào cũng tạo ra đầu ra phù hợp ưu tiên của con người. SFT thu hẹp khoảng cách này bằng cách đưa vào dữ liệu và hướng dẫn theo lĩnh vực cụ thể, cải thiện khả năng của mô hình trong việc tạo ra phản hồi phù hợp hơn với kỳ vọng của con người.
-
-## Huấn luyện có giám sát với Transformer Reinforcement Learning
-
-Một thư viện quan trọng cho SFT đó là Transformer Reinforcement Learning (TRL). TRL là một bộ công cụ được sử dụng để huấn luyện các mô hình ngôn ngữ transformer bằng học tăng cường (RL).
-
-Được xây dựng trên thư viện Transformers của Hugging Face, TRL cho phép người dùng trực tiếp tải các mô hình ngôn ngữ đã được *pre-trained* và hỗ trợ hầu hết các kiến trúc decoder và encoder-decoder. Thư viện này tạo điều kiện cho các quy trình chính của RL được sử dụng trong mô hình hóa ngôn ngữ, bao gồm Supervised Fine-Tuning (SFT), Reward Modeling (RM), Proximal Policy Optimization (PPO), và Direct Preference Optimization (DPO). Chúng ta sẽ sử dụng TRL trong nhiều bài học trong khoá học này.
-
-# Các bước tiếp theo
-
-Hãy thử các hướng dẫn sau để có tìm hiểu các ví dụ SFT thông qua TRL:
-
-⏭️ [Hướng dẫn Địng dạng Chat](./notebooks/chat_templates_example.ipynb)
-
-⏭️ [Hướng dẫn Huấn luyện có giám sát](./notebooks/supervised_fine_tuning_tutorial.ipynb)
\ No newline at end of file
diff --git a/vi/2_preference_alignment/README.md b/vi/2_preference_alignment/README.md
deleted file mode 100644
index 1b36d236..00000000
--- a/vi/2_preference_alignment/README.md
+++ /dev/null
@@ -1,41 +0,0 @@
-# Tinh Chỉnh Theo Sự Ưu Tiên (Preference Alignment)
-
-Trong chương này, bạn sẽ học về các kỹ thuật tinh chỉnh mô hình ngôn ngữ theo sự ưu tiên của con người. Trong khi *học có giám sát* giúp mô hình học các tác vụ, *tinh chỉnh theo sự ưu tiên* khuyến khích đầu ra phù hợp với kỳ vọng và giá trị của con người.
-
-## Tổng Quan
-
-Các phương pháp *tinh chỉnh theo sự ưu tiên* thường bao gồm 2 giai đoạn:
-
-1. Bắt đầu bằng quá trình *học có giám sát* (SFT) để thích ứng mô hình với các lĩnh vực cụ thể
-2. Sau đó, tinh chỉnh mô hình theo sự ưu tiên (như RLHF hoặc DPO) để cải thiện chất lượng phản hồi
-
-Các phương pháp thay thế như ORPO kết hợp cả *tinh chỉnh theo chỉ thị* và *tinh chỉnh theo sự ưu tiên* thành 1 giai đoạn tinh chỉnh duy nhất. Ở đây, chúng ta sẽ tập trung vào các thuật toán DPO và ORPO.
-
-Nếu bạn muốn tìm hiểu thêm về các kỹ thuật tinh chỉnh khác, bạn có thể đọc thêm tại [Argilla Blog](https://argilla.io/blog/mantisnlp-rlhf-part-8).
-
-### 1️⃣ Tối Ưu Hóa Ưu Tiên Trực Tiếp (Direct Preference Optimization - DPO)
-
-Phương pháp này đơn giản hóa quá trình *tinh chỉnh theo chỉ thị* bằng cách tối ưu hóa trực tiếp mô hình sử dụng dữ liệu ưu tiên (preference data). Phương pháp này loại bỏ nhu cầu về các *Mô hình thưởng phạt* (Reward model) riêng biệt và *Học tăng cường* phức tạp, giúp quá trình ổn định và hiệu quả hơn so với Học tăng cường từ phản hồi của con người (RLHF) truyền thống. Để biết thêm chi tiết, bạn có thể tham khảo tài liệu [*tối ưu hóa ưu tiên trực tiếp* (DPO)](./dpo.md).
-
-### 2️⃣ Tối Ưu Hóa Ưu Tiên Theo Tỷ Lệ Odds (Odds Ratio Preference Optimization - ORPO)
-
-ORPO giới thiệu một phương pháp kết hợp cả 2 giai đoạn *tinh chỉnh theo chỉ thị* và *tinh chỉnh theo sự ưu tiên* vào trong 1 giai đoạn tinh chỉnh duy nhất. Phương pháp này điều chỉnh mục tiêu tiêu chuẩn của mô hình ngôn ngữ bằng cách kết hợp *negative log-likelihood loss* với một * tỷ lệ odds* ở cấp độ *token*. Vì vậy, ORPO tạo ra 1 quá trình tinh chỉnh thống nhất với kiến trúc không cần mô hình thưởng phạt và cải thiện đáng kể hiệu quả tính toán. ORPO đã cho thấy kết quả ấn tượng trên nhiều benchmark, thể hiện hiệu suất tốt hơn trên AlpacaEval so với các phương pháp truyền thống. Để biết thêm chi tiết, bạn có thể tham khảo tài liệu [tối ưu hóa ưu tiên theo tỷ lệ odds (ORPO)](./orpo.md).
-
-## Bài Tập
-
-| Tiêu đề | Mô tả | Bài tập | Đường dẫn | Colab |
-|-------|-------------|----------|------|-------|
-| Tinh chỉnh theo DPO | Học cách tinh chỉnh mô hình bằng phương pháp tối ưu hóa ưu tiên trực tiếp | 🐢 Tinh chỉnh mô hình sử dụng bộ dữ liệu HH-RLHF <br>🐕 Sử dụng tập dữ liệu của riêng bạn<br>🦁 Thử nghiệm với các tập dữ liệu và kích thước mô hình khác nhau | [Notebook](./notebooks/dpo_finetuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Sử dụng Colab"/></a> |
-| Tinh chỉnh theo ORPO | Học cách tinh chỉnh mô hình bằng phương pháp tối ưu hóa ưu tiên theo tỷ lệ odds | 🐢 Huấn luyện mô hình sử dụng bộ dữ liệu chỉ thị (instruction) và dữ liệu ưu tiên (preference)<br>🐕 Thử nghiệm với các trọng số loss khác nhau<br>🦁 So sánh kết quả ORPO với DPO | [Notebook](./notebooks/orpo_finetuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Sử dụng Colab"/></a> |
-
-
-## Resources
-
-- [Tài liệu thư viện TRL](https://huggingface.co/docs/trl/index) - Tài liệu cho thư viện Transformers Reinforcement Learning (TRL), triển khai nhiều kỹ thuật căn chỉnh bao gồm DPO và ORPO.
-- [Bài báo nghiên cứu DPO](https://arxiv.org/abs/2305.18290) - bài nghiên cứu gốc giới thiệu *tối ưu hóa ưu tiên trực tiếp* như một giải pháp thay thế đơn giản hơn cho RLHF.
-- [Bài báo nghiên cứu ORPO](https://arxiv.org/abs/2403.07691) - Giới thiệu Odds Ratio Preference Optimization, một phương pháp mới kết hợp *tinh chỉnh theo chỉ thị* và *tinh chỉnh theo sự ưu tiên* thành 1
-- [Bài hướng dẫn của Argilla](https://argilla.io/blog/mantisnlp-rlhf-part-8/) - Hướng dẫn giải thích các kỹ thuật căn chỉnh khác nhau bao gồm RLHF, DPO và cách triển khai thực tế.
-- [Blog về DPO](https://huggingface.co/blog/dpo-trl) - Hướng dẫn thực hành về triển khai DPO sử dụng thư viện TRL với các ví dụ code và phương pháp tốt nhất.
-- [Code mẫu cho DPO trong thư viên TRL](https://github.com/huggingface/trl/blob/main/examples/scripts/dpo.py) - Code mẫu về cách triển khai tinh chỉnh DPO sử dụng thư viện TRL.
-- [Code mẫu cho ORPD trong thư viên TRL](https://github.com/huggingface/trl/blob/main/examples/scripts/orpo.py) - Code mẫu của tinh chỉnh ORPO sử dụng thư viện TRL với các tùy chọn cấu hình chi tiết.
-- [Hugging Face Alignment Handbook](https://github.com/huggingface/alignment-handbook) - Hướng dẫn và codebase cho việc tinh chỉnh mô hình ngôn ngữ sử dụng các kỹ thuật khác nhau bao gồm SFT, DPO và RLHF.
diff --git a/vi/2_preference_alignment/dpo.md b/vi/2_preference_alignment/dpo.md
deleted file mode 100644
index 13a4a82a..00000000
--- a/vi/2_preference_alignment/dpo.md
+++ /dev/null
@@ -1,75 +0,0 @@
-# Tối Ưu Hóa Ưu Tiên Trực Tiếp (Direct Preference Optimization - DPO)
-
-DPO cung cấp một cách tiếp cận đơn giản để tinh chỉnh mô hình ngôn ngữ theo ý muốn của con người. Khác với phương pháp RLHF truyền thống yêu cầu các mô hình thưởng phạt riêng biệt và học tăng cường phức tạp, DPO tối ưu hóa trực tiếp mô hình bằng dữ liệu ưu tiên (preference dataset).
-
-## Tìm Hiểu Về DPO
-
-DPO chuyển đổi bài toán tinh chỉnh ưu tiên thành bài toán phân loại trên dữ liệu ưu tiên của con người. Phương pháp RLHF truyền thống yêu cầu huấn luyện một mô hình thưởng phạt riêng biệt và sử dụng các thuật toán học tăng cường phức tạp như PPO để căn chỉnh đầu ra của mô hình. DPO đơn giản hóa quy trình này bằng cách định nghĩa một hàm mất mát (loss) trực tiếp tối ưu hóa chiến lược học (policy) của mô hình dựa trên các đầu ra được ưu tiên và không được ưu tiên.
-
-Phương pháp này đã chứng minh hiệu quả cao trong thực tế, được sử dụng để huấn luyện các mô hình như Llama. Bằng cách loại bỏ nhu cầu về mô hình thưởng phạt riêng biệt và giai đoạn học tăng cường, DPO giúp việc tinh chỉnh ưu tiên trở nên dễ tiếp cận và ổn định hơn.
-
-## DPO Hoạt Động Như Thế Nào
-
-Quy trình DPO yêu cầu quá trình học có giám sát (SFT) để thích ứng mô hình với lĩnh vực mục tiêu. Điều này tạo nền tảng cho việc học ưu tiên bằng cách huấn luyện trên các tập dữ liệu làm theo chỉ thị tiêu chuẩn. Mô hình học cách hoàn thành tác vụ cơ bản trong khi duy trì các khả năng tổng quát.
-
-Tiếp theo là quá trình học ưu tiên, nơi mô hình được huấn luyện trên các cặp đầu ra - một được ưu tiên và một không được ưu tiên. Các cặp ưu tiên giúp mô hình hiểu phản hồi nào phù hợp hơn với giá trị và kỳ vọng của con người.
-
-Đổi mới cốt lõi của DPO nằm ở cách tiếp cận tối ưu hóa trực tiếp. Thay vì huấn luyện một mô hình thưởng phạt riêng biệt, DPO sử dụng hàm mất mát `binary cross-entropy` để trực tiếp cập nhật trọng số mô hình dựa trên dữ liệu ưu tiên. Quy trình đơn giản này giúp việc huấn luyện ổn định và hiệu quả hơn trong khi đạt được kết quả tương đương hoặc tốt hơn so với RLHF truyền thống.
-
-## Bộ Dữ Liệu DPO
-
-Bộ dữ liệu cho DPO thường được tạo bằng cách gán nhãn các cặp phản hồi là được ưu tiên hoặc không được ưu tiên. Việc này có thể được thực hiện thủ công hoặc sử dụng các kỹ thuật lọc tự động. Dưới đây là cấu trúc mẫu của tập dữ liệu preference một lượt cho DPO:
-
-```
-| Prompt | Chosen | Rejected |
-|--------|--------|----------|
-| ...    | ...    | ...      |
-| ...    | ...    | ...      |
-| ...    | ...    | ...      |
-```
-
-Cột `Prompt` chứa chỉ thị dùng để tạo ra các phản hồi. Cột `Chosen` và `Rejected` chứa các phản hồi được ưu tiên và không được ưu tiên. Có nhiều biến thể của cấu trúc này, ví dụ, bao gồm cột `System Prompt` (chỉ thị hệ thống) hoặc cột `Input` chứa tài liệu tham khảo. Giá trị của `Chosen` và `Rejected` có thể được biểu diễn dưới dạng chuỗi cho hội thoại một lượt hoặc dưới dạng danh sách hội thoại.
-
-Bạn có thể tìm thấy bộ sưu tập các tập dữ liệu DPO trên Hugging Face [tại đây](https://huggingface.co/collections/argilla/preference-datasets-for-dpo-656f0ce6a00ad2dc33069478).
-
-## Triển Khai Với TRL
-
-Thư viện Transformers Reinforcement Learning (TRL) giúp việc triển khai DPO trở nên đơn giản. Các lớp `DPOConfig` và `DPOTrainer` tuân theo API của thư viện `transformers`.
-
-Đây là ví dụ cơ bản về cách thiết lập tinh chỉnh DPO:
-
-```python
-from trl import DPOConfig, DPOTrainer
-
-# Định nghĩa các tham số
-training_args = DPOConfig(
-    ...
-)
-
-# Khởi tạo trainer
-trainer = DPOTrainer(
-    model,
-    train_dataset=dataset,
-    tokenizer=tokenizer,
-    ...
-)
-
-# Huấn luyện mô hình
-trainer.train()
-```
-
-Chúng ta sẽ tìm hiểu thêm chi tiết về cách sử dụng các lớp `DPOConfig` và `DPOTrainer` trong [Hướng dẫn DPO](./notebooks/dpo_finetuning_example.ipynb).
-
-## Phương Pháp Tốt Nhất
-
-**Chất lượng dữ liệu là yếu tố quan trọng** cho việc triển khai DPO thành công. Tập dữ liệu ưu tiên nên bao gồm các ví dụ đa dạng bao quát các khía cạnh khác nhau của hành vi mong muốn. Hướng dẫn gán nhãn rõ ràng đảm bảo việc gán nhãn nhất quán cho các phản hồi được ưu tiên và không được ưu tiên. Bạn có thể cải thiện hiệu suất mô hình bằng cách nâng cao chất lượng tập dữ liệu ưu tiên. Ví dụ, bằng cách lọc các tập dữ liệu lớn hơn để chỉ bao gồm các ví dụ chất lượng cao hoặc các ví dụ liên quan đến trường hợp sử dụng của bạn.
-
-Trong quá trình huấn luyện, cần theo dõi kỹ sự hội tụ của hàm mất mát và đánh giá hiệu suất trên dữ liệu kiểm tra (held-out set). Tham số `beta` có thể cần điều chỉnh để cân bằng giữa việc học ưu tiên với việc duy trì các khả năng tổng quát của mô hình. Đánh giá thường xuyên trên các chỉ thị đa dạng giúp đảm bảo mô hình đang học các ưu tiên mong muốn mà không bị tình trạng quá khớp (overfitting).
-
-So sánh đầu ra của mô hình với mô hình tham chiếu để xác minh sự cải thiện trong tinh chỉnh ưu tiên. Kiểm tra trên nhiều chỉ thị khác nhau, bao gồm cả các trường hợp ngoại lệ (edge cases), giúp đảm bảo việc học ưu tiên mạnh mẽ hơn trong các tình huống khác nhau.
-
-## Các Bước Tiếp Theo
-
-⏩ Để có thể thực hành thực tế với DPO, hãy thử [Hướng dẫn DPO](./notebooks/dpo_finetuning_example.ipynb). Hướng dẫn thực hành này sẽ chỉ dẫn bạn cách triển khai tinh chỉnh ưu tiên với mô hình của riêng bạn, từ chuẩn bị dữ liệu đến huấn luyện và đánh giá.
-
-⏭️ Sau khi hoàn thành hướng dẫn, bạn có thể khám phá về [ORPO](./orpo.md) để tìm hiểu về một kỹ thuật tinh chỉnh ưu tiên khác.
\ No newline at end of file
diff --git a/vi/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb b/vi/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
deleted file mode 100644
index d519b6a9..00000000
--- a/vi/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
+++ /dev/null
@@ -1,5492 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tinh Chỉnh Tối Ưu Hóa Ưu Tiên Trực Tiếp (DPO)\n",
-    "\n",
-    "Bài tập này sẽ hướng dẫn bạn quá trình tinh chỉnh mô hình ngôn ngữ bằng phương pháp DPO. Chúng ta sẽ sử dụng mô hình đã được huấn luyện SFT trước đó `SmolLM2-135M-Instruct`, nên chúng ta có thể thực hiện tinh chỉnh DPO ngay. Bạn cũng có thể sử dụng mô hình bạn đã huấn luyện trong [1_instruction_tuning](../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb).\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "     <h2 style='margin: 0;color:blue'>Bài tập: Tinh chỉnh SmolLM2 với DPOTrainer</h2>\n",
-    "     <p>Lấy một tập dữ liệu từ Hugging Face hub và tinh chỉnh ưu tiên cho mô hình. </p> \n",
-    "     <p><b>Các bài tập</b></p>\n",
-    "     <p>🐢 Sử dụng bộ dữ liệu `trl-lib/ultrafeedback_binarized`</p>\n",
-    "     <p>🐕 Thử nghiệm với bộ dữ liệu `argilla/ultrafeedback-binarized-preferences`</p>\n",
-    "     <p>🦁 Chọn bộ dữ liệu liên quan đến trường hợp thực tế bạn quan tâm, hoặc sử dụng mô hình bạn đã huấn luyện trong \n",
-    "        <a href=\"../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb\">1_instruction_tuning</a></p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Cài đặt các thư viện cần thiết\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "# Đăng nhập vào Hugging Face\n",
-    "\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()\n",
-    "\n",
-    "# Để thuận tiện, bạn có thể tạo một biến môi trường chứa `token hub` của bạn dưới dạng HF_TOKEN"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "_zIBL8IssExG",
-    "outputId": "90d70bd2-30e7-49a4-d5bf-20d74deb6cec"
-   },
-   "outputs": [],
-   "source": [
-    "# Các thư viện cần thiết\n",
-    "\n",
-    "import torch\n",
-    "import os\n",
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "from datasets import load_dataset\n",
-    "from trl import DPOTrainer, DPOConfig"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "d8CvUgROUDw-"
-   },
-   "source": [
-    "## Chuẩn bị dữ liệu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "MCD77GZ60DOT"
-   },
-   "outputs": [],
-   "source": [
-    "# Tải tập dữ liệu\n",
-    "\n",
-    "# TODO: 🦁🐕 thay đổi tập dữ liệu theo lựa chọn của bạn\n",
-    "dataset = load_dataset(path=\"trl-lib/ultrafeedback_binarized\", split=\"train\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO: 🐕 Nếu bộ dữ liệu của bạn không được biểu diễn dưới dạng danh sách hội thoại\n",
-    "# bạn có thể sử dụng hàm `process_dataset` để chuyển đổi nó."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Chọn mô hình\n",
-    "\n",
-    "Chúng ta sẽ sử dụng mô hình `SmolLM2-135M-Instruct` đã được huấn luyện SFT, nên nó tương thích với DPO. Bạn cũng có thể sử dụng mô hình bạn đã huấn luyện trong [1_instruction_tuning](../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb).\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; width:80%; color:black'>\n",
-    "     <p>🦁 thay đổi đường dẫn mô hình thành đường dẫn hoặc repo id của mô hình bạn đã huấn luyện trong <a href=\"../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb\">1_instruction_tuning</a></p>\n",
-    "</div>\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO: 🦁 thay đổi mô hình thành đường dẫn/repo id của mô hình bạn đã huấn luyện\n",
-    "\n",
-    "model_name = \"HuggingFaceTB/SmolLM2-135M-Instruct\"\n",
-    "\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "# Mô hình để tinh chỉnh\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=model_name,\n",
-    "    torch_dtype=torch.float16,\n",
-    ").to(device)\n",
-    "model.config.use_cache = False\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
-    "tokenizer.pad_token = tokenizer.eos_token\n",
-    "\n",
-    "# Đặt tên cho bản tinh chỉnh để lưu &/ tải lên\n",
-    "finetune_name = \"SmolLM2-FT-DPO\"\n",
-    "finetune_tags = [\"smol-course\", \"module_2\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "DeT5eUK_UJgK"
-   },
-   "source": [
-    "## Tinh chỉnh mô hình với DPO"
-   ]
-  },
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-    "# Các tham số huấn luyện\n",
-    "training_args = DPOConfig(\n",
-    "    # Kích thước batch huấn luyện trên mỗi GPU\n",
-    "    per_device_train_batch_size=4,\n",
-    "    # Số bước cập nhật để tích lũy trước khi thực hiện backward/update\n",
-    "    # Kích thước batch hiệu quả = per_device_train_batch_size * gradient_accumulation_steps\n",
-    "    gradient_accumulation_steps=4,\n",
-    "    # Tiết kiệm bộ nhớ bằng cách không lưu các activations trong lượt chuyển tiếp\n",
-    "    # Thay vào đó tính toán lại trong lượt chuyển ngược\n",
-    "    gradient_checkpointing=True,\n",
-    "    # Learning rate cơ bản\n",
-    "    learning_rate=5e-5,\n",
-    "    # Kế hoạch learning rate - 'cosine' giảm dần LR theo đường cong cosine\n",
-    "    lr_scheduler_type=\"cosine\",\n",
-    "    # Tổng số bước huấn luyện\n",
-    "    max_steps=200,\n",
-    "    # Tắt lưu checkpoints trong quá trình huấn luyện\n",
-    "    save_strategy=\"no\",\n",
-    "    # Tần suất ghi logs\n",
-    "    logging_steps=1,\n",
-    "    # Thư mục lưu kết quả\n",
-    "    output_dir=\"smol_dpo_output\",\n",
-    "    # Số bước warmup learning rate\n",
-    "    warmup_steps=100,\n",
-    "    # Sử dụng bfloat16 để tăng tốc huấn luyện\n",
-    "    bf16=True,\n",
-    "    # Tắt logging wandb/tensorboard\n",
-    "    report_to=\"none\",\n",
-    "    # Giữ lại tất cả các cột trong dataset kể cả không sử dụng\n",
-    "    remove_unused_columns=False,\n",
-    "    # Bật MPS cho thiết bị Mac\n",
-    "    use_mps_device=device == \"mps\",\n",
-    "    # ID mô hình cho tải lên HuggingFace Hub\n",
-    "    hub_model_id=finetune_name,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer = DPOTrainer(\n",
-    "    # Mô hình cần huấn luyện\n",
-    "    model=model,\n",
-    "    # Cấu hình huấn luyện\n",
-    "    args=training_args,\n",
-    "    # Tập dữ liệu chứa các cặp phản hồi được ưu tiên/bị từ chối\n",
-    "    train_dataset=dataset,\n",
-    "    # Tokenizer để xử lý đầu vào\n",
-    "    processing_class=tokenizer,\n",
-    "    # Tham số temperature của DPO kiểm soát độ mạnh của mô hình ưu tiên\n",
-    "    # Giá trị thấp hơn (như 0.1) làm mô hình thận trọng hơn khi tuân theo ưu tiên\n",
-    "    beta=0.1,\n",
-    "    # Độ dài tối đa của chỉ thị đầu vào\n",
-    "    max_prompt_length=1024,\n",
-    "    # Độ dài tối đa kết hợp của chỉ thị + phản hồi\n",
-    "    max_length=1536,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Huấn luyện mô hình\n",
-    "trainer.train()\n",
-    "\n",
-    "# Lưu mô hình\n",
-    "trainer.save_model(f\"./{finetune_name}\")\n",
-    "\n",
-    "# Lưu lên huggingface hub nếu đã đăng nhập (HF_TOKEN đã được thiết lập)\n",
-    "if os.getenv(\"HF_TOKEN\"):\n",
-    "    trainer.push_to_hub(tags=finetune_tags)"
-   ]
-  },
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-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 💐 Bạn đã hoàn thành!\n",
-    "\n",
-    "Bài tập này đã hướng dẫn bạn từng bước cách tinh chỉnh mô hình `HuggingFaceTB/SmolLM2-135M` sử dụng `DPOTrainer`. Theo các bước này, bạn có thể điều chỉnh mô hình để thực hiện các tác vụ cụ thể một cách hiệu quả hơn. Nếu bạn muốn tiếp tục học khóa học này, đây là một số bước bạn có thể thử:\n",
-    "\n",
-    "- Thử bài tập này ở các mức độ khó hơn\n",
-    "- Đánh giá PR của bạn học khác\n",
-    "- Cải thiện khóa học thông qua Issue hoặc PR"
-   ]
-  }
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diff --git a/vi/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb b/vi/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
deleted file mode 100644
index 86959e17..00000000
--- a/vi/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
+++ /dev/null
@@ -1,8635 +0,0 @@
-{
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-   "metadata": {},
-   "source": [
-    "# Tối Ưu Hóa Ưu Tiên Theo Tỷ Lệ Odds (Odds Ratio Preference Optimization - ORPO)\n",
-    "\n",
-    "Bài tập này sẽ hướng dẫn bạn quy trình tinh chỉnh mô hình ngôn ngữ sử dụng phương pháp ORPO. Chúng ta sẽ sử dụng mô hình `HuggingFaceTB/SmolLM2-135M` bản gốc (chưa được huấn luyện SFT), nên nó không tương thích với DPO. Điều này có nghĩa là bạn không thể sử dụng mô hình bạn đã huấn luyện trong [1_instruction_tuning](../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb).\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "     <h2 style='margin: 0;color:blue'>Bài tập: Tinh chỉnh SmolLM2 với DPOTrainer</h2>\n",
-    "     <p>Lấy một tập bộ liệu từ Hugging Face hub và điều chỉnh mô hình trên bộ dữ liệu đó. </p> \n",
-    "     <p><b>Các bài tập</b></p>\n",
-    "     <p>🐢 Sử dụng bộ dữ liệu `trl-lib/ultrafeedback_binarized`</p>\n",
-    "     <p>🐕 Thử nghiệm với bộ dữ liệu `argilla/ultrafeedback-binarized-preferences`</p>\n",
-    "     <p>🦁 Thử nghiệm với một phần của bộ dữ liệu `orpo-dpo-mix-40k` của mlabonne</p>\n",
-    "</div>\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "EwNxh0pCQhfI",
-    "outputId": "e4c288e1-2fa3-4717-d9dd-ae3ec0b91e3b"
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/ben/code/smol-course/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Các thư viện cần thiết\n",
-    "\n",
-    "import torch\n",
-    "import os\n",
-    "from datasets import load_dataset\n",
-    "from transformers import (\n",
-    "    AutoModelForCausalLM,\n",
-    "    AutoTokenizer,\n",
-    ")\n",
-    "from trl import ORPOConfig, ORPOTrainer, setup_chat_format\n",
-    "\n",
-    "# Authenticate to Hugging Face\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Chuẩn bị dữ liệu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Generating train split: 100%|██████████| 62135/62135 [00:00<00:00, 450465.27 examples/s]\n",
-      "Generating test split: 100%|██████████| 1000/1000 [00:00<00:00, 382866.64 examples/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Tải tập dữ liệu\n",
-    "\n",
-    "# TODO: 🦁🐕 thay đổi tập dữ liệu theo lựa chọn của bạn\n",
-    "dataset = load_dataset(path=\"trl-lib/ultrafeedback_binarized\", split=\"train\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO: 🐕 Nếu bộ dữ liệu của bạn không được biểu diễn dưới dạng danh sách hội thoại,\n",
-    "# bạn có thể sử dụng hàm `process_dataset` để chuyển đổi nó."
-   ]
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-   "source": [
-    "## Chọn mô hình\n",
-    "\n",
-    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
-    "\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "# Mô hình để tinh chỉnh\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=model_name,\n",
-    "    torch_dtype=torch.float16,\n",
-    ").to(device)\n",
-    "model.config.use_cache = False\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
-    "tokenizer.pad_token = tokenizer.eos_token\n",
-    "\n",
-    "# Đặt tên cho bản tinh chỉnh để lưu &/ tải lên\n",
-    "finetune_name = \"SmolLM2-FT-ORPO\"\n",
-    "finetune_tags = [\"smol-course\", \"module_2\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Huấn luyện mô hình với ORPO"
-   ]
-  },
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-   "outputs": [],
-   "source": [
-    "orpo_args = ORPOConfig(\n",
-    "    # Learning rate nhỏ để tránh catastrophic forgetting\n",
-    "    # Đọc thêm về catastrophic forgetting: https://blog.vinbigdata.org/robot-co-the-hoc-suot-doi-khong/\n",
-    "    learning_rate=8e-6,\n",
-    "    # Giảm learning rate tuyến tính trong quá trình huấn luyện\n",
-    "    lr_scheduler_type=\"linear\",\n",
-    "    # Độ dài tối đa kết hợp của chỉ thị + câu trả lời\n",
-    "    max_length=1024,\n",
-    "    # Độ dài tối đa cho input prompts\n",
-    "    max_prompt_length=512,\n",
-    "    # Kiểm soát trọng số của hàm mất mát odds ratio (λ trong paper)\n",
-    "    beta=0.1,\n",
-    "    # Kích thước batch cho huấn luyện\n",
-    "    per_device_train_batch_size=2,\n",
-    "    per_device_eval_batch_size=2,\n",
-    "    # Giúp ổn định quá trình huấn luyện bằng cách tích luỹ gradient trước khi cập nhật\n",
-    "    gradient_accumulation_steps=4,\n",
-    "    # Optimizer tiết kiệm bộ nhớ cho CUDA, sử dụng adamw_torch cho CPU/MPS\n",
-    "    optim=\"paged_adamw_8bit\" if device == \"cuda\" else \"adamw_torch\",\n",
-    "    # Số epoch huấn luyện\n",
-    "    num_train_epochs=1,\n",
-    "    # Khi nào chạy đánh giá\n",
-    "    evaluation_strategy=\"steps\",\n",
-    "    # Đánh giá sau mỗi 20% huấn luyện\n",
-    "    eval_steps=0.2,\n",
-    "    # Ghi log metrics mỗi step\n",
-    "    logging_steps=1,\n",
-    "    # Tăng learning rate dần dần\n",
-    "    warmup_steps=10,\n",
-    "    # Tắt logging bên ngoài\n",
-    "    report_to=\"none\",\n",
-    "    # Nơi lưu model/checkpoints\n",
-    "    output_dir=\"./results/\",\n",
-    "    # Bật MPS (Metal Performance Shaders) nếu có\n",
-    "    use_mps_device=device == \"mps\",\n",
-    "    hub_model_id=finetune_name,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer = ORPOTrainer(\n",
-    "    model=model,\n",
-    "    args=orpo_args,\n",
-    "    train_dataset=dataset[\"train\"],\n",
-    "    eval_dataset=dataset[\"test\"],\n",
-    "    processing_class=tokenizer,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.train()  # Huấn luyện mô hình\n",
-    "\n",
-    "# Lưu mô hình\n",
-    "trainer.save_model(f\"./{finetune_name}\")\n",
-    "\n",
-    "# Lưu lên huggingface hub nếu đã đăng nhập (HF_TOKEN đã được thiết lập)\n",
-    "if os.getenv(\"HF_TOKEN\"):\n",
-    "    trainer.push_to_hub(tags=finetune_tags)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 💐 Bạn đã hoàn thành!\n",
-    "\n",
-    "Bài tập này đã hướng dẫn bạn từng bước cách tinh chỉnh mô hình `HuggingFaceTB/SmolLM2-135M` sử dụng `ORPOTrainer`. Theo các bước này, bạn có thể điều chỉnh mô hình để thực hiện các tác vụ cụ thể một cách hiệu quả hơn. Nếu bạn muốn tiếp tục học khóa học này, đây là một số bước bạn có thể thử:\n",
-    "\n",
-    "- Thử bài tập này ở các mức độ khó hơn\n",
-    "- Đánh giá PR của bạn học khác\n",
-    "- Cải thiện khóa học thông qua Issue hoặc PR"
-   ]
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "gpuType": "L4",
-   "machine_shape": "hm",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "name": "python3"
-  },
-  "language_info": {
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diff --git a/vi/2_preference_alignment/orpo.md b/vi/2_preference_alignment/orpo.md
deleted file mode 100644
index 8c3ef80c..00000000
--- a/vi/2_preference_alignment/orpo.md
+++ /dev/null
@@ -1,89 +0,0 @@
-# Tối Ưu Hóa Ưu Tiên Theo Tỷ Lệ Odds (Odds Ratio Preference Optimization - ORPO)
-
-ORPO là một kỹ thuật tinh chỉnh mới kết hợp cả quá trình *tinh chỉnh theo chỉ thị* và *tinh chỉnh ưu tiên* thành một quy trình thống nhất. Cách tiếp cận kết hợp này mang lại những lợi thế về hiệu quả và hiệu suất so với các phương pháp truyền thống như RLHF hoặc DPO.
-
-## Tìm Hiểu Về ORPO
-
-Phương pháp tinh chỉnh như DPO thường liên quan đến hai bước riêng biệt:
-1) học có giám sát để thích ứng mô hình với một lĩnh vực và định dạng
-2) sau đó là tinh chỉnh ưu tiên để phù hợp với ý muốn của con người
-
-Trong khi SFT hiệu quả trong việc thích ứng mô hình với các lĩnh vực mục tiêu, nó có thể vô tình làm tăng xác suất tạo ra cả phản hồi mong muốn và không mong muốn. ORPO giải quyết hạn chế này bằng cách tích hợp cả hai bước vào một quy trình duy nhất, như minh họa trong hình so sánh dưới đây:
-
-![So sánh các kỹ thuật tinh chỉnh mô hình khác nhau](https://argilla.io/images/blog/mantisnlp-rlhf/part-8-alignments.png)
-*So sánh các kỹ thuật tinh chỉnh mô hình khác nhau*
-
-## ORPO Hoạt Động Như Thế Nào
-
-Quy trình huấn luyện sử dụng một tập dữ liệu ưu tiên tương tự như chúng ta đã sử dụng cho DPO, trong đó mỗi mẫu huấn luyện chứa một chỉ thị đầu vào cùng với hai phản hồi: một được ưu tiên và một bị loại bỏ. Khác với các phương pháp tinh chỉnh khác yêu cầu các giai đoạn riêng biệt và mô hình tham chiếu, ORPO tích hợp trực tiếp tinh chỉnh ưu tiên vào quá trình học có giám sát. Cách tiếp cận thống nhất này không cần mô hình tham chiếu, hiệu quả hơn về mặt tính toán và bộ nhớ với ít FLOPs hơn.
-
-ORPO tạo ra một mục tiêu mới bằng cách kết hợp hai thành phần chính:
-
-1. **SFT Loss**: Hàm mất mát *negative log-likelihood* tiêu chuẩn được sử dụng trong việc mô hình hóa ngôn ngữ, tối đa hóa xác suất tạo ra các *token tham chiếu*. Điều này giúp duy trì khả năng ngôn ngữ tổng quát của mô hình.
-
-2. **Odds Ratio Loss**:  Một hàm mất mát mới giúp phạt các phản hồi không mong muốn trong khi thưởng cho các phản hồi được ưu tiên. Hàm mất mát này sử dụng tỷ lệ odds để so sánh hiệu quả giữa các phản hồi được ưa thích và không ưa thích ở cấp độ *token*.
-
-Cùng nhau, các thành phần này hướng dẫn mô hình thích ứng với các phản hồi mong muốn cho lĩnh vực cụ thể trong khi tích cực ngăn chặn các phản hồi từ tập các phản hồi bị từ chối. Cơ chế tỷ lệ odds cung cấp một cách tự nhiên để đo lường và tối ưu hóa ưu tiên của mô hình giữa các đầu ra đã chọn và bị từ chối. Nếu bạn muốn tìm hiểu sâu về phần toán học, bạn có thể đọc [bài báo ORPO](https://arxiv.org/abs/2403.07691). Nếu bạn muốn tìm hiểu về ORPO từ góc độ triển khai, bạn nên xem cách tính toán hàm mất mát cho ORPO trong [thư viện TRL](https://github.com/huggingface/trl/blob/b02189aaa538f3a95f6abb0ab46c0a971bfde57e/trl/trainer/orpo_trainer.py#L660).
-
-## Hiệu Suất và Kết Quả
-
-ORPO đã cho thấy các kết quả ấn tượng trên nhiều bài kiểm tra. Trên `MT-Bench`, phương pháp này giúp mô hình sau tinh chỉnh đạt điểm số cạnh tranh trên các danh mục khác nhau:
-
-![Kết quả MT-Bench](https://argilla.io/images/blog/mantisnlp-rlhf/part-8-mtbench.png)
-*Kết quả MT-Bench theo danh mục cho các mô hình Mistral-ORPO*
-
-So với các phương pháp tinh chỉnh khác, ORPO thể hiện hiệu suất vượt trội trên AlpacaEval 2.0:
-
-![Kết quả AlpacaEval](https://argilla.io/images/blog/mantisnlp-rlhf/part-8-winrate.png)
-*Điểm số AlpacaEval 2.0 trên các phương pháp tinh chỉnh khác nhau*
-
-So với việc kết hợp cả SFT và DPO, ORPO giảm yêu cầu tính toán bằng cách loại bỏ nhu cầu về mô hình tham chiếu và giảm một nửa số lần chuyển tiếp (forward pass) cho mỗi batch. Ngoài ra, quy trình huấn luyện ổn định hơn trên các kích thước mô hình và tập dữ liệu khác nhau, yêu cầu ít siêu tham số cần tinh chỉnh hơn. Về mặt hiệu suất, ORPO ngang bằng với các mô hình lớn hơn trong khi cho thấy sự tinh chỉnh tốt hơn với ý muốn của con người.
-
-## Triển Khai 
-
-Triển khai thành công ORPO phụ thuộc nhiều vào **dữ liệu ưu tiên chất lượng cao**. Dữ liệu huấn luyện nên tuân theo các hướng dẫn gán nhãn rõ ràng và cung cấp sự đại diện cân bằng của các phản hồi được ưu tiên và bị từ chối trong các tình huống đa dạng. 
-
-### Triển Khai với TRL
-
-ORPO có thể được triển khai sử dụng thư viện TRL. Đây là một ví dụ cơ bản:
-
-```python
-from trl import ORPOConfig, ORPOTrainer
-
-# Cấu hình tinh chỉnh ORPO
-orpo_config = ORPOConfig(
-    learning_rate=1e-5,
-    per_device_train_batch_size=4,
-    gradient_accumulation_steps=4,
-    max_steps=1000,
-    orpo_alpha=1.0,  # Kiểm soát độ mạnh của tối ưu hóa ưu tiên
-    orpo_beta=0.1,   # Tham số cho sự ngẫu nhiên (temperature) trong tính tỷ lệ odds
-)
-
-# Khởi tạo trainer
-trainer = ORPOTrainer(
-    model=model,
-    args=orpo_config,
-    train_dataset=dataset,
-    tokenizer=tokenizer,
-)
-
-# Huấn luyện mô hình
-trainer.train()
-```
-
-Các tham số chính cần xem xét:
-
-- `orpo_alpha`: Kiểm soát độ mạnh của thuật toán tối ưu hóa ưu tiên
-- `orpo_beta`: Tham số cho sự ngẫu nhiên (temperature) trong phép tính tỷ lệ odds
-- `learning_rate`: Nên tương đối nhỏ để tránh catastrophic forgetting
-- `gradient_accumulation_steps`: Giúp ổn định quá trình huấn luyện
-
-## Các Bước Tiếp Theo
-
-⏩ Bạn có thể làm theo hướng dẫn trong [Hướng dẫn ORPO](./notebooks/orpo_tutorial.ipynb) để triển khai cách tinh chỉnh ưu tiên này.
-
-## Resources
-- [Bài báo nghiên cứu về ORPO](https://arxiv.org/abs/2403.07691)
-- [Tài liệu về thư viện TRL](https://huggingface.co/docs/trl/index)
-- [Hướng dẫn của Argilla](https://argilla.io/blog/mantisnlp-rlhf-part-8/) 
\ No newline at end of file
diff --git a/vi/3_parameter_efficient_finetuning/README.md b/vi/3_parameter_efficient_finetuning/README.md
deleted file mode 100644
index 447097e3..00000000
--- a/vi/3_parameter_efficient_finetuning/README.md
+++ /dev/null
@@ -1,38 +0,0 @@
-# Tinh chỉnh hiệu quả tham số (Parameter-Efficient Fine-Tuning - PEFT)
-
-Khi các mô hình ngôn ngữ ngày càng lớn hơn, việc tinh chỉnh truyền thống trở nên ngày càng thách thức. Việc tinh chỉnh đầy đủ một mô hình với 1.7B tham số đòi hỏi bộ nhớ GPU lớn, việc lưu trữ các bản sao mô hình riêng biệt tốn kém, và có nguy cơ làm mất đi các khả năng ban đầu của mô hình. Các phương pháp tinh chỉnh hiệu quả tham số (PEFT) giải quyết những thách thức này bằng cách chỉ điều chỉnh một tập nhỏ các tham số mô hình trong khi giữ nguyên phần lớn mô hình.
-
-Tinh chỉnh truyền thống cập nhật tất cả các tham số mô hình trong quá trình huấn luyện, điều này trở nên không khả thi với các mô hình lớn. Các phương pháp PEFT giới thiệu cách tiếp cận để điều chỉnh mô hình sử dụng ít tham số có thể huấn luyện hơn - thường ít hơn 1% kích thước mô hình gốc. Việc giảm đáng kể số lượng tham số có thể huấn luyện cho phép:
-
-- Tinh chỉnh trên phần cứng tiêu dùng với bộ nhớ GPU hạn chế 
-- Lưu trữ nhiều phiên bản điều chỉnh (adapters) cho từng tác vụ một cách hiệu quả
-- Khả năng tổng quát hóa tốt hơn trong các trường hợp dữ liệu ít
-- Chu kỳ huấn luyện và thử nghiệm nhanh hơn
-
-## Các phương pháp hiện có
-
-Trong chương này, chúng ta sẽ tìm hiểu hai phương pháp PEFT phổ biến:
-
-### 1️⃣ Phương Pháp LoRA (Low-Rank Adaptation)
-
-LoRA đã nổi lên như phương pháp PEFT được áp dụng rộng rãi nhất, cung cấp giải pháp hoàn hảo cho việc điều chỉnh mô hình hiệu quả mà không tốn nhiều tài nguyên tính toán. Thay vì sửa đổi toàn bộ mô hình, **LoRA đưa các ma trận có thể huấn luyện vào các lớp attention của mô hình.** Cách tiếp cận này thường giảm các tham số có thể huấn luyện khoảng 90% trong khi vẫn duy trì hiệu suất tương đương với tinh chỉnh đầy đủ. Chúng ta sẽ khám phá LoRA trong phần [LoRA (Low-Rank Adaptation)](./lora_adapters.md).
-
-### 2️⃣ Phương Pháp Điều Chỉnh Chỉ Thị (Prompt Tuning)
-
-Prompt tuning cung cấp cách tiếp cận **thậm chí nhẹ hơn** bằng cách **thêm các token có thể huấn luyện vào đầu vào** thay vì sửa đổi trọng số mô hình. Prompt tuning ít phổ biến hơn LoRA, nhưng có thể là kỹ thuật hữu ích để nhanh chóng điều chỉnh mô hình cho các tác vụ hoặc lĩnh vực mới. Chúng ta sẽ khám phá prompt tuning trong phần [Prompt Tuning](./prompt_tuning.md).
-
-## Notebooks bài tập
-
-| Tiêu đề | Mô tả | Bài tập | Link | Colab |
-|---------|--------|---------|------|-------|
-| Tinh chỉnh LoRA | Học cách tinh chỉnh mô hình sử dụng LoRA adapters | 🐢 Huấn luyện mô hình sử dụng LoRA<br>🐕 Thử nghiệm với các giá trị rank khác nhau<br>🦁 So sánh hiệu suất với tinh chỉnh đầy đủ | [Notebook](./notebooks/finetune_sft_peft.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-| Tải LoRA Adapters | Học cách tải và sử dụng LoRA adapters đã huấn luyện | 🐢 Tải adapters đã huấn luyện trước<br>🐕 Gộp adapters với mô hình cơ sở<br>🦁 Chuyển đổi giữa nhiều adapters | [Notebook](./notebooks/load_lora_adapter.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-
-## Tài liệu tham khảo
-- [Tài liệu PEFT](https://huggingface.co/docs/peft)
-- [Bài báo nghiên cứu LoRA](https://arxiv.org/abs/2106.09685)
-- [Bài báo nghiên cứu QLoRA](https://arxiv.org/abs/2305.14314)
-- [Bài báo nghiên cứu Prompt Tuning](https://arxiv.org/abs/2104.08691)
-- [Hướng dẫn sử dụng PEFT của Hugging Face](https://huggingface.co/blog/peft)
-- [Cách Tinh chỉnh LLM với Hugging Face](https://www.philschmid.de/fine-tune-llms-in-2024-with-trl)
-- [Thư viện TRL](https://huggingface.co/docs/trl/index)
\ No newline at end of file
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GIT binary patch
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HcmV?d00001

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diff --git a/vi/3_parameter_efficient_finetuning/lora_adapters.md b/vi/3_parameter_efficient_finetuning/lora_adapters.md
deleted file mode 100644
index d8fdafe4..00000000
--- a/vi/3_parameter_efficient_finetuning/lora_adapters.md
+++ /dev/null
@@ -1,124 +0,0 @@
-# Phương Pháp LoRA (Low-Rank Adaptation)
-
-LoRA đã trở thành phương pháp PEFT được sử dụng rộng rãi nhất. Nó hoạt động bằng cách thêm các ma trận phân rã hạng thấp (small rank decomposition matrices) vào các trọng số attention, điều này dẫn đến việc giảm khoảng 90% số lượng tham số có thể huấn luyện.
-
-## Tìm Hiểu Về LoRA
-
-LoRA (Low-Rank Adaptation) là một kỹ thuật tinh chỉnh hiệu quả tham số, đóng băng các trọng số của mô hình đã huấn luyện trước và đưa thêm các ma trận phân rã hạng (rank decomposition matrices) có thể huấn luyện vào các lớp của mô hình. Thay vì huấn luyện tất cả các tham số mô hình trong quá trình tinh chỉnh, LoRA phân rã việc cập nhật trọng số thành các ma trận nhỏ hơn thông qua phân rã hạng thấp (low-rank decomposition), giảm đáng kể số lượng tham số có thể huấn luyện trong khi vẫn duy trì hiệu suất mô hình. Ví dụ, khi áp dụng cho GPT-3 175B, LoRA giảm số lượng tham số có thể huấn luyện xuống 10.000 lần và yêu cầu bộ nhớ GPU giảm 3 lần so với tinh chỉnh đầy đủ. Bạn có thể đọc thêm về LoRA trong [bài báo nghiên cứu LoRA](https://arxiv.org/pdf/2106.09685).
-
-LoRA hoạt động bằng cách thêm các cặp ma trận phân rã hạng vào các lớp transformer, thường tập trung vào các trọng số attention. Trong quá trình suy luận, các trọng số adapter có thể được gộp với mô hình cơ sở, không gây thêm độ trễ. LoRA đặc biệt hữu ích cho việc điều chỉnh các mô hình ngôn ngữ lớn cho các tác vụ hoặc lĩnh vực cụ thể trong khi không yêu cầu nhiều tài nguyên tính toán.
-
-## Lắp Các LoRA Adapters Vào Mô Hình Ngôn Ngữ
-
-Adapters có thể được lắp vào một mô hình đã huấn luyện trước với hàm `load_adapter()`, điều này hữu ích khi thử nghiệm các adapters khác nhau mà trọng số không được gộp. Đặt trọng số adapter đang hoạt động bằng hàm `set_adapter()`. Để trở về mô hình cơ sở, bạn có thể sử dụng `unload()` để gỡ tất cả các module LoRA. Điều này giúp dễ dàng chuyển đổi giữa các trọng số cho từng tác vụ cụ thể.
-
-```python
-from transformers import AutoModelForCausalLM
-from peft import PeftModel
-
-base_model = AutoModelForCausalLM.from_pretrained("<base_model_name>")
-peft_model_id = "<peft_adapter_id>"
-model = PeftModel.from_pretrained(base_model, peft_model_id)
-```
-
-![lora_load_adapter](./images/lora_adapter.png)
-
-## Gộp LoRA Adapters
-
-Sau khi huấn luyện với LoRA, bạn có thể muốn gộp các trọng số adapter trở lại mô hình cơ sở để triển khai dễ dàng hơn. Điều này tạo ra một mô hình duy nhất với các trọng số đã kết hợp, loại bỏ nhu cầu tải adapters riêng biệt trong quá trình suy luận.
-
-Quá trình gộp đòi hỏi chú ý đến quản lý bộ nhớ và độ chính xác. Vì bạn sẽ cần tải cả mô hình cơ sở và trọng số adapter cùng lúc, hãy đảm bảo có đủ bộ nhớ GPU/CPU. Sử dụng `device_map="auto"` trong `transformers` sẽ giúp tự động quản lý bộ nhớ. Duy trì độ chính xác nhất quán (ví dụ: float16) trong suốt quá trình, phù hợp với độ chính xác được sử dụng trong quá trình huấn luyện và lưu mô hình đã gộp trong cùng một định dạng để triển khai. Trước khi triển khai, luôn xác thực mô hình đã gộp bằng cách so sánh đầu ra và các chỉ số hiệu suất với phiên bản dựa trên adapter.
-
-Adapters cũng thuận tiện cho việc chuyển đổi giữa các tác vụ hoặc lĩnh vực khác nhau. Bạn có thể tải mô hình cơ sở và trọng số adapter riêng biệt. Điều này cho phép chuyển đổi nhanh chóng giữa các trọng số cho từng tác vụ cụ thể.
-
-## Hướng dẫn triển khai
-
-Thư mục `notebooks/` chứa các hướng dẫn thực hành và bài tập để triển khai các phương pháp PEFT khác nhau. Bắt đầu với `load_lora_adapter_example.ipynb` để có giới thiệu cơ bản, sau đó khám phá `lora_finetuning.ipynb` để xem xét chi tiết hơn về cách tinh chỉnh một mô hình với LoRA và SFT.
-
-Khi triển khai các phương pháp PEFT, hãy bắt đầu với các giá trị hạng nhỏ (4-8) cho LoRA và theo dõi mất mát trong quá trình huấn luyện. Sử dụng tập validation để tránh overfitting và so sánh kết quả với các baseline tinh chỉnh đầy đủ khi có thể. Hiệu quả của các phương pháp khác nhau có thể thay đổi theo tác vụ, vì vậy thử nghiệm là chìa khóa.
-
-## OLoRA
-
-[OLoRA](https://arxiv.org/abs/2406.01775) sử dụng phân rã QR để khởi tạo các adapter LoRA. OLoRA dịch chuyển các trọng số cơ sở của mô hình bằng một hệ số của phân rã QR của chúng, tức là nó thay đổi trọng số trước khi thực hiện bất kỳ huấn luyện nào trên chúng. Cách tiếp cận này cải thiện đáng kể tính ổn định, tăng tốc độ hội tụ và cuối cùng đạt được hiệu suất vượt trội.
-
-## Sử dụng TRL với PEFT
-
-Các phương pháp PEFT có thể được kết hợp với thư viện TRL để tinh chỉnh hiệu quả. Sự tích hợp này đặc biệt hữu ích cho RLHF (Reinforcement Learning from Human Feedback) vì nó giảm yêu cầu bộ nhớ.
-
-```python
-from peft import LoraConfig
-from transformers import AutoModelForCausalLM
-
-# Tải mô hình với cấu hình PEFT
-lora_config = LoraConfig(
-    r=16,
-    lora_alpha=32,
-    lora_dropout=0.05,
-    bias="none",
-    task_type="CAUSAL_LM"
-)
-
-# Tải mô hình trên thiết bị cụ thể
-model = AutoModelForCausalLM.from_pretrained(
-    "your-model-name",
-    load_in_8bit=True,  # Tùy chọn: sử dụng độ chính xác 8-bit
-    device_map="auto",
-    peft_config=lora_config
-)
-```
-
-Ở trên, chúng ta đã sử dụng `device_map="auto"` để tự động gán mô hình cho thiết bị phù hợp. Bạn cũng có thể gán thủ công mô hình cho một thiết bị cụ thể bằng cách sử dụng `device_map={"": device_index}`. Bạn cũng có thể mở rộng việc huấn luyện trên nhiều GPU trong khi vẫn giữ việc sử dụng bộ nhớ hiệu quả.
-
-## Triển khai gộp cơ bản
-
-Sau khi huấn luyện một adapter LoRA, bạn có thể gộp trọng số adapter trở lại mô hình cơ sở. Đây là cách thực hiện:
-
-```python
-import torch
-from transformers import AutoModelForCausalLM
-from peft import PeftModel
-
-# 1. Tải mô hình cơ sở
-base_model = AutoModelForCausalLM.from_pretrained(
-    "base_model_name",
-    torch_dtype=torch.float16,
-    device_map="auto"
-)
-
-# 2. Tải LoRA adapter
-peft_model = PeftModel.from_pretrained(
-    base_model,
-    "path/to/adapter",
-    torch_dtype=torch.float16
-)
-
-# 3. Gộp trọng số adapter với mô hình cơ sở
-try:
-    merged_model = peft_model.merge_and_unload()
-except RuntimeError as e:
-    print(f"Gộp thất bại: {e}")
-    # Triển khai chiến lược dự phòng hoặc tối ưu hóa bộ nhớ
-
-# 4. Lưu mô hình đã gộp
-merged_model.save_pretrained("path/to/save/merged_model")
-```
-
-Nếu bạn gặp sự khác biệt về kích thước trong mô hình đã lưu, hãy đảm bảo bạn cũng lưu tokenizer:
-
-```python
-# Lưu cả mô hình và tokenizer
-tokenizer = AutoTokenizer.from_pretrained("base_model_name")
-merged_model.save_pretrained("path/to/save/merged_model")
-tokenizer.save_pretrained("path/to/save/merged_model")
-```
-
-## Các bước tiếp theo
-
-⏩ Chuyển sang [Hướng dẫn Prompt Tuning](prompt_tuning.md) để tìm hiểu cách tinh chỉnh một mô hình bằng prompt tuning.
-⏩ Chuyển sang [Hướng dẫn lắp LoRA Adapters](./notebooks/load_lora_adapter.ipynb) để tìm hiểu cách lắp LoRA adapters.
-
-# Tài liệu tham khảo
-
-- [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/pdf/2106.09685)
-- [Tài liệu PEFT](https://huggingface.co/docs/peft)
-- [Bài viết blog của Hugging Face về PEFT](https://huggingface.co/blog/peft)
\ No newline at end of file
diff --git a/vi/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb b/vi/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
deleted file mode 100644
index f53c614a..00000000
--- a/vi/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
+++ /dev/null
@@ -1,510 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "z-6LLOPZouLg"
-   },
-   "source": [
-    "# Cách tinh chỉnh LLM với LoRA Adapters sử dụng Hugging Face TRL\n",
-    "\n",
-    "Trong Notebook này bạn sẽ được học cách tinh chỉnh hiệu quả các mô hình ngôn ngữ lớn sử dụng LoRA (Low-Rank Adaptation) adapters. LoRA là một kỹ thuật tinh chỉnh tham số hiệu quả:\n",
-    "- Đóng băng các trọng số mô hình đã huấn luyện trước\n",
-    "- Thêm các ma trận phân rã hạng nhỏ có thể huấn luyện vào các lớp attention  \n",
-    "- Thường giảm khoảng 90% tham số có thể huấn luyện\n",
-    "- Duy trì hiệu suất mô hình trong khi sử dụng bộ nhớ hiệu quả\n",
-    "\n",
-    "Chúng ta sẽ tìm hiểu:  \n",
-    "1. Cài đặt môi trường phát triển và cấu hình LoRA\n",
-    "2. Tạo và chuẩn bị dữ liệu để huấn luyện adapter \n",
-    "3. Tinh chỉnh sử dụng `trl` và `SFTTrainer` với LoRA adapters\n",
-    "4. Kiểm tra mô hình và gộp adapters (tùy chọn)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "fXqd9BXgouLi"
-   },
-   "source": [
-    "## 1. Cài đặt môi trường phát triển\n",
-    "\n",
-    "Bước đầu tiên là cài đặt các thư viện Hugging Face và PyTorch, bao gồm `trl, transformers và datasets`. Nếu bạn chưa nghe nói về `trl`, đừng lo lắng. Đó là một thư viện mới trên nền tảng transformers và datasets, giúp việc tinh chỉnh các mô hình ngôn ngữ lớn (LLM) trở nên dễ dàng hơn."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "tKvGVxImouLi"
-   },
-   "outputs": [],
-   "source": [
-    "# Cài đặt các yêu cầu trong Google Colab\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "# Xác thực với Hugging Face\n",
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()\n",
-    "\n",
-    "# để thuận tiện bạn có thể tạo một biến môi trường chứa token hub của bạn là HF_TOKEN"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "XHUzfwpKouLk"
-   },
-   "source": [
-    "## 2. Tải dữ liệu "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "id": "z4p6Bvo7ouLk"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['full_topic', 'messages'],\n",
-       "        num_rows: 2260\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['full_topic', 'messages'],\n",
-       "        num_rows: 119\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Tải một tập dữ liệu mẫu\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "# TODO: xác định dataset và config của bạn sử dụng các tham số path và name\n",
-    "dataset = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")\n",
-    "dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "9TOhJdtsouLk"
-   },
-   "source": [
-    "## 3. Tinh chỉnh LLM sử dụng `trl` và `SFTTrainer` với LoRA\n",
-    "\n",
-    "[SFTTrainer](https://huggingface.co/docs/trl/sft_trainer) từ `trl` cung cấp tích hợp với LoRA adapters thông qua thư viện [PEFT](https://huggingface.co/docs/peft/en/index). Những lợi thế chính của cài đặt này bao gồm:\n",
-    "\n",
-    "1. **Hiệu quả bộ nhớ**:\n",
-    "   - Chỉ các tham số adapter được lưu trữ trong bộ nhớ GPU \n",
-    "   - Trọng số mô hình cơ sở vẫn đóng băng và có thể được tải với độ chính xác thấp hơn\n",
-    "   - Cho phép tinh chỉnh các mô hình lớn trên GPU tiêu dùng\n",
-    "\n",
-    "2. **Tính năng huấn luyện**:\n",
-    "   - Tích hợp PEFT/LoRA sẵn có với cài đặt tối thiểu\n",
-    "   - Hỗ trợ QLoRA (LoRA lượng tử hóa) cho hiệu quả bộ nhớ tốt hơn\n",
-    "\n",
-    "3. **Quản lý Adapter**: \n",
-    "   - Lưu trọng số adapter trong quá trình checkpoint\n",
-    "   - Tính năng gộp adapters trở lại mô hình cơ sở\n",
-    "\n",
-    "Chúng ta sẽ sử dụng LoRA trong ví dụ của mình, **kết hợp LoRA với lượng tử hóa 4-bit** để giảm thêm việc sử dụng bộ nhớ mà không ảnh hưởng đến hiệu suất. Cài đặt chỉ yêu cầu một vài bước cấu hình:\n",
-    "1. Xác định cấu hình LoRA (rank, alpha, dropout)\n",
-    "2. Tạo SFTTrainer với cấu hình PEFT \n",
-    "3. Huấn luyện và lưu trọng số adapter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Các thư viện cần thiết \n",
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-    "from datasets import load_dataset\n",
-    "from trl import SFTConfig, SFTTrainer, setup_chat_format\n",
-    "import torch\n",
-    "\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "# Tải mô hình và tokenizer\n",
-    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
-    "\n",
-    "model = AutoModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=model_name\n",
-    ").to(device)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
-    "\n",
-    "# Thiết lập định dạng chat\n",
-    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n",
-    "\n",
-    "# Đặt tên cho bản tinh chỉnh để lưu &/ tải lên\n",
-    "finetune_name = \"SmolLM2-FT-LoRA-Adapter\"\n",
-    "finetune_tags = [\"smol-course\", \"module_3\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ZbuVArTHouLk"
-   },
-   "source": [
-    "`SFTTrainer` hỗ trợ tích hợp sẵn với `peft`, điều này giúp tinh chỉnh hiệu quả các LLM dễ dàng hơn bằng cách sử dụng, ví dụ như LoRA. Chúng ta chỉ cần tạo `LoraConfig` và cung cấp nó cho trainer.\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Bài tập: Xác định tham số LoRA cho tinh chỉnh</h2>\n",
-    "    <p>Lấy một bộ dữ liệu từ Hugging Face hub và tinh chỉnh một mô hình trên nó.</p>\n",
-    "    <p><b>Mức độ khó</b></p> \n",
-    "    <p>🐢 Sử dụng các tham số chung cho một bản tinh chỉnh tùy ý</p>\n",
-    "    <p>🐕 Điều chỉnh các tham số và so sánh trong weights & biases.</p>\n",
-    "    <p>🦁 Điều chỉnh các tham số và cho thấy sự thay đổi trong kết quả suy luận.</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "blDSs9swouLk"
-   },
-   "outputs": [],
-   "source": [
-    "from peft import LoraConfig\n",
-    "\n",
-    "# TODO: Điều chỉnh cấu hình tham số LoRA\n",
-    "# r: chiều rank cho ma trận cập nhật LoRA (nhỏ hơn = nén nhiều hơn)\n",
-    "rank_dimension = 4\n",
-    "# lora_alpha: hệ số tỷ lệ cho các lớp LoRA (cao hơn = điều chỉnh mạnh hơn) \n",
-    "lora_alpha = 8\n",
-    "# lora_dropout: xác suất dropout cho các lớp LoRA (giúp tránh overfitting)\n",
-    "lora_dropout = 0.05\n",
-    "\n",
-    "peft_config = LoraConfig(\n",
-    "    r=rank_dimension,  # Chiều rank - thường từ 4-32\n",
-    "    lora_alpha=lora_alpha,  # Hệ số tỷ lệ LoRA - thường gấp 2 lần rank\n",
-    "    lora_dropout=lora_dropout,  # Xác suất dropout cho các lớp LoRA \n",
-    "    bias=\"none\",  # Loại bias cho LoRA. các bias tương ứng sẽ được cập nhật trong quá trình huấn luyện.\n",
-    "    target_modules=\"all-linear\",  # Những module nào áp dụng LoRA\n",
-    "    task_type=\"CAUSAL_LM\",  # Loại tác vụ cho kiến trúc mô hình\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "l5NUDPcaouLl"
-   },
-   "source": [
-    "Trước khi bắt đầu huấn luyện, chúng ta cần xác định các `siêu tham số (TrainingArguments)` mà chúng ta muốn sử dụng."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "NqT28VZlouLl"
-   },
-   "outputs": [],
-   "source": [
-    "# Cấu hình huấn luyện  \n",
-    "# Siêu tham số dựa trên khuyến nghị từ bài báo QLoRA \n",
-    "args = SFTConfig(\n",
-    "    # Cài đặt đầu ra\n",
-    "    output_dir=finetune_name,  # Thư mục để lưu checkpoint mô hình\n",
-    "    # Thời gian huấn luyện\n",
-    "    num_train_epochs=1,  # Số epoch huấn luyện\n",
-    "    # Cài đặt kích thước batch \n",
-    "    per_device_train_batch_size=2,  # Kích thước batch cho mỗi GPU\n",
-    "    gradient_accumulation_steps=2,  # Tích lũy gradient cho batch hiệu quả lớn hơn\n",
-    "    # Tối ưu bộ nhớ\n",
-    "    gradient_checkpointing=True,  # Đánh đổi tính toán để tiết kiệm bộ nhớ\n",
-    "    # Cài đặt optimizer\n",
-    "    optim=\"adamw_torch_fused\",  # Sử dụng AdamW fusion cho hiệu quả\n",
-    "    learning_rate=2e-4,  # Tốc độ học (từ bài báo QLoRA)\n",
-    "    max_grad_norm=0.3,  # Ngưỡng cắt gradient\n",
-    "    # Lịch trình học\n",
-    "    warmup_ratio=0.03,  # Phần bước cho warmup\n",
-    "    lr_scheduler_type=\"constant\",  # Giữ tốc độ học không đổi sau warmup\n",
-    "    # Ghi log và lưu\n",
-    "    logging_steps=10,  # Ghi metrics mỗi N bước\n",
-    "    save_strategy=\"epoch\",  # Lưu checkpoint mỗi epoch\n",
-    "    # Cài đặt độ chính xác\n",
-    "    bf16=True,  # Sử dụng độ chính xác bfloat16\n",
-    "    # Cài đặt tích hợp\n",
-    "    push_to_hub=False,  # Không đẩy lên HuggingFace Hub\n",
-    "    report_to=None,  # Tắt ghi log bên ngoài\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "cGhR7uFBouLl"
-   },
-   "source": [
-    "Bây giờ chúng ta đã có mọi thứ cần thiết để tạo `SFTTrainer` và bắt đầu huấn luyện mô hình của mình."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "M00Har2douLl"
-   },
-   "outputs": [],
-   "source": [
-    "max_seq_length = 1512  # độ dài chuỗi tối đa cho mô hình và đóng gói (packing) bộ dữ liệu\n",
-    "\n",
-    "# Tạo SFTTrainer với cấu hình LoRA\n",
-    "trainer = SFTTrainer(\n",
-    "    model=model,\n",
-    "    args=args,\n",
-    "    train_dataset=dataset[\"train\"],\n",
-    "    peft_config=peft_config,  # Cấu hình LoRA\n",
-    "    max_seq_length=max_seq_length,  # Độ dài chuỗi tối đa \n",
-    "    tokenizer=tokenizer,\n",
-    "    packing=True,  # Bật đóng gói đầu vào cho hiệu quả \n",
-    "    dataset_kwargs={\n",
-    "        \"add_special_tokens\": False,  # Token đặc biệt được xử lý bởi template\n",
-    "        \"append_concat_token\": False,  # Không cần thêm separator \n",
-    "    },\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "zQ_kRN24ouLl"
-   },
-   "source": [
-    "Bắt đầu huấn luyện mô hình bằng cách gọi phương thức `train()` trên `Trainer` của chúng ta. Việc này sẽ bắt đầu vòng lặp huấn luyện và huấn luyện mô hình của chúng ta trong `3 epochs`. Vì chúng ta đang sử dụng phương pháp PEFT, chúng ta sẽ chỉ lưu phần trọng số của adapter đã điều chỉnh và không lưu toàn bộ mô hình."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Tq4nIYqKouLl"
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "300e5dfbb4b54750b77324345c7591f9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/72 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "TrainOutput(global_step=72, training_loss=1.6402628521124523, metrics={'train_runtime': 195.2398, 'train_samples_per_second': 1.485, 'train_steps_per_second': 0.369, 'total_flos': 282267289092096.0, 'train_loss': 1.6402628521124523, 'epoch': 0.993103448275862})"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# bắt đầu huấn luyện, mô hình sẽ tự động được lưu lên hub và thư mục đầu ra\n",
-    "trainer.train()\n",
-    "\n",
-    "# lưu mô hình\n",
-    "trainer.save_model()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "y4HHSYYzouLl"
-   },
-   "source": [
-    "Việc huấn luyện với Flash Attention cho 3 epoch với một dataset 15k mẫu mất `4:14:36` trên một cụm máy `g5.2xlarge` của AWS. Cụm máy này có giá `1.21$/h` và tổng chi phí của lần huấn luyện này chỉ tốn khoảng `5.3$`.\n",
-    "\n",
-    "**Ghi chú: bạn hoàn toàn có thể sử dụng GPU của Kaggle hoặc Google Colab để huấn luyện**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "C309KsXjouLl"
-   },
-   "source": [
-    "### Gộp LoRA Adapter vào Mô hình Gốc\n",
-    "\n",
-    "Khi sử dụng LoRA, chúng ta chỉ huấn luyện trọng số adapter trong khi giữ nguyên mô hình cơ sở. Trong quá trình huấn luyện, chúng ta chỉ lưu những trọng số adapter nhẹ này (~2-10MB) thay vì một bản sao mô hình đầy đủ. Tuy nhiên, để triển khai, bạn có thể muốn gộp các adapter trở lại mô hình cơ sở để:\n",
-    "\n",
-    "1. **Đơn giản hóa triển khai**: Một file mô hình thay vì mô hình cơ sở + adapters\n",
-    "2. **Tốc độ suy luận**: Không có chi phí tính toán adapter phụ thêm\n",
-    "3. **Tương thích Framework**: Tương thích tốt hơn với các framework phục vụ"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from peft import AutoPeftModelForCausalLM\n",
-    "\n",
-    "\n",
-    "# Tải mô hình PEFT trên CPU  \n",
-    "model = AutoPeftModelForCausalLM.from_pretrained(\n",
-    "    pretrained_model_name_or_path=args.output_dir,\n",
-    "    torch_dtype=torch.float16,\n",
-    "    low_cpu_mem_usage=True,\n",
-    ")\n",
-    "\n",
-    "# Gộp LoRA và mô hình cơ sở và lưu\n",
-    "merged_model = model.merge_and_unload()\n",
-    "merged_model.save_pretrained(\n",
-    "    args.output_dir, safe_serialization=True, max_shard_size=\"2GB\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "-yO6E9quouLl"
-   },
-   "source": [
-    "## 3. Kiểm tra Mô hình\n",
-    "\n",
-    "Sau khi huấn luyện hoàn tất, chúng ta muốn kiểm tra mô hình của mình. Chúng ta sẽ tải các mẫu khác nhau từ dataset gốc và đánh giá mô hình trên những mẫu đó, sử dụng một vòng lặp đơn giản và độ chính xác làm điểm số đánh giá.\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Bài tập Bonus: Tải LoRA Adapter</h2>\n",
-    "    <p>Sử dụng những gì bạn đã học được từ notebook ví dụ để tải adapter LoRA đã huấn luyện của bạn cho suy luận.</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "id": "I5B494OdouLl"
-   },
-   "outputs": [],
-   "source": [
-    "# giải phóng bộ nhớ một lần nữa\n",
-    "del model\n",
-    "del trainer\n",
-    "torch.cuda.empty_cache()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "P1UhohVdouLl"
-   },
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from peft import AutoPeftModelForCausalLM \n",
-    "from transformers import AutoTokenizer, pipeline\n",
-    "\n",
-    "# Tải Mô hình với PEFT adapter\n",
-    "tokenizer = AutoTokenizer.from_pretrained(finetune_name)\n",
-    "model = AutoPeftModelForCausalLM.from_pretrained(\n",
-    "    finetune_name, device_map=\"auto\", torch_dtype=torch.float16\n",
-    ")\n",
-    "pipe = pipeline(\n",
-    "    \"text-generation\", model=merged_model, tokenizer=tokenizer, device=device\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "99uFDAuuouLl"
-   },
-   "source": [
-    "Hãy thử một số chỉ định mẫu và xem mô hình hoạt động như thế nào."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "id": "-shSmUbvouLl",
-    "outputId": "16d97c61-3b31-4040-c780-3c4de75c3824"
-   },
-   "outputs": [],
-   "source": [
-    "prompts = [\n",
-    "    \"Thủ đô của Việt Nam thành phố nào? Giải thích tại sao là như vậy và liệu nó có khác trong quá khứ không?\",\n",
-    "    \"Viết một hàm Python để tính giai thừa của một số.\",\n",
-    "    \"Một khu vườn hình chữ nhật có chiều dài 25 mét và chiều rộng 15 mét. Nếu bạn muốn xây một hàng rào xung quanh toàn bộ khu vườn, bạn sẽ cần bao nhiêu mét hàng rào?\",\n",
-    "    \"Sự khác biệt giữa trái cây và rau củ là gì? Đưa ra ví dụ cho mỗi loại.\",\n",
-    "]\n",
-    "\n",
-    "\n",
-    "def test_inference(prompt):\n",
-    "    prompt = pipe.tokenizer.apply_chat_template(\n",
-    "        [{\"role\": \"user\", \"content\": prompt}],\n",
-    "        tokenize=False,\n",
-    "        add_generation_prompt=True,\n",
-    "    )\n",
-    "    outputs = pipe(\n",
-    "        prompt,\n",
-    "    )\n",
-    "    return outputs[0][\"generated_text\"][len(prompt):].strip()\n",
-    "\n",
-    "\n",
-    "for prompt in prompts:\n",
-    "    print(f\"    prompt:\\n{prompt}\")\n",
-    "    print(f\"    response:\\n{test_inference(prompt)}\")\n",
-    "    print(\"-\" * 50)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.10"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/vi/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb b/vi/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
deleted file mode 100644
index 73d258e9..00000000
--- a/vi/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
+++ /dev/null
@@ -1,5059 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "otj46qRbtpnd",
-        "outputId": "0fa81296-44b7-479d-e03b-7ef196cc89e4"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install -q bitsandbytes datasets accelerate loralib\n",
-        "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git"
-      ]
-    },
-    {
-      "cell_type": "code",
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-        "id": "cg3fiQOvmI3Q",
-        "outputId": "92fcd520-ad2c-4488-8f66-664ffc566b89"
-      },
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
-        "import torch\n",
-        "import torch.nn as nn\n",
-        "import bitsandbytes as bnb\n",
-        "from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "S65GcxNGA9kz"
-      },
-      "source": [
-        "## Sử dụng adapters từ Hugging Face Hub"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 517,
-          "referenced_widgets": [
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-          ]
-        },
-        "id": "hsD1VKqeA62Z",
-        "outputId": "83ecbf31-3f84-4b8e-d667-10317f7adcec"
-      },
-      "outputs": [],
-      "source": [
-        "import torch\n",
-        "import transformers\n",
-        "from datasets import load_dataset\n",
-        "from peft import LoraConfig, get_peft_model\n",
-        "from peft import PeftModel, PeftConfig\n",
-        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-        "\n",
-        "peft_model_id = \"ybelkada/opt-6.7b-lora\"\n",
-        "config = PeftConfig.from_pretrained(peft_model_id)\n",
-        "model = AutoModelForCausalLM.from_pretrained(\n",
-        "    config.base_model_name_or_path,\n",
-        "    return_dict=True,\n",
-        "    load_in_8bit=True,\n",
-        "    device_map=\"auto\",\n",
-        ")\n",
-        "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
-        "\n",
-        "# Tải Lora adapter\n",
-        "model = PeftModel.from_pretrained(model, peft_model_id)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "MDqJWba-tpnv",
-        "outputId": "dd65644f-6921-46e1-920c-400bd7d888cf"
-      },
-      "outputs": [],
-      "source": [
-        "batch = tokenizer(\"Machine learning is: \", return_tensors='pt')\n",
-        "\n",
-        "with torch.cuda.amp.autocast():\n",
-        "  output_tokens = model.generate(**batch, max_new_tokens=50)\n",
-        "\n",
-        "print('\\n\\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True))"
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "machine_shape": "hm",
-      "provenance": []
-    },
-    "gpuClass": "standard",
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
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-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
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diff --git a/vi/3_parameter_efficient_finetuning/prompt_tuning.md b/vi/3_parameter_efficient_finetuning/prompt_tuning.md
deleted file mode 100644
index 3e09a107..00000000
--- a/vi/3_parameter_efficient_finetuning/prompt_tuning.md
+++ /dev/null
@@ -1,76 +0,0 @@
-# Phương Pháp Điều Chỉnh Chỉ Thị (Prompt Tuning)
-
-Prompt tuning là một phương pháp tiết kiệm tham số bằng cách điều chỉnh biểu diễn đầu vào thay vì trọng số mô hình. Không giống như tinh chỉnh truyền thống cập nhật tất cả các tham số mô hình, prompt tuning thêm và tối ưu hóa một tập nhỏ các token có thể huấn luyện trong khi giữ nguyên mô hình cơ sở.
-
-## Tìm Hiểu Về Prompt Tuning
-
-Prompt tuning là một phương pháp thay thế tiết kiệm tham số cho việc tinh chỉnh mô hình bằng cách thêm các vector liên tục có thể huấn luyện (soft prompts) vào trước văn bản đầu vào. Không giống như các chỉ thị rời rạc (discrete prompt), những soft prompt này được học thông qua truyền ngược (back-propagation) trong khi giữ nguyên mô hình ngôn ngữ. Phương pháp này được giới thiệu trong ["The Power of Scale for Parameter-Efficient Prompt Tuning"](https://arxiv.org/abs/2104.08691) (Lester et al., 2021), cho thấy prompt tuning trở nên cạnh tranh hơn với việc tinh chỉnh mô hình khi kích thước mô hình tăng lên. Trong bài báo, với khoảng 10 tỷ tham số, prompt tuning đạt hiệu suất tương đương với tinh chỉnh mô hình trong khi chỉ sửa đổi vài trăm tham số cho mỗi tác vụ.
-
-Các soft prompt này là các vector liên tục trong không gian embedding của mô hình được tối ưu hóa trong quá trình huấn luyện. Không giống như các chỉ thị rời rạc truyền thống sử dụng các token ngôn ngữ tự nhiên, soft prompt là các chỉ thị không có ý nghĩa nhưng được học để tạo ra hành vi mong muốn từ mô hình đã đóng băng thông qua gradient descent. Kỹ thuật này đặc biệt hiệu quả cho các kịch bản đa tác vụ vì mỗi tác vụ chỉ cần lưu trữ một vector prompt nhỏ (thường là vài trăm tham số) thay vì một bản sao mô hình đầy đủ. Phương pháp này không chỉ duy trì bộ nhớ tối thiểu mà còn cho phép chuyển đổi tác vụ nhanh chóng bằng cách chỉ cần hoán đổi vector prompt mà không cần tải lại mô hình.
-
-## Quá trình huấn luyện
-
-Soft prompt thường có từ **8 đến 32 token** và có thể được **khởi tạo ngẫu nhiên hoặc từ văn bản hiện có**. Phương pháp khởi tạo đóng vai trò quan trọng trong quá trình huấn luyện, với việc khởi tạo dựa trên văn bản thường hoạt động tốt hơn so với khởi tạo ngẫu nhiên.
-
-Trong quá trình huấn luyện, chỉ các tham số prompt được cập nhật trong khi mô hình cơ sở vẫn đóng băng. Cách tiếp cận tập trung này sử dụng các mục tiêu huấn luyện tiêu chuẩn nhưng đòi hỏi chú ý đến tốc độ học và hành vi gradient của các token prompt.
-
-![prompt_tuning_explain](./images/prompt_tuning.png)
-
-## Triển khai với PEFT
-
-Thư viện PEFT giúp việc triển khai prompt tuning trở nên đơn giản. Đây là một ví dụ cơ bản:
-
-```python
-from peft import PromptTuningConfig, TaskType, get_peft_model
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-# Tải mô hình cơ sở
-model = AutoModelForCausalLM.from_pretrained("your-base-model")
-tokenizer = AutoTokenizer.from_pretrained("your-base-model")
-
-# Điều chỉnh thông số prompt tuning
-peft_config = PromptTuningConfig(
-    task_type=TaskType.CAUSAL_LM,
-    num_virtual_tokens=8,  # Số token có thể huấn luyện
-    prompt_tuning_init="TEXT",  # Khởi tạo từ văn bản
-    prompt_tuning_init_text="Phân loại xem văn bản này là tích cực hay tiêu cực:",
-    tokenizer_name_or_path="your-base-model",
-)
-
-# Tạo mô hình có thể prompt-tune
-model = get_peft_model(model, peft_config)
-```
-
-## So sánh với các phương pháp khác
-
-Khi so sánh với các phương pháp PEFT khác, prompt tuning nổi bật về hiệu quả. Trong khi LoRA cung cấp số lượng tham số và bộ nhớ thấp nhưng yêu cầu tải adapter khi chuyển đổi tác vụ, prompt tuning đạt được mức sử dụng tài nguyên thậm chí thấp hơn và cho phép chuyển đổi tác vụ ngay lập tức. Ngược lại, tinh chỉnh đầy đủ đòi hỏi tài nguyên đáng kể và yêu cầu các bản sao mô hình riêng biệt cho các tác vụ khác nhau.
-
-| Phương pháp | Tham số | Bộ nhớ | Chuyển đổi tác vụ |
-|--------|------------|---------|----------------|
-| Prompt Tuning | Rất thấp | Tối thiểu | Dễ dàng |
-| LoRA | Thấp | Thấp | Yêu cầu tải |
-| Tinh chỉnh đầy đủ | Cao | Cao | Cần bản sao mô hình mới |
-
-Khi triển khai prompt tuning, hãy bắt đầu với một số lượng nhỏ token ảo (8-16) và chỉ tăng lên nếu độ phức tạp của tác vụ yêu cầu. Khởi tạo từ văn bản thường cho kết quả tốt hơn so với khởi tạo ngẫu nhiên, đặc biệt khi sử dụng văn bản liên quan đến tác vụ. Chiến lược khởi tạo nên phản ánh độ phức tạp của tác vụ mục tiêu của bạn.
-
-Huấn luyện đòi hỏi những cân nhắc hơi khác so với tinh chỉnh đầy đủ. Tốc độ học (learning rates) cao hơn thường sẽ cho kết quả tốt hơn, nhưng cũng cần theo dõi cẩn thận *gradient* của token prompt. Kiểm tra thường xuyên trên các ví dụ đa dạng giúp đảm bảo hiệu suất mạnh mẽ trong các tình huống khác nhau.
-
-## Ứng dụng
-
-Prompt tuning xuất sắc trong một số kịch bản:
-
-1. Triển khai đa tác vụ
-2. Môi trường hạn chế tài nguyên
-3. Thích ứng tác vụ nhanh chóng
-4. Ứng dụng nhạy cảm về quyền riêng tư
-
-Khi mô hình nhỏ hơn, prompt tuning trở nên kém cạnh tranh hơn so với tinh chỉnh đầy đủ. Ví dụ, trên các mô hình như SmolLM2, prompt tuning ít liên quan hơn so với tinh chỉnh đầy đủ.
-
-## Các bước tiếp theo
-
-⏭️ Chuyển sang [Hướng dẫn LoRA Adapters](./notebooks/finetune_sft_peft.ipynb) để tìm hiểu cách tinh chỉnh một mô hình với LoRA adapters.
-
-## Tài liệu tham khảo
-- [Tài liệu về thư viện PEFT](https://huggingface.co/docs/peft)
-- [Bài báo nghiên cứu về Prompt Tuning](https://arxiv.org/abs/2104.08691) 
-- [Hướng dẫn thực hành của Hugging Face](https://huggingface.co/learn/cookbook/prompt_tuning_peft)
\ No newline at end of file
diff --git a/vi/4_evaluation/README.md b/vi/4_evaluation/README.md
deleted file mode 100644
index 27b796da..00000000
--- a/vi/4_evaluation/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
-# Đánh giá mô hình
-
-Đánh giá là bước quan trọng trong việc phát triển và triển khai các mô hình ngôn ngữ. Nó giúp chúng ta hiểu được mô hình hoạt động tốt như thế nào qua các khả năng khác nhau và xác định những lĩnh vực cần cải thiện. Chương này bao gồm các phương pháp đánh giá (benchmark) tiêu chuẩn và các phương pháp đánh giá theo lĩnh vực cụ thể để đánh giá toàn diện mô hình của bạn.
-
-Chúng ta sẽ sử dụng [`lighteval`](https://github.com/huggingface/lighteval), một thư viện chuyên được sử dụng để đánh giá mô hình ngôn ngữ được phát triển bởi Hugging Face. Thư viện này được thiết kế tối giản giúp người sử dụng dễ dàng tiếp cận và được tích hợp để phù hợp với hệ sinh thái của Hugging Face. Để tìm hiểu sâu hơn về các khái niệm và thực hành về đánh giá mô hình ngôn ngữ, hãy tham khảo [hướng dẫn](https://github.com/huggingface/evaluation-guidebook) về đánh giá.
-
-## Tổng quan chương học
-
-Một quy trình đánh giá mô hình thường bao gồm nhiều khía cạnh về hiệu năng của mô hình. Chúng ta đánh giá các khả năng theo tác vụ cụ thể như *trả lời câu hỏi* và *tóm tắt* để hiểu mô hình xử lý các loại vấn đề khác nhau tốt đến đâu. Chúng ta đo lường chất lượng đầu ra thông qua các yếu tố như *tính mạch lạc* và *độ chính xác*. Đánh giá về *độ an toàn* giúp xác định các đầu ra của mô hình có chứa các hành vi khuyển khích thực hiện hành đông độc hại hoặc định kiến tiềm ẩn. Cuối cùng, kiểm tra *chuyên môn* về lĩnh vực xác minh kiến thức chuyên biệt của mô hình trong lĩnh vực mục tiêu của bạn.
-
-## Nội dung
-
-### 1️⃣ [Đánh giá mô hình tự động](./automatic_benchmarks.md)
-
-Học cách đánh giá mô hình của bạn bằng các phương pháp đánh giá và số liệu chuẩn hoá. Chúng ta sẽ khám phá các phương pháp đánh giá phổ biến như `MMLU` và `TruthfulQA`, hiểu các chỉ số đánh giá và cài đặt quan trọng, đồng thời đề cập đến các thực hành tốt nhất cho việc đánh giá có thể tái tạo lại.
-
-### 2️⃣ [Đánh giá theo yêu cầu đặc biệt](./custom_evaluation.md)
-Khám phá cách tạo quy trình (pipeline) đánh giá phù hợp với trường hợp sử dụng cụ thể của bạn. Chúng ta sẽ hướng dẫn thiết kế các tác vụ đánh giá theo yêu cầu (custom), triển khai các số liệu chuyên biệt và xây dựng tập dữ liệu đánh giá phù hợp với yêu cầu của bạn.
-
-### 3️⃣ [Dự án đánh giá theo lĩnh vực cụ thể](./project/README.md)
-Theo dõi một ví dụ hoàn chỉnh về việc xây dựng quy trình đánh giá cho lĩnh vực cụ thể. Bạn sẽ học cách tạo tập dữ liệu đánh giá, sử dụng `Argilla` để gán nhãn dữ liệu, tạo tập dữ liệu chuẩn hoá và đánh giá mô hình bằng `LightEval`.
-
-### Các notebook bài tập
-
-| Tiêu đề | Mô tả | Bài tập | Link | Colab |
-|---------|-------|---------|------|-------|
-| Đánh giá và phân tích mô hình LLM của bạn | Học cách sử dụng `LightEval` để đánh giá và so sánh các mô hình trên các lĩnh vực cụ thể | 🐢 Sử dụng các tác vụ trong **lĩnh vực y tế** để đánh giá mô hình <br> 🐕 Tạo đánh giá lĩnh vực mới với các tác vụ MMLU khác nhau <br> 🦁 Tạo tác vụ đánh giá cho lĩnh vực của bạn | [Notebook](./notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-
-## Tài liệu tham khảo
-
-- [Hướng dẫn đánh giá](https://github.com/huggingface/evaluation-guidebook) - Hướng dẫn toàn diện về đánh giá LLM
-- [Tài liệu LightEval](https://github.com/huggingface/lighteval) - Tài liệu chính thức cho thư viện LightEval 
-- [Tài liệu Argilla](https://docs.argilla.io) - Tìm hiểu về nền tảng gán nhãn Argilla
-- [Paper MMLU](https://arxiv.org/abs/2009.03300) - Paper mô tả benchmark MMLU
-- [Tạo tác vụ theo yêu cầu](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
-- [Tạo tiêu chuẩn đánh giá theo yêu cầu](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
-- [Sử dụng số liệu có sẵn](https://github.com/huggingface/lighteval/wiki/Metric-List)
\ No newline at end of file
diff --git a/vi/4_evaluation/automatic_benchmarks.md b/vi/4_evaluation/automatic_benchmarks.md
deleted file mode 100644
index b5852e9c..00000000
--- a/vi/4_evaluation/automatic_benchmarks.md
+++ /dev/null
@@ -1,131 +0,0 @@
-# Đánh giá tự động
-
-Đánh giá tự động là các công cụ chuẩn hoá để đánh giá các mô hình ngôn ngữ qua các tác vụ và khả năng khác nhau. Mặc dù chúng cung cấp điểm khởi đầu hữu ích để hiểu hiệu năng của mô hình, điều quan trọng là phải nhận ra rằng chúng chỉ là một phần trong toàn bộ khả năng của mô hình.
-
-## Hiểu về đánh giá tự động
-
-Đánh giá tự động thường bao gồm các tập dữ liệu được tuyển chọn với các tác vụ và tiêu chí đánh giá được định nghĩa trước. Những đánh giá này nhằm đánh giá nhiều khía cạnh về khả năng của mô hình, từ hiểu ngôn ngữ cơ bản đến suy luận phức tạp. Lợi thế chính của việc sử dụng đánh giá tự động là tính chuẩn hoá - chúng cho phép so sánh nhất quán giữa các mô hình khác nhau và cung cấp kết quả có thể tái tạo lại.
-
-Tuy nhiên, điều quan trọng là phải hiểu rằng điểm số trên các bài đánh giá không phải lúc nào cũng thể hiện chính xác hiệu quả của mô hình trong thực tế. Một mô hình xuất sắc trong các bài đánh giá học thuật (academic benchmark) vẫn có thể gặp khó khăn với các ứng dụng vào lĩnh vực cụ thể hoặc các trường hợp sử dụng thực tế.
-
-## Các bài đánh giá và hạn chế của chúng
-
-### Đánh giá kiến thức tổng quát
-
-`MMLU` (Massive Multitask Language Understanding) là bài đánh giá dùng để kiểm tra kiến thức trên *57 môn học*, từ khoa học tự nhiên đến khoa học xã hội. Mặc dù toàn diện, nó có thể không phản ánh độ sâu của chuyên môn cần thiết cho các lĩnh vực cụ thể. `TruthfulQA` đánh giá xu hướng mô hình tái tạo các quan niệm sai lầm phổ biến, mặc dù nó không thể nắm bắt tất cả các hình thức thông tin sai lệch.
-
-### Đánh giá khả năng suy luận
-`BBH` (Big Bench Hard) và `GSM8K` tập trung vào các tác vụ suy luận phức tạp. `BBH` kiểm tra tư duy logic và lập kế hoạch, trong khi `GSM8K` nhắm vào giải quyết vấn đề toán học. Những bài đánh giá này giúp kiểm tra khả năng phân tích nhưng có thể không nắm bắt được khả năng suy luận tinh tế cần thiết trong các tình huống thực tế.
-
-### Đánh giá khả năng ngôn ngữ
-`HELM` cung cấp một đánh giá toàn diện, trong khi `WinoGrande` kiểm tra hiểu biết thông thường thông qua việc giải nghĩa các đại từ. Những bài đánh giá này cung cấp cái nhìn sâu sắc về khả năng xử lý ngôn ngữ nhưng có thể không hoàn toàn đại diện cho độ phức tạp của giao tiếp tự nhiên hoặc thuật ngữ theo lĩnh vực cụ thể.
-
-## Các phương pháp đánh giá thay thế
-
-Nhiều tổ chức đã phát triển các phương pháp đánh giá thay thế để khắc phục những hạn chế của bài đánh giá tiêu chuẩn:
-
-### LLM trong vài trò người đánh giá 
-Sử dụng một mô hình ngôn ngữ để đánh giá đầu ra của mô hình khác ngày càng phổ biến. Phương pháp này có thể cung cấp phản hồi chi tiết hơn các bài đánh giá truyền thống, mặc dù nó đi kèm với những định kiến (bias) của mô hình đánh giá và hạn chế riêng.
-
-### Môi trường đánh giá
-Các nền tảng như Constitutional AI Arena của Anthropic cho phép các mô hình tương tác và đánh giá lẫn nhau trong môi trường được kiểm soát. Điều này có thể cho thấy những điểm mạnh và điểm yếu có thể không rõ ràng trong các benchmark truyền thống.
-
-### Bộ đánh giá tuỳ chỉnh
-Các tổ chức thường phát triển bộ benchmark nội bộ được điều chỉnh cho nhu cầu và trường hợp sử dụng cụ thể của họ. Những bộ này có thể bao gồm kiểm tra kiến thức theo lĩnh vực cụ thể hoặc các kịch bản đánh giá phản ánh điều kiện triển khai thực tế.
-
-## Tạo chiến lược đánh giá riêng
-
-Hãy nhớ rằng mặc dù LightEval giúp dễ dàng chạy các benchmark tiêu chuẩn, bạn cũng nên dành thời gian phát triển phương pháp đánh giá phù hợp với trường hợp sử dụng của mình.
-
-Mặc dù benchmark tiêu chuẩn cung cấp một đường cơ sở hữu ích, chúng không nên là phương pháp đánh giá duy nhất của bạn. Dưới đây là cách phát triển một phương pháp toàn diện hơn:
-
-1. Bắt đầu với các benchmark tiêu chuẩn liên quan để thiết lập đường cơ sở và cho phép so sánh với các mô hình khác.
-
-2. Xác định các yêu cầu và thách thức cụ thể của trường hợp sử dụng của bạn. Mô hình của bạn sẽ thực hiện những tác vụ gì? Những loại lỗi nào sẽ gây rắc rối nhất?
-
-3. Phát triển tập dữ liệu đánh giá tuỳ chỉnh phản ánh trường hợp sử dụng thực tế của bạn. Điều này có thể bao gồm:
-   - Các câu hỏi thực tế từ người dùng trong lĩnh vực của bạn
-   - Các trường hợp ngoại lệ phổ biến bạn đã gặp
-   - Các ví dụ về tình huống đặc biệt khó khăn
-
-4. Xem xét triển khai chiến lược đánh giá nhiều lớp:
-   - Số liệu tự động để nhận phản hồi nhanh
-   - Đánh giá bởi con người để hiểu sâu sắc hơn
-   - Đánh giá của chuyên gia lĩnh vực cho các ứng dụng chuyên biệt
-   - Kiểm tra A/B trong môi trường được kiểm soát
-
-## Sử dụng LightEval để đánh giá
-
-Các tác vụ LightEval được định nghĩa theo định dạng cụ thể:
-```
-{suite}|{task}|{num_few_shot}|{auto_reduce}
-```
-
-- **suite**: Bộ benchmark (ví dụ: 'mmlu', 'truthfulqa')  
-- **task**: Tác vụ cụ thể trong bộ (ví dụ: 'abstract_algebra')
-- **num_few_shot**: Số lượng ví dụ để đưa vào prompt (0 cho zero-shot)
-- **auto_reduce**: Có tự động giảm ví dụ few-shot nếu prompt quá dài hay không (0 hoặc 1)
-
-Ví dụ: `"mmlu|abstract_algebra|0|0"` đánh giá tác vụ đại số trừu tượng của MMLU với suy luận zero-shot.
-
-### Ví dụ về Pipeline đánh giá 
-
-Đây là một ví dụ hoàn chỉnh về việc thiết lập và chạy đánh giá trên các benchmark tự động liên quan đến một lĩnh vực cụ thể:
-
-```python
-from lighteval.tasks import Task, Pipeline
-from transformers import AutoModelForCausalLM
-
-# Định nghĩa các tác vụ để đánh giá
-domain_tasks = [
-    "mmlu|anatomy|0|0",
-    "mmlu|high_school_biology|0|0", 
-    "mmlu|high_school_chemistry|0|0",
-    "mmlu|professional_medicine|0|0"
-]
-
-# Cấu hình tham số pipeline
-pipeline_params = {
-    "max_samples": 40,  # Số lượng mẫu để đánh giá
-    "batch_size": 1,    # Kích thước batch cho inference 
-    "num_workers": 4    # Số lượng worker process
-}
-
-# Tạo evaluation tracker
-evaluation_tracker = EvaluationTracker(
-    output_path="./results",
-    save_generations=True
-)
-
-# Tải mô hình và tạo pipeline
-model = AutoModelForCausalLM.from_pretrained("your-model-name")
-pipeline = Pipeline(
-    tasks=domain_tasks,
-    pipeline_parameters=pipeline_params,
-    evaluation_tracker=evaluation_tracker,
-    model=model
-)
-
-# Chạy đánh giá
-pipeline.evaluate()
-
-# Lấy và hiển thị kết quả 
-results = pipeline.get_results()
-pipeline.show_results()
-```
-
-Kết quả được hiển thị dưới dạng bảng thể hiện:
-```
-|                  Task                  |Version|Metric|Value |   |Stderr|
-|----------------------------------------|------:|------|-----:|---|-----:|
-|all                                     |       |acc   |0.3333|±  |0.1169|
-|leaderboard:mmlu:_average:5             |       |acc   |0.3400|±  |0.1121|
-|leaderboard:mmlu:anatomy:5              |      0|acc   |0.4500|±  |0.1141|
-|leaderboard:mmlu:high_school_biology:5  |      0|acc   |0.1500|±  |0.0819|
-```
-
-Bạn cũng có thể xử lý kết quả trong pandas DataFrame và trực quan hoá hoặc biểu diễn chúng theo cách bạn muốn.
-
-# Bước tiếp theo
-
-⏩ Khám phá [Đánh giá theo lĩnh vực tuỳ chỉnh](./custom_evaluation.md) để học cách tạo pipeline đánh giá phù hợp với nhu cầu cụ thể của bạn
\ No newline at end of file
diff --git a/vi/4_evaluation/custom_evaluation.md b/vi/4_evaluation/custom_evaluation.md
deleted file mode 100644
index 3461cb48..00000000
--- a/vi/4_evaluation/custom_evaluation.md
+++ /dev/null
@@ -1,133 +0,0 @@
-# Đánh Giá Theo Lĩnh Vực Cụ Thể
-
-Mặc dù các phương pháp đánh giá tiêu chuẩn (`benchmark`) cung cấp những thông tin chuyên sâu có giá trị, nhiều ứng dụng đòi hỏi các phương pháp đánh giá chuyên biệt phù hợp với các lĩnh vực hoặc trường hợp sử dụng cụ thể. Bài học này sẽ giúp bạn tạo các quy trình (`pipeline`) tùy chỉnh các phương pháp đánh giá để có cái nhìn chính xác hơn về hiệu suất của mô hình trong lĩnh vực cụ thể của bạn.
-
-## Thiết kế chiến lược đánh giá của bạn
-
-Một chiến lược đánh giá tùy chỉnh đầy đủ bắt đầu với các mục tiêu rõ ràng. Hãy xem xét những khả năng cụ thể mà mô hình của bạn cần thể hiện trong lĩnh vực của bạn. Điều này có thể bao gồm kiến thức kỹ thuật, các mẫu suy luận hoặc các định dạng đặc thù cho lĩnh vực đó. Ghi lại cẩn thận các yêu cầu này - chúng sẽ hướng dẫn cả việc thiết kế tác vụ (`task`) và lựa chọn các chỉ số (`metric`).
-
-Việc đánh giá của bạn nên kiểm tra cả các trường hợp sử dụng tiêu chuẩn và các trường hợp biên (`edge case`). Ví dụ, trong lĩnh vực y tế, bạn có thể đánh giá cả các kịch bản chẩn đoán phổ biến và các tình trạng hiếm gặp. Trong các ứng dụng tài chính, bạn có thể kiểm tra cả các giao dịch thông thường và các trường hợp biên phức tạp liên quan đến nhiều loại tiền tệ hoặc các điều kiện đặc biệt.
-
-## Triển khai với LightEval
-
-`LightEval` cung cấp một khung (`framework`) linh hoạt để triển khai các đánh giá tùy chỉnh. Đây là cách để tạo một tác vụ (`task`) tùy chỉnh:
-
-```python
-from lighteval.tasks import Task, Doc
-from lighteval.metrics import SampleLevelMetric, MetricCategory, MetricUseCase
-
-class CustomEvalTask(Task):
-    def __init__(self):
-        super().__init__(
-            name="custom_task",
-            version="0.0.1",
-            metrics=["accuracy", "f1"],  # Các chỉ số bạn chọn
-            description="Mô tả tác vụ đánh giá tùy chỉnh của bạn"
-        )
-    
-    def get_prompt(self, sample):
-        # Định dạng đầu vào của bạn thành một lời nhắc (prompt)
-        return f"Question: {sample['question']}\nAnswer:"
-    
-    def process_response(self, response, ref):
-        # Xử lý đầu ra của mô hình và so sánh với tham chiếu
-        return response.strip() == ref.strip()
-```
-
-
-## Các chỉ số (`metric`) tùy chỉnh
-
-Các tác vụ theo lĩnh vực cụ thể thường yêu cầu các chỉ số chuyên biệt. `LightEval` cung cấp một khung linh hoạt để tạo các chỉ số tùy chỉnh nắm bắt các khía cạnh liên quan đến lĩnh vực của hiệu suất:
-
-```python
-from aenum import extend_enum
-from lighteval.metrics import Metrics, SampleLevelMetric, SampleLevelMetricGrouping
-import numpy as np
-
-# Định nghĩa một hàm chỉ số mức mẫu (sample-level metric)
-def custom_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> dict:
-    """Chỉ số ví dụ trả về nhiều điểm số cho mỗi mẫu"""
-    response = predictions[0]
-    return {
-        "accuracy": response == formatted_doc.choices[formatted_doc.gold_index],
-        "length_match": len(response) == len(formatted_doc.reference)
-    }
-
-# Tạo một chỉ số trả về nhiều giá trị cho mỗi mẫu
-custom_metric_group = SampleLevelMetricGrouping(
-    metric_name=["accuracy", "length_match"],  # Tên của các chỉ số
-    higher_is_better={  # Liệu giá trị cao hơn có tốt hơn cho mỗi chỉ số không
-        "accuracy": True,
-        "length_match": True
-    },
-    category=MetricCategory.CUSTOM,
-    use_case=MetricUseCase.SCORING,
-    sample_level_fn=custom_metric,
-    corpus_level_fn={  # Cách tổng hợp từng chỉ số
-        "accuracy": np.mean,
-        "length_match": np.mean
-    }
-)
-
-# Đăng ký chỉ số vào LightEval
-extend_enum(Metrics, "custom_metric_name", custom_metric_group)
-```
-
-Đối với các trường hợp đơn giản hơn, nơi bạn chỉ cần một giá trị chỉ số cho mỗi mẫu:
-
-```python
-def simple_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> bool:
-    """Chỉ số ví dụ trả về một điểm duy nhất cho mỗi mẫu"""
-    response = predictions[0]
-    return response == formatted_doc.choices[formatted_doc.gold_index]
-
-simple_metric_obj = SampleLevelMetric(
-    metric_name="simple_accuracy",
-    higher_is_better=True,
-    category=MetricCategory.CUSTOM,
-    use_case=MetricUseCase.SCORING,
-    sample_level_fn=simple_metric,
-    corpus_level_fn=np.mean  # Cách tổng hợp trên các mẫu
-)
-
-extend_enum(Metrics, "simple_metric", simple_metric_obj)
-```
-
-Sau đó, bạn có thể sử dụng các chỉ số tùy chỉnh của mình trong các tác vụ đánh giá bằng cách tham chiếu chúng trong cấu hình tác vụ. Các chỉ số sẽ được tự động tính toán trên tất cả các mẫu và tổng hợp theo các hàm bạn đã chỉ định.
-
-Đối với các chỉ số phức tạp hơn, hãy xem xét:
-- Sử dụng siêu dữ liệu (`metadata`) trong các tài liệu được định dạng của bạn để đánh trọng số hoặc điều chỉnh điểm số
-- Triển khai các hàm tổng hợp tùy chỉnh cho các thống kê cấp độ kho ngữ liệu (`corpus-level`)
-- Thêm kiểm tra xác thực cho đầu vào chỉ số của bạn
-- Ghi lại các trường hợp biên và hành vi mong đợi
-
-Để có một ví dụ đầy đủ về các chỉ số tùy chỉnh trong thực tế, hãy xem [dự án đánh giá theo lĩnh vực](./project/README.md) của chúng tôi.
-
-## Tạo tập dữ liệu
-
-Đánh giá chất lượng cao đòi hỏi các tập dữ liệu được sắp xếp cẩn thận. Hãy xem xét các phương pháp sau để tạo tập dữ liệu:
-
-1. Chú thích của chuyên gia: Làm việc với các chuyên gia trong lĩnh vực để tạo và xác thực các ví dụ đánh giá. Các công cụ như [Argilla](https://github.com/argilla-io/argilla) làm cho quá trình này hiệu quả hơn.
-
-2. Dữ liệu thế giới thực: Thu thập và ẩn danh hóa dữ liệu sử dụng thực tế, đảm bảo nó đại diện cho các kịch bản triển khai thực tế.
-
-3. Tạo tổng hợp: Sử dụng `LLM` để tạo các ví dụ ban đầu, sau đó để các chuyên gia xác thực và tinh chỉnh chúng.
-
-## Các phương pháp tốt nhất
-
-- Ghi lại phương pháp đánh giá của bạn một cách kỹ lưỡng, bao gồm mọi giả định hoặc hạn chế
-- Bao gồm các trường hợp kiểm thử đa dạng bao gồm các khía cạnh khác nhau trong lĩnh vực của bạn
-- Xem xét cả các chỉ số tự động và đánh giá thủ công khi thích hợp
-- Kiểm soát phiên bản (`Version control`) các tập dữ liệu và mã đánh giá của bạn
-- Thường xuyên cập nhật bộ đánh giá của bạn khi bạn phát hiện ra các trường hợp biên hoặc yêu cầu mới
-
-## Tài liệu tham khảo
-
-- [Hướng dẫn tác vụ tùy chỉnh LightEval](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
-- [Chỉ số tùy chỉnh LightEval](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
-- [Tài liệu Argilla](https://docs.argilla.io) để chú thích tập dữ liệu
-- [Hướng dẫn đánh giá](https://github.com/huggingface/evaluation-guidebook) cho các nguyên tắc đánh giá chung
-
-# Các bước tiếp theo
-
-⏩ Để có một ví dụ đầy đủ về việc triển khai các khái niệm này, hãy xem [dự án đánh giá theo lĩnh vực](./project/README.md) của chúng tôi.
\ No newline at end of file
diff --git a/vi/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb b/vi/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
deleted file mode 100644
index 92d67ae1..00000000
--- a/vi/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
+++ /dev/null
@@ -1,19422 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TZVw9f5QYWPL"
-      },
-      "source": [
-        "# `lighteval` là thư viện đánh giá AI của bạn\n",
-        "\n",
-        "Notebook này khám phá cách bạn có thể sử dụng `lighteval` để đánh giá và so sánh các `LLM`.\n",
-        "\n",
-        "`lighteval` đã xuất hiện được một thời gian và nó là một công cụ tuyệt vời để đạt được điểm đánh giá trên các `benchmark` chính. Nó vừa được tái cấu trúc để hỗ trợ việc sử dụng như một thư viện trong Python, điều này làm cho nó trở nên tuyệt vời để so sánh các mô hình trên các `benchmark`.\n",
-        "\n",
-        "Vì vậy, hãy đi sâu vào một số điểm đánh giá.\n",
-        "\n",
-        "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-        "    <h2 style='margin: 0;color:blue'>Bài tập: Đánh giá mô hình của riêng bạn</h2>\n",
-        "    <p>Bây giờ bạn đã thấy cách đánh giá các mô hình trên các lĩnh vực cụ thể, hãy thử đánh giá một mô hình trên một lĩnh vực mà bạn quan tâm.</p>\n",
-        "    <p><b>Các mức độ khó</b></p>\n",
-        "    <p>🐢 Sử dụng các tác vụ lĩnh vực y tế hiện có nhưng đánh giá một mô hình khác từ Hugging Face Hub</p>\n",
-        "    <p>🐕 Tạo một đánh giá lĩnh vực mới bằng cách chọn các tác vụ MMLU khác nhau (ví dụ: khoa học máy tính, toán học, vật lý)</p>\n",
-        "    <p>🦁 Tạo một tác vụ đánh giá tùy chỉnh bằng cách sử dụng khung tác vụ của LightEval và đánh giá các mô hình trên lĩnh vực cụ thể của bạn</p>\n",
-        "</div>\n",
-        "\n",
-        "## Cài đặt các thư viện"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "afW_CLJoPCnF",
-        "outputId": "91611cd5-51d6-4f73-9b3d-ac79c918d074"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install -qqq -U \"torch<2.5\" \"torchvision<2.5\" \"torchaudio<2.5\" --index-url https://download.pytorch.org/whl/cu121\n",
-        "!pip list | grep torch"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "8xMnn_cQ1EEi",
-        "outputId": "bcaf8498-19c3-46af-9ae1-0306326f006b"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install -qqq git+https://github.com/huggingface/lighteval.git tiktoken"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TDKs5ShvXw8K"
-      },
-      "source": [
-        "## Thiết lập đánh giá `lighteval`\n",
-        "\n",
-        "Chúng ta cần thiết lập môi trường và quy trình (`pipeline`) đánh giá. Bài tập này sẽ lưu mọi thứ trong notebook, nhưng bạn cũng có thể sử dụng `push_to_hub` hoặc `push_to_tensorboard` để lưu trữ các tập đánh giá để sử dụng về sau.\n",
-        "\n",
-        "### `push_to_hub`\n",
-        "\n",
-        "Lệnh này rất hữu ích nếu chúng ta đang đánh giá một mô hình và muốn lưu trữ đánh giá của nó với trọng số và cấu hình (`configs`) trên Hugging Face Hub.\n",
-        "\n",
-        "### `push_to_tensorboard`\n",
-        "\n",
-        "Lệnh này sẽ hữu ích nếu chúng ta đang xây dựng một công cụ hoặc tập lệnh đánh giá, nơi chúng ta muốn xem kết quả trong `tensorboard`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 296,
-          "referenced_widgets": [
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-          ]
-        },
-        "id": "3cUebd-z6IWs",
-        "outputId": "4b5ef2ce-04a0-4b2a-f3d7-5ecb7afb662e"
-      },
-      "outputs": [],
-      "source": [
-        "import lighteval\n",
-        "import os\n",
-        "from datetime import timedelta\n",
-        "from transformers import AutoModelForCausalLM\n",
-        "\n",
-        "from lighteval.logging.evaluation_tracker import EvaluationTracker\n",
-        "from lighteval.logging.hierarchical_logger import hlog_warn, htrack\n",
-        "from lighteval.models.model_config import create_model_config\n",
-        "from lighteval.pipeline import EnvConfig, ParallelismManager, Pipeline, PipelineParameters\n",
-        "\n",
-        "TOKEN = os.getenv(\"HF_TOKEN\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "muikmXNQXgFv"
-      },
-      "outputs": [],
-      "source": [
-        "env_config = EnvConfig(token=TOKEN, cache_dir=\"~/tmp\")\n",
-        "\n",
-        "evaluation_tracker = EvaluationTracker(\n",
-        "    output_dir=\"~/tmp\",\n",
-        "    save_details=False,\n",
-        "    push_to_hub=False,\n",
-        "    push_to_tensorboard=False,\n",
-        "    public=False,\n",
-        "    hub_results_org=False,\n",
-        ")\n",
-        "\n",
-        "pipeline_params = PipelineParameters(\n",
-        "    launcher_type=ParallelismManager.ACCELERATE,\n",
-        "    env_config=env_config,\n",
-        "    job_id=1,\n",
-        "    override_batch_size=1,\n",
-        "    num_fewshot_seeds=0,\n",
-        "    max_samples=10,\n",
-        "    use_chat_template=False,\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "nsNjwzCtltkA"
-      },
-      "source": [
-        "# So sánh các mô hình với `lighteval`\n",
-        "\n",
-        "Chúng ta sẽ so sánh hai `LLM` nhỏ trên một lĩnh vực. Chúng ta sẽ sử dụng `Qwen2.5-0.5B` và `SmolLM2-360M-Instruct` và chúng ta sẽ đánh giá chúng trên lĩnh vực y tế.\n",
-        "\n",
-        "Chúng ta có thể tạo một đánh giá lĩnh vực từ một tập hợp con các đánh giá `MMLU`, bằng cách xác định các tác vụ đánh giá. Trong `lighteval`, các tác vụ được mô tả dưới dạng chuỗi.\n",
-        "\n",
-        "`{suite}|{task}:{subtask}|{num_few_shot}|{0 or 1 to reduce num_few_shot if prompt is too long}`\n",
-        "\n",
-        "Do đó, chúng ta sẽ chuyển danh sách các tác vụ liên quan đến y học của mình như sau:\n",
-        "\n",
-        "```\n",
-        "\"leaderboard|mmlu:anatomy|5|0,leaderboard|mmlu:professional_medicine|5|0,leaderboard|mmlu:high_school_biology|5|0,leaderboard|mmlu:high_school_chemistry|5|0\"\n",
-        "```\n",
-        "\n",
-        "Có thể được hiểu như sau:\n",
-        "\n",
-        "| Suite | Tác vụ | Số mẫu Fewshot | Giới hạn Fewshots |\n",
-        "|---|---|---|---|\n",
-        "| leaderboard | mmlu:anatomy | 5 | False |\n",
-        "| leaderboard | mmlu:professional_medicine | 5 | False |\n",
-        "| leaderboard | mmlu:high_school_biology | 5 | False |\n",
-        "| leaderboard | mmlu:high_school_chemistry | 5 | False |\n",
-        "\n",
-        "Để có danh sách đầy đủ các tác vụ được `lighteval` hỗ trợ. Hãy xem trang này trong [tài liệu](https://github.com/huggingface/lighteval/wiki/Available-Tasks)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "qTqsizv9mVbO"
-      },
-      "outputs": [],
-      "source": [
-        "domain_tasks = \"leaderboard|mmlu:anatomy|5|0,leaderboard|mmlu:professional_medicine|5|0,leaderboard|mmlu:high_school_biology|5|0,leaderboard|mmlu:high_school_chemistry|5|0\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "XwcJklSFX4H6"
-      },
-      "source": [
-        "# Đánh giá Qwen2.5 0.5B"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
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-        "id": "PXJuaXVxUNBO",
-        "outputId": "87aca361-5bd5-4b12-9097-3e97f1165d7b"
-      },
-      "outputs": [],
-      "source": [
-        "qwen_model = AutoModelForCausalLM.from_pretrained(\"Qwen/Qwen2.5-0.5B\")\n",
-        "\n",
-        "pipeline = Pipeline(\n",
-        "    tasks=domain_tasks,\n",
-        "    pipeline_parameters=pipeline_params,\n",
-        "    evaluation_tracker=evaluation_tracker,\n",
-        "    model=qwen_model\n",
-        ")\n",
-        "\n",
-        "pipeline.evaluate()\n",
-        "\n",
-        "qwen_results = pipeline.get_results()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "CIwCaCxJX_hA"
-      },
-      "source": [
-        "# Đánh giá SmolLM 360M"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
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-        },
-        "id": "Dxg0RtlNVT4y",
-        "outputId": "ee40393c-bf62-499d-d443-e96da72411e8"
-      },
-      "outputs": [],
-      "source": [
-        "smol_model = AutoModelForCausalLM.from_pretrained(\"HuggingFaceTB/SmolLM2-360M-Instruct\")\n",
-        "\n",
-        "pipeline = Pipeline(\n",
-        "    tasks=domain_tasks,\n",
-        "    pipeline_parameters=pipeline_params,\n",
-        "    evaluation_tracker=evaluation_tracker,\n",
-        "    model=smol_model\n",
-        ")\n",
-        "\n",
-        "pipeline.evaluate()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "pdjyzfKHVt52"
-      },
-      "outputs": [],
-      "source": [
-        "smol_results = pipeline.get_results()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "eugvMFfgV1VD",
-        "outputId": "62956e3f-29c8-4493-f868-4ba63b7d8e96"
-      },
-      "outputs": [],
-      "source": [
-        "pipeline.show_results()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0HD8aFwSYGHu"
-      },
-      "source": [
-        "# Trực quan hóa kết quả\n",
-        "\n",
-        "Bây giờ chúng ta đã có kết quả từ hai mô hình, chúng ta có thể trực quan hóa chúng cạnh nhau. Chúng ta sẽ giữ cho việc trực quan hóa đơn giản ở đây, nhưng với cấu trúc dữ liệu này, bạn có thể biểu diễn điểm số theo nhiều cách."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 447
-        },
-        "id": "1sReqrgQUO9r",
-        "outputId": "9a0779e1-c278-48ab-9ecc-5055028ea3fd"
-      },
-      "outputs": [],
-      "source": [
-        "import pandas as pd\n",
-        "\n",
-        "df = pd.DataFrame.from_records(smol_results[\"results\"]).T[\"acc\"].rename(\"SmolLM2-360M-Instruct\")\n",
-        "_df = pd.DataFrame.from_records(qwen_results[\"results\"]).T[\"acc\"].rename(\"Qwen2-0.5B-DPO\")\n",
-        "df = pd.concat([df, _df], axis=1)\n",
-        "df.plot(kind=\"barh\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "qJEbQeYDplKX"
-      },
-      "source": [
-        "# 💐 Chúc mừng bạn đã hoàn thành!\n",
-        "\n",
-        "Bạn đã có một notebook tiện dụng để đánh giá các mô hình. Bạn có thể sử dụng notebook này để:\n",
-        "\n",
-        "- chọn mô hình phù hợp cho trường hợp sử dụng suy luận của bạn\n",
-        "- đánh giá các điểm kiểm tra trong quá trình đào tạo\n",
-        "- chia sẻ điểm số mô hình"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jWdS38syaipm"
-      },
-      "source": [
-        "🏃Các bước tiếp theo\n",
-        "\n",
-        "- Nếu bạn muốn đi sâu hơn vào kết quả đánh giá của mình, hãy xem [sổ tay này](https://github.com/huggingface/evaluation-guidebook/blob/main/contents/examples/comparing_task_formulations.ipynb)"
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "gpuType": "A100",
-      "machine_shape": "hm",
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
-    },
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diff --git a/vi/4_evaluation/project/README.md b/vi/4_evaluation/project/README.md
deleted file mode 100644
index 4e47e8e5..00000000
--- a/vi/4_evaluation/project/README.md
+++ /dev/null
@@ -1,83 +0,0 @@
-# Đánh Giá Theo Lĩnh Vực Cụ Thể với Argilla, Distilabel, và LightEval
-
-Hầu hết các bộ tiêu chuẩn (`benchmark`) phổ biến đều xem xét các khả năng rất chung chung (lý luận, toán học, lập trình), nhưng bạn đã bao giờ cần nghiên cứu các khả năng cụ thể hơn chưa?
-
-Bạn nên làm gì nếu bạn cần đánh giá một mô hình trên một **lĩnh vực cụ thể** liên quan đến các trường hợp sử dụng của bạn? (Ví dụ: các trường hợp sử dụng tài chính, pháp lý, y tế)
-
-Hướng dẫn này chỉ cho bạn toàn bộ quy trình (`pipeline`) mà bạn có thể làm theo, từ việc tạo dữ liệu liên quan và chú thích các mẫu của bạn đến việc đánh giá mô hình của bạn trên chúng, với các công cụ dễ sử dụng [Argilla](https://github.com/argilla-io/argilla), [distilabel](https://github.com/argilla-io/distilabel), và [lighteval](https://github.com/huggingface/lighteval). Trong ví dụ của chúng tôi, chúng tôi sẽ tập trung vào việc tạo các câu hỏi đánh giá từ nhiều tài liệu.
-
-## Cấu trúc dự án
-
-Đối với quy trình của chúng tôi, chúng tôi sẽ làm theo 4 bước, với một tập lệnh cho mỗi bước: tạo tập dữ liệu, chú thích nó, trích xuất các mẫu liên quan để đánh giá từ nó và thực sự đánh giá các mô hình.
-
-| Tên tập lệnh | Mô tả |
-|-------------|-------------|
-| generate_dataset.py | Tạo các câu hỏi đánh giá từ nhiều tài liệu văn bản bằng cách sử dụng một mô hình ngôn ngữ được chỉ định. |
-| annotate_dataset.py | Tạo tập dữ liệu Argilla để chú thích thủ công các câu hỏi đánh giá được tạo. |
-| create_dataset.py | Xử lý dữ liệu đã chú thích từ Argilla và tạo tập dữ liệu Hugging Face. |
-| evaluation_task.py | Định nghĩa một tác vụ LightEval tùy chỉnh để đánh giá các mô hình ngôn ngữ trên tập dữ liệu câu hỏi đánh giá. |
-
-## Các bước thực hành
-
-### 1. Tạo tập dữ liệu
-
-Tập lệnh `generate_dataset.py` sử dụng thư viện `distilabel` để tạo các câu hỏi thi dựa trên nhiều tài liệu văn bản. Nó sử dụng mô hình được chỉ định (mặc định: Meta-Llama-3.1-8B-Instruct) để tạo các câu hỏi, câu trả lời đúng và câu trả lời sai (được gọi là câu gây nhiễu). Bạn nên thêm các mẫu dữ liệu của riêng bạn và bạn có thể muốn sử dụng một mô hình khác.
-
-Để chạy quá trình tạo:
-
-```sh
-python generate_dataset.py --input_dir path/to/your/documents --model_id your_model_id --output_path output_directory
-```
-
-Thao tác này sẽ tạo một [Distiset](https://distilabel.argilla.io/dev/sections/how_to_guides/advanced/distiset/) chứa các câu hỏi thi được tạo cho tất cả các tài liệu trong thư mục đầu vào.
-
-### 2. Chú thích tập dữ liệu
-
-Tập lệnh `annotate_dataset.py` lấy các câu hỏi đã tạo và tạo tập dữ liệu Argilla để chú thích. Nó thiết lập cấu trúc tập dữ liệu và điền vào đó các câu hỏi và câu trả lời đã tạo, sắp xếp ngẫu nhiên thứ tự các câu trả lời để tránh sai lệch. Khi ở trong Argilla, bạn hoặc một chuyên gia trong lĩnh vực có thể xác thực tập dữ liệu với các câu trả lời đúng.
-
-Bạn sẽ thấy các câu trả lời đúng được đề xuất từ `LLM` theo thứ tự ngẫu nhiên và bạn có thể phê duyệt câu trả lời đúng hoặc chọn một câu trả lời khác. Thời gian của quá trình này sẽ phụ thuộc vào quy mô của tập dữ liệu đánh giá của bạn, độ phức tạp của dữ liệu lĩnh vực của bạn và chất lượng của `LLM` của bạn. Ví dụ: chúng tôi đã có thể tạo 150 mẫu trong vòng 1 giờ trên lĩnh vực chuyển giao học tập (`transfer learning`), sử dụng Llama-3.1-70B-Instruct, chủ yếu bằng cách phê duyệt câu trả lời đúng và loại bỏ những câu trả lời không chính xác.
-
-Để chạy quá trình chú thích:
-
-```sh
-python annotate_dataset.py --dataset_path path/to/distiset --output_dataset_name argilla_dataset_name
-```
-
-Thao tác này sẽ tạo một tập dữ liệu Argilla có thể được sử dụng để xem xét và chú thích thủ công.
-
-![argilla_dataset](./images/domain_eval_argilla_view.png)
-
-Nếu bạn không sử dụng Argilla, hãy triển khai cục bộ hoặc trên không gian (`spaces`) theo [hướng dẫn bắt đầu nhanh](https://docs.argilla.io/latest/getting_started/quickstart/) này.
-
-### 3. Tạo tập dữ liệu
-
-Tập lệnh `create_dataset.py` xử lý dữ liệu đã chú thích từ Argilla và tạo tập dữ liệu Hugging Face. Nó xử lý cả các câu trả lời được đề xuất và được chú thích thủ công. Tập lệnh sẽ tạo một tập dữ liệu với câu hỏi, các câu trả lời có thể và tên cột cho câu trả lời đúng. Để tạo tập dữ liệu cuối cùng:
-
-```sh
-huggingface_hub login
-python create_dataset.py --dataset_path argilla_dataset_name --dataset_repo_id your_hf_repo_id
-```
-
-Thao tác này sẽ đẩy tập dữ liệu lên Hugging Face Hub dưới kho lưu trữ được chỉ định. Bạn có thể xem tập dữ liệu mẫu trên `hub` [tại đây](https://huggingface.co/datasets/burtenshaw/exam_questions/viewer/default/train) và bản xem trước của tập dữ liệu trông như thế này:
-
-![hf_dataset](./images/domain_eval_dataset_viewer.png)
-
-### 4. Tác vụ đánh giá
-
-Tập lệnh `evaluation_task.py` định nghĩa một tác vụ LightEval tùy chỉnh để đánh giá các mô hình ngôn ngữ trên tập dữ liệu câu hỏi thi. Nó bao gồm một hàm nhắc nhở (`prompt function`), một chỉ số chính xác tùy chỉnh và cấu hình tác vụ.
-
-Để đánh giá một mô hình bằng `lighteval` với tác vụ câu hỏi thi tùy chỉnh:
-
-```sh
-lighteval accelerate \
-    --model_args "pretrained=HuggingFaceH4/zephyr-7b-beta" \
-    --tasks "community|exam_questions|0|0" \
-    --custom_tasks domain-eval/evaluation_task.py \
-    --output_dir "./evals"
-```
-
-Bạn có thể tìm thấy các hướng dẫn chi tiết trong `lighteval wiki` về từng bước sau:
-
-- [Tạo tác vụ tùy chỉnh](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
-- [Tạo chỉ số tùy chỉnh](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
-- [Sử dụng các chỉ số hiện có](https://github.com/huggingface/lighteval/wiki/Metric-List)
\ No newline at end of file
diff --git a/vi/4_evaluation/project/annotate_dataset.py b/vi/4_evaluation/project/annotate_dataset.py
deleted file mode 100644
index dadffc10..00000000
--- a/vi/4_evaluation/project/annotate_dataset.py
+++ /dev/null
@@ -1,129 +0,0 @@
-import argparse
-import json
-from random import choices, sample
-
-import argilla as rg
-from distilabel.distiset import Distiset
-
-################################################################################
-# Script Parameters
-################################################################################
-
-parser = argparse.ArgumentParser(
-    description="Annotate exam questions dataset using Argilla."
-)
-parser.add_argument(
-    "--argilla_api_key",
-    type=str,
-    default="argilla.apikey",
-    help="API key for Argilla",
-)
-parser.add_argument(
-    "--argilla_api_url",
-    type=str,
-    default="http://localhost:6900",
-    help="API URL for Argilla",
-)
-parser.add_argument(
-    "--dataset_path",
-    type=str,
-    default="exam_questions",
-    help="Path to the exam questions dataset",
-)
-parser.add_argument(
-    "--dataset_config",
-    type=str,
-    default="default",
-    help="Configuration of the dataset",
-)
-parser.add_argument(
-    "--dataset_split",
-    type=str,
-    default="train",
-    help="Split of the dataset to use",
-)
-parser.add_argument(
-    "--output_dataset_name",
-    type=str,
-    default="exam_questions",
-    help="Name of the output Argilla dataset",
-)
-
-args = parser.parse_args()
-
-################################################################################
-# Create Argilla dataset with the feedback task for validation
-################################################################################
-
-client = rg.Argilla(api_key=args.argilla_api_key, api_url=args.argilla_api_url)
-
-if client.datasets(args.output_dataset_name):
-    print(f"Deleting existing dataset '{args.output_dataset_name}'")
-    client.datasets(args.output_dataset_name).delete()
-
-settings = rg.Settings(
-    fields=[
-        rg.TextField("question"),
-        rg.TextField("answer_a"),
-        rg.TextField("answer_b"),
-        rg.TextField("answer_c"),
-        rg.TextField("answer_d"),
-    ],
-    questions=[
-        rg.LabelQuestion(
-            name="correct_answer",
-            labels=["answer_a", "answer_b", "answer_c", "answer_d"],
-        ),
-        rg.TextQuestion(
-            name="improved_question",
-            description="Could you improve the question?",
-        ),
-        rg.TextQuestion(
-            name="improved_answer",
-            description="Could you improve the best answer?",
-        ),
-    ],
-)
-
-dataset = rg.Dataset(settings=settings, name=args.output_dataset_name)
-dataset.create()
-
-################################################################################
-# Load the Distiset and process and add records to Argilla dataset
-# We will validate that questions appear in random order to avoid bias
-# but we will show correct answers in the Argilla UI as suggestions.
-################################################################################
-
-distiset = Distiset.load_from_disk(args.dataset_path)
-answer_names = ["answer_a", "answer_b", "answer_c", "answer_d"]
-dataset_records = []
-
-for exam in distiset[args.dataset_config][args.dataset_split]:
-    exam_json = json.loads(exam["generation"])["exam"]
-
-    for question in exam_json:
-        answer = question["answer"]
-        distractors = question["distractors"]
-        distractors = choices(distractors, k=3)
-        answers = distractors + [answer]
-        answers = sample(answers, len(answers))
-        suggestion_idx = answers.index(answer)
-        fields = dict(zip(answer_names, answers))
-        fields["question"] = question["question"]
-
-        record = rg.Record(
-            fields=fields,
-            suggestions=[
-                rg.Suggestion(
-                    question_name="correct_answer",
-                    value=answer_names[suggestion_idx],
-                )
-            ],
-        )
-        dataset_records.append(record)
-
-dataset.records.log(dataset_records)
-
-print(
-    f"Dataset '{args.output_dataset_name}' has been created and populated in Argilla."
-)
diff --git a/vi/4_evaluation/project/create_dataset.py b/vi/4_evaluation/project/create_dataset.py
deleted file mode 100644
index 822e268e..00000000
--- a/vi/4_evaluation/project/create_dataset.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import argparse
-
-import argilla as rg
-from datasets import Dataset
-
-################################################################################
-# Script Parameters
-################################################################################
-
-parser = argparse.ArgumentParser(
-    description="Create a Hugging Face dataset from annotated Argilla data."
-)
-parser.add_argument(
-    "--argilla_api_key",
-    type=str,
-    default="argilla.apikey",
-    help="API key for Argilla",
-)
-parser.add_argument(
-    "--argilla_api_url",
-    type=str,
-    default="http://localhost:6900",
-    help="API URL for Argilla",
-)
-parser.add_argument(
-    "--dataset_path",
-    type=str,
-    default="exam_questions",
-    help="Path to the Argilla dataset",
-)
-parser.add_argument(
-    "--dataset_repo_id",
-    type=str,
-    default="burtenshaw/exam_questions",
-    help="Hugging Face dataset repository ID",
-)
-
-args = parser.parse_args()
-
-################################################################################
-# Initialize Argilla client and load dataset
-################################################################################
-
-client = rg.Argilla(api_key=args.argilla_api_key, api_url=args.argilla_api_url)
-dataset = client.datasets(args.dataset_path)
-
-################################################################################
-# Process Argilla records
-################################################################################
-
-dataset_rows = []
-
-for record in dataset.records(with_suggestions=True, with_responses=True):
-    row = record.fields
-
-    if len(record.responses) == 0:
-        answer = record.suggestions["correct_answer"].value
-        row["correct_answer"] = answer
-    else:
-        for response in record.responses:
-            if response.question_name == "correct_answer":
-                row["correct_answer"] = response.value
-    dataset_rows.append(row)
-
-################################################################################
-# Create Hugging Face dataset and push to Hub
-################################################################################
-
-hf_dataset = Dataset.from_list(dataset_rows)
-hf_dataset.push_to_hub(repo_id=args.dataset_repo_id)
-
-print(f"Dataset has been successfully pushed to {args.dataset_repo_id}")
diff --git a/vi/4_evaluation/project/evaluation_task.py b/vi/4_evaluation/project/evaluation_task.py
deleted file mode 100644
index 397c75ad..00000000
--- a/vi/4_evaluation/project/evaluation_task.py
+++ /dev/null
@@ -1,86 +0,0 @@
-import numpy as np
-
-from lighteval.tasks.lighteval_task import LightevalTaskConfig
-from lighteval.tasks.requests import Doc
-from lighteval.metrics.utils.metric_utils import (
-    SampleLevelMetric,
-    MetricCategory,
-    MetricUseCase,
-)
-
-################################################################################
-# Define the prompt function based on the structure of the dataset
-################################################################################
-
-
-def prompt_fn(line, task_name: str = None):
-    """Converts a dataset line to a Doc object for evaluation."""
-    instruction = "Choose the correct answer for the following exam question:"
-    return Doc(
-        task_name=task_name,
-        query=f"{instruction} {line['question']}",
-        choices=[
-            f" {line['answer_a']}",
-            f" {line['answer_b']}",
-            f" {line['answer_c']}",
-            f" {line['answer_d']}",
-        ],
-        gold_index=["answer_a", "answer_b", "answer_c", "answer_d"].index(
-            line["correct_answer"]
-        ),
-        instruction=instruction,
-    )
-
-
-################################################################################
-# Define the custom metric based on guide here https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric
-# Or use an existing metric based on the guide here: https://github.com/huggingface/lighteval/wiki/Metric-List
-# Existing metrics can be imported from lighteval.metrics.metrics
-################################################################################
-
-
-def sample_level_fn(formatted_doc: Doc, **kwargs) -> bool:
-    response = np.argmin(kwargs["choices_logprob"])
-    return response == formatted_doc.gold_index
-
-
-custom_metric = SampleLevelMetric(
-    metric_name="exam_question_accuracy",
-    higher_is_better=True,
-    category=MetricCategory.MULTICHOICE,
-    use_case=MetricUseCase.NONE,
-    sample_level_fn=sample_level_fn,
-    corpus_level_fn=np.mean,
-)
-
-################################################################################
-# Define the task based on the prompt function and the custom metric
-# Based on the guide here: https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task
-################################################################################
-
-task = LightevalTaskConfig(
-    name="example",
-    prompt_function=prompt_fn,
-    suite=["community"],
-    hf_repo="burtenshaw/exam_questions",
-    hf_subset="default",
-    hf_avail_splits=["train"],
-    evaluation_splits=["train"],
-    few_shots_split=None,
-    few_shots_select=None,
-    metric=[custom_metric],
-)
-
-# Add the task to TASKS_TABLE
-TASKS_TABLE = [task]
-
-# MODULE LOGIC
-if __name__ == "__main__":
-    print([t.name for t in TASKS_TABLE])
-    print(len(TASKS_TABLE))
-
-# lighteval accelerate \
-# "pretrained=HuggingFaceTB/SmolLM2-135M-Instruct" \
-# "community|example|0|0" \
-# --custom-tasks "submitted_tasks/example.py" \
-# --output-dir "results"
diff --git a/vi/4_evaluation/project/generate_dataset.py b/vi/4_evaluation/project/generate_dataset.py
deleted file mode 100644
index cb531d3f..00000000
--- a/vi/4_evaluation/project/generate_dataset.py
+++ /dev/null
@@ -1,167 +0,0 @@
-import argparse
-import os
-from pydantic import BaseModel, Field
-from datasets import Dataset
-
-from distilabel.llms import InferenceEndpointsLLM
-from distilabel.pipeline import Pipeline
-from distilabel.steps.tasks import TextGeneration
-
-
-################################################################################
-# Script Parameters
-################################################################################
-
-parser = argparse.ArgumentParser(
-    description="Generate exam questions from text files in a directory."
-)
-parser.add_argument(
-    "--model_id",
-    type=str,
-    default="Qwen/Qwen2.5-7B-Instruct",
-    help="Model ID for text generation",
-)
-parser.add_argument(
-    "--tokenizer_id",
-    type=str,
-    default="Qwen/Qwen2.5-7B-Instruct",
-    help="Tokenizer ID for text generation",
-)
-parser.add_argument(
-    "--input_dir",
-    type=str,
-    help="Directory containing input text files",
-    default="data",
-)
-parser.add_argument(
-    "--max_new_tokens",
-    type=int,
-    default=2048,
-    help="Maximum number of new tokens to generate",
-)
-parser.add_argument(
-    "--output_path",
-    type=str,
-    default="exam_questions_output",
-    help="Directory to save the generated datasets",
-)
-
-args = parser.parse_args()
-
-################################################################################
-# Load the documents
-# We assume that the documents are in the input directory, and that each file
-# is a separate document about the same topic.
-################################################################################
-
-# Process all text files in the input directory
-documents = []
-for filename in os.listdir(args.input_dir):
-    if filename.endswith(".txt"):
-        file_path = os.path.join(args.input_dir, filename)
-        with open(file=file_path, mode="r", encoding="utf-8") as file:
-            document_content = file.read()
-            documents.append(document_content)
-
-# Create a single dataset from all document contents
-dataset = Dataset.from_dict({"document": documents})
-
-################################################################################
-# Define the prompts
-# We use a system prompt to guide the model to generate the correct output format.
-# A template is used to insert the document into the prompt.
-################################################################################
-
-SYSTEM_PROMPT = """\
-You are an exam writer specialized in writing exams for students.
-Your goal is to create questions and answers based on the document provided, 
-and a list of distractors, that are incorrect but viable answers to the question.
-Your answer must adhere to the following format:
-```
-[
-    {
-        "question": "Your question",
-        "answer": "The correct answer to the question",
-        "distractors": ["wrong answer 1", "wrong answer 2", "wrong answer 3"]
-    },
-    ... (more questions and answers as required)
-]
-```
-""".strip()
-
-INSTRUCTION_TEMPLATE = """\
-    Generate a list of answers and questions about the document. 
-    Document:\n\n{{ instruction }}"""
-
-################################################################################
-# Define the output structure
-# We define a data model for the output of the pipeline, this is used to ensure
-# that the output is in the correct format for the evaluation task.
-################################################################################
-
-
-class ExamQuestion(BaseModel):
-    question: str = Field(..., description="The question to be answered")
-    answer: str = Field(..., description="The correct answer to the question")
-    distractors: List[str] = Field(
-        ..., description="A list of incorrect but viable answers to the question"
-    )
-
-
-class ExamQuestions(BaseModel):
-    exam: List[ExamQuestion]
-
-
-################################################################################
-# Create the pipeline
-# We create a pipeline with a single task that generates the exam questions
-# based on the document and in the correct format. We will Hugging Face
-# InferenceEndpoints and the model specified in the arguments.
-################################################################################
-
-with Pipeline(
-    name="Domain-Eval-Questions",
-    description="Generate exam questions based on given documents.",
-) as pipeline:
-    # Set up the text generation task
-    text_generation = TextGeneration(
-        name="exam_generation",
-        llm=InferenceEndpointsLLM(
-            model_id=args.model_id,
-            tokenizer_id=args.model_id,
-            api_key=os.environ["HF_TOKEN"],
-            structured_output={
-                "schema": ExamQuestions.model_json_schema(),
-                "format": "json",
-            },
-        ),
-        input_batch_size=8,
-        output_mappings={"model_name": "generation_model"},
-        input_mappings={"instruction": "document"},
-        system_prompt=SYSTEM_PROMPT,
-        template=INSTRUCTION_TEMPLATE,
-    )
-
-
-################################################################################
-# Run the pipeline
-# We run the pipeline for all documents and save the results to the output path.
-################################################################################
-
-if __name__ == "__main__":
-    # Run the pipeline for all documents
-    distiset = pipeline.run(
-        parameters={
-            "exam_generation": {
-                "llm": {
-                    "generation_kwargs": {
-                        "max_new_tokens": args.max_new_tokens,
-                    }
-                }
-            }
-        },
-        use_cache=False,
-        dataset=dataset,
-    )
-
-    distiset.save_to_disk(args.output_path)
diff --git a/vi/4_evaluation/project/images/domain_eval_argilla_view.png b/vi/4_evaluation/project/images/domain_eval_argilla_view.png
deleted file mode 100644
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diff --git a/vi/5_vision_language_models/README.md b/vi/5_vision_language_models/README.md
deleted file mode 100644
index 2f04c1f6..00000000
--- a/vi/5_vision_language_models/README.md
+++ /dev/null
@@ -1,41 +0,0 @@
-# Mô hình Ngôn ngữ Thị giác
-
-## 1. Sử dụng Mô hình Ngôn ngữ Thị giác (Vision Language Models)
-
-Mô hình Ngôn ngữ Thị giác (VLMs) xử lý đầu vào hình ảnh cùng với văn bản để thực hiện các tác vụ như chú thích của ảnh, trả lời câu hỏi bằng hình ảnh và suy luận đa phương thức (multimodal).
-
-Một kiến trúc VLM điển hình bao gồm:
-1. Bộ mã hóa hình ảnh (*image encoder*) để trích xuất các đặc trưng thị giác
-2. Lớp chiếu (*projection layer*) để căn chỉnh các biểu diễn thị giác với văn bản
-3. Mô hình ngôn ngữ để xử lý hoặc tạo văn bản. Điều này cho phép mô hình thiết lập các kết nối giữa các yếu tố về thị giác và các khái niệm trong ngôn ngữ.
-
-Tùy thuộc vào từng trường hợp mà có thể sử dụng các VLMs được huấn luyện theo các tác vụ khác nhau. Các mô hình cơ sở (base models) xử lý các tác vụ thị giác-ngôn ngữ tổng quát, trong khi các biến thể tối ưu hóa cho trò chuyện (chat-optimized variants) hỗ trợ các tương tác hội thoại. Một số mô hình bao gồm các thành phần bổ sung để làm rõ dự đoán dựa trên các bằng chứng thị giác (*visual evidence*) hoặc chuyên về các tác vụ cụ thể như phát hiện đối tượng (*object detection*).
-
-Để biết thêm chi tiết về kỹ thuật và cách sử dụng VLMs, hãy tham khảo trang [Sử dụng VLM](./vlm_usage.md).
-
-## 2. Tinh chỉnh Mô hình Ngôn ngữ Thị giác (VLM)
-
-Tinh chỉnh VLM là việc điều chỉnh một mô hình đã được huấn luyện trước (*pre-trained*) để thực hiện các tác vụ cụ thể hoặc để hoạt động hiệu quả trên một tập dữ liệu cụ thể. Quá trình này có thể tuân theo các phương pháp như tinh chỉnh có giám sát (*supervised fine-tuning*), tối ưu hóa tùy chọn (*preference optimization*) hoặc phương pháp kết hợp (*hybrid approach*) cả hai, như đã giới thiệu trong Chương 1 và Chương 2.
-
-Mặc dù các công cụ và kỹ thuật cốt lõi vẫn tương tự như các công cụ và kỹ thuật được sử dụng cho các mô hình ngôn ngữ (LLMs), việc tinh chỉnh VLMs đòi hỏi phải tập trung nhiều hơn vào việc biểu diễn và chuẩn bị dữ liệu cho hình ảnh. Điều này đảm bảo mô hình tích hợp và xử lý hiệu quả cả dữ liệu thị giác và văn bản để đạt hiệu suất tối ưu. Vì mô hình demo, SmolVLM, lớn hơn đáng kể so với mô hình ngôn ngữ được sử dụng trong bài trước, điều cần thiết là phải khám phá các phương pháp tinh chỉnh hiệu quả. Các kỹ thuật như lượng tử hóa (*quantization*) và Tinh chỉnh hiệu quả tham số - PEFT (*Parameter-Efficient Fine-Tuning*) có thể giúp làm cho quá trình này dễ tiếp cận hơn và tiết kiệm chi phí hơn, cho phép nhiều người dùng thử nghiệm với mô hình hơn.
-
-Để được hướng dẫn chi tiết về tinh chỉnh VLMs, hãy truy cập trang [Tinh chỉnh VLM](./vlm_finetuning.md).
-
-## Bài tập thực hành
-
-| Tiêu đề | Mô tả | Bài tập | Link | Colab |
-|-------|-------------|----------|------|-------|
-| Sử dụng VLM | Tìm hiểu cách tải và sử dụng VLM đã được huấn luyện trước cho các tác vụ khác nhau | 🐢 Xử lý một hình ảnh<br>🐕 Xử lý nhiều hình ảnh với xử lý hàng loạt <br>🦁 Xử lý toàn bộ video| [Notebook](./notebooks/vlm_usage_sample.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/5_vision_language_models/notebooks/vlm_usage_sample.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-| Tinh chỉnh VLM | Tìm hiểu cách tinh chỉnh VLM đã được huấn luyện trước cho các tập dữ liệu theo từng nhiệm vụ | 🐢 Sử dụng tập dữ liệu cơ bản để tinh chỉnh<br>🐕 Thử tập dữ liệu mới<br>🦁 Thử nghiệm với các phương pháp tinh chỉnh thay thế | [Notebook](./notebooks/vlm_sft_sample.ipynb)| <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/5_vision_language_models/notebooks/vlm_sft_sample.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-
-## Tài liệu tham khảo
-
-- [Hugging Face Learn: Tinh chỉnh có giám sát VLMs](https://huggingface.co/learn/cookbook/fine_tuning_vlm_trl)
-- [Hugging Face Learn: Tinh chỉnh có giám sát SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_smol_vlm_sft_trl)
-- [Hugging Face Learn: Tinh chỉnh tối ưu hóa tùy chọn SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_vlm_dpo_smolvlm_instruct)
-- [Hugging Face Blog: Tối ưu hóa tùy chọn cho VLMs](https://huggingface.co/blog/dpo_vlm)
-- [Hugging Face Blog: Mô hình Ngôn ngữ Thị giác](https://huggingface.co/blog/vlms)
-- [Hugging Face Blog: SmolVLM](https://huggingface.co/blog/smolvlm)
-- [Hugging Face Model: SmolVLM-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct)
-- [CLIP: Learning Transferable Visual Models from Natural Language Supervision](https://arxiv.org/abs/2103.00020)
-- [Align Before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://arxiv.org/abs/2107.07651)
diff --git a/vi/5_vision_language_models/images/VLM_Architecture.png b/vi/5_vision_language_models/images/VLM_Architecture.png
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diff --git a/vi/5_vision_language_models/notebooks/vlm_sft_sample.ipynb b/vi/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
deleted file mode 100644
index 65fb7301..00000000
--- a/vi/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
+++ /dev/null
@@ -1,458 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tinh chỉnh Mô hình Ngôn ngữ Thị giác (VLM)\n",
-    "\n",
-    "Notebook này minh họa cách tinh chỉnh mô hình `HuggingFaceTB/SmolVLM-Instruct` bằng cách sử dụng `SFTTrainer` từ thư viện `trl`. Các ô của notebook sẽ chạy và tinh chỉnh mô hình. Bạn có thể chọn mức độ khó bằng cách thử các tập dữ liệu khác nhau.\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "<h2 style='margin: 0;color:blue'>Bài tập: Tinh chỉnh SmolVLM với SFTTrainer</h2>\n",
-    "<p>Hãy chọn một tập dữ liệu từ Hugging Face Hub và tinh chỉnh một mô hình trên đó.</p>\n",
-    "<p><b>Các mức độ khó</b></p>\n",
-    "<p>🐢 Sử dụng tập dữ liệu HuggingFaceM4/ChartQA để chạy huấn luyện SFT.</p>\n",
-    "<p>🐕 Sử dụng mô hình đã được tinh chỉnh để tạo ra phản hồi và cải thiện dựa trên ví dụ cơ bản.</p>\n",
-    "<p>🦁 Thử các tập dữ liệu khác và cho thấy sự cải thiện.</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Cài đặt các thư viện cần thiết trong Google Colab\n",
-    "# !pip install transformers datasets trl huggingface_hub\n",
-    "\n",
-    "# Xác thực với Hugging Face\n",
-    "from huggingface_hub import login\n",
-    "login()\n",
-    "\n",
-    "# Để thuận tiện, bạn có thể tạo một biến môi trường chứa token Hugging Face của mình là HF_TOKEN"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Nhập các thư viện cần thiết\n",
-    "\n",
-    "import torch\n",
-    "import os\n",
-    "from transformers import AutoProcessor, AutoModelForVision2Seq, BitsAndBytesConfig\n",
-    "from transformers.image_utils import load_image"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`low_cpu_mem_usage` was None, now default to True since model is quantized.\n",
-      "You shouldn't move a model that is dispatched using accelerate hooks.\n",
-      "Some kwargs in processor config are unused and will not have any effect: image_seq_len. \n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "quantization_config = BitsAndBytesConfig(load_in_4bit=True, \n",
-    "                                         bnb_4bit_use_double_quant=True, \n",
-    "                                         bnb_4bit_quant_type=\"nf4\", \n",
-    "                                         bnb_4bit_compute_dtype=torch.bfloat16)\n",
-    "model_name = \"HuggingFaceTB/SmolVLM-Instruct\"\n",
-    "model = AutoModelForVision2Seq.from_pretrained(\n",
-    "    model_name,\n",
-    "    quantization_config=quantization_config,\n",
-    "    torch_dtype=torch.bfloat16,\n",
-    ").to(device)\n",
-    "processor = AutoProcessor.from_pretrained(\"HuggingFaceTB/SmolVLM-Instruct\")\n",
-    "\n",
-    "# Đặt tên cho bản tinh chỉnh để lưu và tải lên\n",
-    "finetune_name = \"SmolVLM-FT-MyDataset\"\n",
-    "finetune_tags = [\"smol-course\", \"module_5\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Chuẩn bị tập dữ liệu\n",
-    "\n",
-    "Chúng ta sẽ tải một tập dữ liệu mẫu và định dạng nó để huấn luyện. Tập dữ liệu phải được cấu trúc với các cặp đầu vào-đầu ra, trong đó mỗi đầu vào là một lời nhắc (prompt) và đầu ra là phản hồi mong đợi từ mô hình.\n",
-    "\n",
-    "**TRL sẽ định dạng các thông báo đầu vào dựa trên các mẫu trò chuyện (chat templates) của mô hình.** Chúng cần được biểu diễn dưới dạng danh sách các từ điển với các khóa: `role` và `content`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['image', 'query', 'label', 'human_or_machine'],\n",
-       "        num_rows: 28299\n",
-       "    })\n",
-       "    val: Dataset({\n",
-       "        features: ['image', 'query', 'label', 'human_or_machine'],\n",
-       "        num_rows: 1920\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['image', 'query', 'label', 'human_or_machine'],\n",
-       "        num_rows: 2500\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Tải tập dữ liệu mẫu\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "# TODO: xác định tập dữ liệu và cấu hình của bạn bằng cách sử dụng các tham số đường dẫn và tên\n",
-    "dataset_name = \"HuggingFaceM4/ChartQA\"\n",
-    "ds = load_dataset(path=dataset_name)\n",
-    "ds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=314x281 at 0x7D354DDE73D0>, 'query': 'What is the sum favourable  value in the year 2014 and 2015?', 'label': ['95'], 'human_or_machine': 0, 'image_size': ((314, 281),)}\n",
-      "Query: What is the sum favourable  value in the year 2014 and 2015?\n",
-      "Expected Answer: 95\n",
-      "Model Prediction: User:<image>What is the sum favourable  value in the year 2014 and 2015?\n",
-      "Answer: 91.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "sample = ds['train'][3]\n",
-    "\n",
-    "# Hiển thị cấu trúc của một mẫu đơn\n",
-    "sample['image_size'] = sample['image'].size,\n",
-    "\n",
-    "# Trực quan hóa hình ảnh và siêu dữ liệu liên quan\n",
-    "plt.imshow(sample['image'])\n",
-    "plt.axis(\"off\")\n",
-    "plt.title(\"Hình ảnh biểu đồ mẫu\")\n",
-    "plt.show()\n",
-    "\n",
-    "print(sample)\n",
-    "\n",
-    "# Tiền xử lý mẫu\n",
-    "prompt = [{\"role\": \"user\", \"content\": [{\"type\": \"image\"}, {\"type\": \"text\", \"text\": sample[\"query\"]}]}]\n",
-    "formatted_query = processor.apply_chat_template(prompt, tokenize=False)\n",
-    "\n",
-    "inputs = processor(\n",
-    "    images=sample[\"image\"], \n",
-    "    text=formatted_query, \n",
-    "    return_tensors=\"pt\"\n",
-    ").to(device)\n",
-    "inputs = {key: val.to(device, dtype=torch.bfloat16) if val.dtype == torch.float else val.to(device) for key, val in inputs.items()}\n",
-    "\n",
-    "# Tạo dự đoán\n",
-    "with torch.no_grad():\n",
-    "    outputs = model.generate(**inputs,\n",
-    "                             max_length=1600)\n",
-    "\n",
-    "# Giải mã dự đoán\n",
-    "prediction = processor.batch_decode(outputs, skip_special_tokens=True)\n",
-    "\n",
-    "# Hiển thị kết quả\n",
-    "print(f\"Truy vấn: {sample['query']}\")\n",
-    "print(f\"Câu trả lời mong đợi: {sample['label'][0]}\")\n",
-    "print(f\"Dự đoán của mô hình: {prediction[0]}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Thiết lập LoRA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "trainable params: 2,568,192 || all params: 2,248,841,072 || trainable%: 0.1142\n"
-     ]
-    }
-   ],
-   "source": [
-    "from peft import LoraConfig, get_peft_model\n",
-    "\n",
-    "# Cấu hình LoRA\n",
-    "peft_config = LoraConfig(\n",
-    "    lora_alpha=16,\n",
-    "    lora_dropout=0.05,\n",
-    "    r=8,\n",
-    "    bias=\"none\",\n",
-    "    target_modules=[\"q_proj\", \"v_proj\"],\n",
-    "    task_type=\"CAUSAL_LM\",\n",
-    ")\n",
-    "\n",
-    "# Áp dụng điều chỉnh mô hình PEFT\n",
-    "peft_model = get_peft_model(model, peft_config)\n",
-    "\n",
-    "# In các tham số có thể huấn luyện\n",
-    "peft_model.print_trainable_parameters()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Cấu hình Trình huấn luyện (Trainer)\n",
-    "\n",
-    "`Trainer` được định cấu hình với nhiều tham số khác nhau để kiểm soát quá trình huấn luyện. Chúng bao gồm số bước huấn luyện, kích thước batch, tốc độ học và chiến lược đánh giá. Điều chỉnh các tham số này dựa trên các yêu cầu cụ thể và tài nguyên tính toán của bạn."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def collate_fn(examples):\n",
-    "    # Mẫu thông báo hệ thống cho VLM\n",
-    "    system_message = \"\"\"Bạn là một Mô hình Ngôn ngữ Thị giác chuyên giải thích dữ liệu trực quan từ hình ảnh biểu đồ.\n",
-    "    Nhiệm vụ của bạn là phân tích hình ảnh biểu đồ được cung cấp và trả lời các truy vấn bằng các câu trả lời ngắn gọn, thường là một từ, số hoặc cụm từ ngắn.\n",
-    "    Các biểu đồ bao gồm nhiều loại khác nhau (ví dụ: biểu đồ đường, biểu đồ cột) và chứa màu sắc, nhãn và văn bản.\n",
-    "    Tập trung vào việc cung cấp các câu trả lời chính xác, ngắn gọn dựa trên thông tin trực quan. Tránh giải thích thêm trừ khi thực sự cần thiết.\"\"\"\n",
-    "\n",
-    "    # Khởi tạo danh sách cho đầu vào văn bản và hình ảnh\n",
-    "    text_inputs = []\n",
-    "    image_inputs = []\n",
-    "\n",
-    "    # Xử lý tất cả các ví dụ trong một vòng lặp\n",
-    "    for example in examples:\n",
-    "        # Định dạng cấu trúc trò chuyện cho bộ xử lý\n",
-    "        formatted_example = {\n",
-    "            \"messages\": [\n",
-    "                {\n",
-    "                    \"role\": \"system\",\n",
-    "                    \"content\": [{\"type\": \"text\", \"text\": system_message}],\n",
-    "                },\n",
-    "                {\n",
-    "                    \"role\": \"user\",\n",
-    "                    \"content\": [\n",
-    "                        {\n",
-    "                            \"type\": \"image\",\n",
-    "                        },\n",
-    "                        {\n",
-    "                            \"type\": \"text\",\n",
-    "                            \"text\": example[\"query\"],\n",
-    "                        },\n",
-    "                    ],\n",
-    "                },\n",
-    "            ]\n",
-    "        }\n",
-    "        # Áp dụng mẫu trò chuyện và loại bỏ khoảng trắng thừa\n",
-    "        text_inputs.append(processor.apply_chat_template(formatted_example[\"messages\"], tokenize=False).strip())\n",
-    "        \n",
-    "        # Đảm bảo hình ảnh ở chế độ RGB\n",
-    "        image = example[\"image\"]\n",
-    "        if image.mode != 'RGB':\n",
-    "            image = image.convert('RGB')\n",
-    "        image_inputs.append( [image] )\n",
-    "\n",
-    "    # Token hóa văn bản và xử lý hình ảnh\n",
-    "    batch = processor(\n",
-    "        text=text_inputs,\n",
-    "        images=image_inputs,\n",
-    "        return_tensors=\"pt\",\n",
-    "        padding=True\n",
-    "    )\n",
-    "\n",
-    "    # Sao chép ID đầu vào cho nhãn\n",
-    "    labels = batch[\"input_ids\"].clone()\n",
-    "    labels[labels == processor.tokenizer.pad_token_id] = -100  # Che các token đệm (pad token) trong nhãn\n",
-    "\n",
-    "    # Đảm bảo image_token được chuyển đổi thành chuỗi nếu nó là AddedToken\n",
-    "    # Trong một số bộ xử lý, processor.image_token trả về một danh sách cho mỗi hình ảnh.\n",
-    "    # TODO: AutoProcessor.from_pretrained(\"HuggingFaceTB/SmolVLM-Instruct\") chỉ có một?\n",
-    "    image_token_id = processor.tokenizer.convert_tokens_to_ids(str(processor.image_token))\n",
-    "\n",
-    "    # Che các ID token hình ảnh trong nhãn\n",
-    "    labels[labels == image_token_id] = -100\n",
-    "\n",
-    "    # Thêm nhãn trở lại batch\n",
-    "    batch[\"labels\"] = labels\n",
-    "\n",
-    "    return batch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from trl import SFTConfig, SFTTrainer\n",
-    "\n",
-    "# Cấu hình Trình huấn luyện\n",
-    "training_args = SFTConfig(\n",
-    "    output_dir=\"sft_output\",                # Thư mục để lưu mô hình\n",
-    "    num_train_epochs=3,                     # số epoch huấn luyện\n",
-    "    per_device_train_batch_size=1,          # kích thước batch trên mỗi thiết bị trong quá trình huấn luyện\n",
-    "    gradient_accumulation_steps=16,         # số bước trước khi thực hiện lan truyền ngược/cập nhật\n",
-    "    gradient_checkpointing=True,            # sử dụng checkpoint gradient để tiết kiệm bộ nhớ\n",
-    "    optim=\"adamw_torch_fused\",              # sử dụng trình tối ưu hóa adamw_torch_fused\n",
-    "    logging_steps=5,                        # ghi log sau mỗi 5 bước\n",
-    "    save_strategy=\"epoch\",                  # lưu checkpoint sau mỗi epoch\n",
-    "    learning_rate=2e-4,                     # tốc độ học, dựa trên bài báo QLoRA\n",
-    "    bf16=True,                              # sử dụng độ chính xác bfloat16\n",
-    "    tf32=True,                              # sử dụng độ chính xác tf32\n",
-    "    max_grad_norm=0.3,                      # chuẩn gradient tối đa dựa trên bài báo QLoRA\n",
-    "    warmup_ratio=0.03,                      # tỷ lệ khởi động dựa trên bài báo QLoRA\n",
-    "    lr_scheduler_type=\"constant\",           # sử dụng bộ lập lịch tốc độ học không đổi\n",
-    "    push_to_hub=True,                       # đẩy mô hình lên hub\n",
-    "    gradient_checkpointing_kwargs = {\"use_reentrant\": False}, # sử dụng checkpointing reentrant\n",
-    "    # dataloader_num_workers=16, \n",
-    "    dataset_text_field=\"\",                           # cần một trường giả cho bộ đối chiếu (collator)\n",
-    "    dataset_kwargs = {\"skip_prepare_dataset\": True}, # quan trọng cho bộ đối chiếu\n",
-    "    remove_unused_columns = False                    # cần thiết nếu không các đặc trưng ngoại trừ nhãn sẽ bị xóa\n",
-    ")\n",
-    "# Khởi tạo Trình huấn luyện\n",
-    "trainer = SFTTrainer(\n",
-    "    model=model,\n",
-    "    args=training_args,\n",
-    "    train_dataset=ds[\"train\"],\n",
-    "    eval_dataset=ds[\"test\"],\n",
-    "    data_collator=collate_fn,\n",
-    "    peft_config=peft_config,\n",
-    "    tokenizer=processor.tokenizer,\n",
-    ")\n",
-    "\n",
-    "# TODO: 🦁 🐕 điều chỉnh các tham số SFTTrainer với tập dữ liệu bạn đã chọn. Ví dụ: nếu bạn đang sử dụng tập dữ liệu `bigcode/the-stack-smol`, bạn sẽ cần chọn cột `content`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Huấn luyện mô hình\n",
-    "\n",
-    "Với trình huấn luyện được cấu hình, giờ đây chúng ta có thể tiến hành huấn luyện mô hình. Quá trình huấn luyện sẽ bao gồm việc lặp qua tập dữ liệu, tính toán hàm mất mát (loss) và cập nhật các tham số của mô hình để giảm thiểu hàm mất mát này."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6b4e2897ddbc47fa90254bcf12ce92f6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/5304 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Train the model\n",
-    "trainer.train()\n",
-    "\n",
-    "# Save the model\n",
-    "trainer.save_model(f\"./{finetune_name}\")\n",
-    "\n",
-    "# Save to the huggingface hub if login (HF_TOKEN is set)\n",
-    "if os.getenv(\"HF_TOKEN\"):\n",
-    "    trainer.push_to_hub(tags=finetune_tags)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 💐  Chúc mừng bạn đã hoàn thành bài tập!\n",
-    "Notebook này đã cung cấp hướng dẫn từng bước để tinh chỉnh mô hình `HuggingFaceTB/SmolVLM` bằng cách sử dụng `SFTTrainer`. Bằng cách làm theo các bước này, bạn có thể điều chỉnh mô hình để thực hiện các tác vụ cụ thể hiệu quả hơn. Nếu bạn muốn tiếp tục làm việc với khóa học này, đây là các bước bạn có thể thử:\n",
-    "\n",
-    "- Thử notebook này ở độ khó cao hơn\n",
-    "- Cải thiện tài liệu khóa học thông qua một Issue hoặc PR."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py310",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/vi/5_vision_language_models/notebooks/vlm_usage_sample.ipynb b/vi/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
deleted file mode 100644
index f8bd393f..00000000
--- a/vi/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
+++ /dev/null
@@ -1,474 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Xử lý hình ảnh và văn bản với Mô hình Ngôn ngữ Thị giác (VLM)\n",
-    "\n",
-    "Notebook này minh họa cách sử dụng mô hình `HuggingFaceTB/SmolVLM-Instruct` đã được lượng tử hóa **4 bit** cho các tác vụ đa phương thức (multimodal) khác nhau như:\n",
-    "\n",
-    "- Hỏi đáp bằng hình ảnh (Visual Question Answering - VQA): Trả lời các câu hỏi dựa trên nội dung hình ảnh.\n",
-    "\n",
-    "- Nhận dạng văn bản (OCR): Trích xuất và diễn giải văn bản trong hình ảnh.\n",
-    "\n",
-    "- Mô tả video: Mô tả video thông qua phân tích khung hình tuần tự.\n",
-    "\n",
-    "Bằng cách cấu trúc các lời nhắc (prompt) một cách hiệu quả, bạn có thể tận dụng mô hình cho nhiều ứng dụng, chẳng hạn như hiểu cảnh, phân tích tài liệu và suy luận hình ảnh động."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Cài đặt các yêu cầu trong Google Colab\n",
-    "# !pip install transformers datasets trl huggingface_hub bitsandbytes\n",
-    "\n",
-    "# Xác thực với Hugging Face\n",
-    "from huggingface_hub import notebook_login\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`low_cpu_mem_usage` was None, now default to True since model is quantized.\n",
-      "You shouldn't move a model that is dispatched using accelerate hooks.\n",
-      "Some kwargs in processor config are unused and will not have any effect: image_seq_len. \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'longest_edge': 1536}\n"
-     ]
-    }
-   ],
-   "source": [
-    "import torch, PIL\n",
-    "from transformers import AutoProcessor, AutoModelForVision2Seq, BitsAndBytesConfig\n",
-    "from transformers.image_utils import load_image\n",
-    "\n",
-    "device = (\n",
-    "    \"cuda\"\n",
-    "    if torch.cuda.is_available()\n",
-    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
-    ")\n",
-    "\n",
-    "quantization_config = BitsAndBytesConfig(load_in_4bit=True)\n",
-    "model_name = \"HuggingFaceTB/SmolVLM-Instruct\"\n",
-    "model = AutoModelForVision2Seq.from_pretrained(\n",
-    "    model_name,\n",
-    "    quantization_config=quantization_config,\n",
-    ").to(device)\n",
-    "processor = AutoProcessor.from_pretrained(\"HuggingFaceTB/SmolVLM-Instruct\")\n",
-    "\n",
-    "print(processor.image_processor.size)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Xử lý hình ảnh\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Hãy bắt đầu với việc tạo chú thích và trả lời các câu hỏi về một hình ảnh. Chúng ta cũng sẽ khám phá việc xử lý nhiều hình ảnh.\n",
-    "\n",
-    "### 1. Tạo chú thích cho một hình ảnh"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<img src=\"https://cdn.pixabay.com/photo/2024/11/20/09/14/christmas-9210799_1280.jpg\"/>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<img src=\"https://cdn.pixabay.com/photo/2024/11/23/08/18/christmas-9218404_1280.jpg\"/>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from IPython.display import Image, display\n",
-    "\n",
-    "image_url1 = \"https://cdn.pixabay.com/photo/2024/11/20/09/14/christmas-9210799_1280.jpg\"\n",
-    "display(Image(url=image_url1))\n",
-    "\n",
-    "image_url2 = \"https://cdn.pixabay.com/photo/2024/11/23/08/18/christmas-9218404_1280.jpg\"\n",
-    "display(Image(url=image_url2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/duydl/Miniconda3/envs/py310/lib/python3.10/site-packages/bitsandbytes/nn/modules.py:451: UserWarning: Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference or training speed.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['User:<image>Can you describe the image?\\nAssistant: The image is a scene of a person walking in a forest. The person is wearing a coat and a cap. The person is holding the hand of another person. The person is walking on a path. The path is covered with dry leaves. The background of the image is a forest with trees.']\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Tải một hình ảnh\n",
-    "image1 = load_image(image_url1)\n",
-    "\n",
-    "# Tạo các thông báo đầu vào\n",
-    "messages = [\n",
-    "    {\n",
-    "        \"role\": \"user\",\n",
-    "        \"content\": [\n",
-    "            {\"type\": \"image\"},\n",
-    "            {\"type\": \"text\", \"text\": \"Bạn có thể mô tả hình ảnh này không?\"}\n",
-    "        ]\n",
-    "    },\n",
-    "]\n",
-    "\n",
-    "# Chuẩn bị đầu vào\n",
-    "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
-    "inputs = processor(text=prompt, images=[image1], return_tensors=\"pt\")\n",
-    "inputs = inputs.to(device)\n",
-    "\n",
-    "# Tạo đầu ra\n",
-    "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
-    "generated_texts = processor.batch_decode(\n",
-    "    generated_ids,\n",
-    "    skip_special_tokens=True,\n",
-    ")\n",
-    "\n",
-    "print(generated_texts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2. So sánh nhiều hình ảnh\n",
-    "Mô hình có thể xử lý và so sánh nhiều hình ảnh. Hãy xác định chủ đề chung giữa hai hình ảnh."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['User:<image>What event do they both represent?\\nAssistant: Christmas.']\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Tải hình ảnh\n",
-    "image2 = load_image(image_url2)\n",
-    "\n",
-    "# Tạo các thông báo đầu vào\n",
-    "messages = [\n",
-    "    # {\n",
-    "    #     \"role\": \"user\",\n",
-    "    #     \"content\": [\n",
-    "    #         {\"type\": \"image\"},\n",
-    "    #         {\"type\": \"image\"},\n",
-    "    #         {\"type\": \"text\", \"text\": \"Bạn có thể mô tả hai hình ảnh này không?\"}\n",
-    "    #     ]\n",
-    "    # },\n",
-    "    {\n",
-    "        \"role\": \"user\",\n",
-    "        \"content\": [\n",
-    "            {\"type\": \"image\"},\n",
-    "            {\"type\": \"image\"},\n",
-    "            {\"type\": \"text\", \"text\": \"Chúng đại diện cho sự kiện gì?\"}\n",
-    "        ]\n",
-    "    },\n",
-    "]\n",
-    "\n",
-    "# Chuẩn bị đầu vào\n",
-    "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
-    "inputs = processor(text=prompt, images=[image1, image2], return_tensors=\"pt\")\n",
-    "inputs = inputs.to(device)\n",
-    "\n",
-    "# Tạo đầu ra\n",
-    "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
-    "generated_texts = processor.batch_decode(\n",
-    "    generated_ids,\n",
-    "    skip_special_tokens=True,\n",
-    ")\n",
-    "\n",
-    "print(generated_texts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 🔠 Nhận dạng văn bản (OCR)\n",
-    "VLM cũng có thể nhận ra và diễn giải văn bản trong hình ảnh, làm cho nó phù hợp với các tác vụ như phân tích tài liệu.\n",
-    "Bạn có thể thử nghiệm trên các hình ảnh có văn bản dày đặc hơn."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<img src=\"https://cdn.pixabay.com/photo/2020/11/30/19/23/christmas-5792015_960_720.png\"/>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['User:<image>What is written?\\nAssistant: MERRY CHRISTMAS AND A HAPPY NEW YEAR']\n"
-     ]
-    }
-   ],
-   "source": [
-    "document_image_url = \"https://cdn.pixabay.com/photo/2020/11/30/19/23/christmas-5792015_960_720.png\"\n",
-    "display(Image(url=document_image_url))\n",
-    "\n",
-    "# Tải hình ảnh tài liệu\n",
-    "document_image = load_image(document_image_url)\n",
-    "\n",
-    "# Tạo thông báo đầu vào để phân tích\n",
-    "messages = [\n",
-    "    {\n",
-    "        \"role\": \"user\",\n",
-    "        \"content\": [\n",
-    "            {\"type\": \"image\"},\n",
-    "            {\"type\": \"text\", \"text\": \"Nội dung là gì?\"}\n",
-    "        ]\n",
-    "    }\n",
-    "]\n",
-    "\n",
-    "# Chuẩn bị đầu vào\n",
-    "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
-    "inputs = processor(text=prompt, images=[document_image], return_tensors=\"pt\")\n",
-    "inputs = inputs.to(device)\n",
-    "\n",
-    "# Tạo đầu ra\n",
-    "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
-    "generated_texts = processor.batch_decode(\n",
-    "    generated_ids,\n",
-    "    skip_special_tokens=True,\n",
-    ")\n",
-    "\n",
-    "print(generated_texts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Xử lý video\n",
-    "Các Mô hình Ngôn ngữ Thị giác (VLM) có thể xử lý video gián tiếp bằng cách trích xuất các khung hình chính (keyframe) và suy luận trên chúng theo thứ tự thời gian. Mặc dù VLM thiếu khả năng nhận biết thời gian thực của các mô hình video chuyên dụng, chúng vẫn có thể:\n",
-    "\n",
-    "- Mô tả các hành động hoặc sự kiện bằng cách phân tích các khung hình được lấy mẫu tuần tự.\n",
-    "\n",
-    "- Trả lời các câu hỏi về video dựa trên các khung hình chính đại diện.\n",
-    "\n",
-    "- Tóm tắt nội dung video bằng cách kết hợp các mô tả văn bản của nhiều khung hình.\n",
-    "\n",
-    "Hãy thử nghiệm trên một ví dụ:\n",
-    "\n",
-    "<video width=\"640\" height=\"360\" controls>\n",
-    "  <source src=\"https://cdn.pixabay.com/video/2023/10/28/186794-879050032_large.mp4\" type=\"video/mp4\">\n",
-    "  Your browser does not support the video tag.\n",
-    "</video>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# !pip install opencv-python"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from IPython.display import Video\n",
-    "import cv2\n",
-    "import numpy as np\n",
-    "\n",
-    "def extract_frames(video_path, max_frames=50, target_size=None):\n",
-    "    cap = cv2.VideoCapture(video_path)\n",
-    "    if not cap.isOpened():\n",
-    "        raise ValueError(f\"Không thể mở video: {video_path}\")\n",
-    "    \n",
-    "    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
-    "    frame_indices = np.linspace(0, total_frames - 1, max_frames, dtype=int)\n",
-    "\n",
-    "    frames = []\n",
-    "    for idx in frame_indices:\n",
-    "        cap.set(cv2.CAP_PROP_POS_FRAMES, idx)\n",
-    "        ret, frame = cap.read()\n",
-    "        if ret:\n",
-    "            frame = PIL.Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n",
-    "            if target_size:\n",
-    "                frames.append(resize_and_crop(frame, target_size))\n",
-    "            else:\n",
-    "                frames.append(frame)\n",
-    "    cap.release()\n",
-    "    return frames\n",
-    "\n",
-    "def resize_and_crop(image, target_size):\n",
-    "    width, height = image.size\n",
-    "    scale = target_size / min(width, height)\n",
-    "    image = image.resize((int(width * scale), int(height * scale)), PIL.Image.Resampling.LANCZOS)\n",
-    "    left = (image.width - target_size) // 2\n",
-    "    top = (image.height - target_size) // 2\n",
-    "    return image.crop((left, top, left + target_size, top + target_size))\n",
-    "\n",
-    "# Đường dẫn video\n",
-    "video_link = \"https://cdn.pixabay.com/video/2023/10/28/186794-879050032_large.mp4\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Response: User: Following are the frames of a video in temporal order.<image>Describe what the woman is doing.\n",
-      "Assistant: The woman is hanging an ornament on a Christmas tree.\n"
-     ]
-    }
-   ],
-   "source": [
-    "question = \"Mô tả những gì người phụ nữ đang làm.\"\n",
-    "\n",
-    "def generate_response(model, processor, frames, question):\n",
-    "\n",
-    "    image_tokens = [{\"type\": \"image\"} for _ in frames]\n",
-    "    messages = [\n",
-    "        {\n",
-    "            \"role\": \"user\",\n",
-    "            \"content\": [{\"type\": \"text\", \"text\": \"Sau đây là các khung hình của một video theo thứ tự thời gian.\"}, *image_tokens, {\"type\": \"text\", \"text\": question}]\n",
-    "        }\n",
-    "    ]\n",
-    "    inputs = processor(\n",
-    "        text=processor.apply_chat_template(messages, add_generation_prompt=True),\n",
-    "        images=frames,\n",
-    "        return_tensors=\"pt\"\n",
-    "    ).to(model.device)\n",
-    "\n",
-    "    outputs = model.generate(\n",
-    "        **inputs, max_new_tokens=100, num_beams=5, temperature=0.7, do_sample=True, use_cache=True\n",
-    "    )\n",
-    "    return processor.decode(outputs[0], skip_special_tokens=True)\n",
-    "\n",
-    "# Trích xuất khung hình từ video\n",
-    "frames = extract_frames(video_link, max_frames=15, target_size=384)\n",
-    "\n",
-    "processor.image_processor.size = (384, 384)\n",
-    "processor.image_processor.do_resize = False\n",
-    "# Tạo phản hồi\n",
-    "response = generate_response(model, processor, frames, question)\n",
-    "\n",
-    "# Hiển thị kết quả\n",
-    "# print(\"Câu hỏi:\", question)\n",
-    "print(\"Phản hồi:\", response)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 💐 Bạn đã hoàn thành!\n",
-    "Notebook này đã minh họa cách sử dụng Mô hình Ngôn ngữ Thị giác (VLM) như định dạng lời nhắc cho các tác vụ đa phương thức. Bằng cách làm theo các bước được nêu ở đây, bạn có thể thử nghiệm với VLM và các ứng dụng của chúng.\n",
-    "\n",
-    "### Các bước tiếp theo để khám phá:\n",
-    "- Thử nghiệm với nhiều trường hợp sử dụng VLM hơn.\n",
-    "\n",
-    "- Cộng tác với đồng nghiệp bằng cách xem xét các yêu cầu kéo (PR) của họ.\n",
-    "\n",
-    "- Đóng góp để cải thiện tài liệu khóa học này bằng cách mở một vấn đề (issue) hoặc gửi PR để giới thiệu các trường hợp sử dụng, ví dụ hoặc khái niệm mới.\n",
-    "\n",
-    "Chúc bạn khám phá vui vẻ! 🌟"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py310",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/vi/5_vision_language_models/vlm_finetuning.md b/vi/5_vision_language_models/vlm_finetuning.md
deleted file mode 100644
index 28f57b5c..00000000
--- a/vi/5_vision_language_models/vlm_finetuning.md
+++ /dev/null
@@ -1,106 +0,0 @@
-# Tinh chỉnh Mô hình Ngôn ngữ Thị giác (VLM)
-
-## Tinh chỉnh Hiệu quả
-
-### Lượng tử hóa (Quantization)
-
-Lượng tử hóa làm giảm độ chính xác của trọng số mô hình (model weights) và hàm kích hoạt (activations), giúp giảm đáng kể mức sử dụng bộ nhớ và tăng tốc độ tính toán. Ví dụ: chuyển từ `float32` sang `bfloat16` giúp giảm một nửa yêu cầu bộ nhớ cho mỗi tham số trong khi vẫn duy trì hiệu suất. Để nén mạnh hơn, có thể sử dụng lượng tử hóa `8 bit` và `4 bit`, giúp giảm mức sử dụng bộ nhớ hơn nữa, mặc dù phải đánh đổi bằng việc giảm độ chính xác. Những kỹ thuật này có thể được áp dụng cho cả trọng số mô hình và trình tối ưu hóa (optimizer), cho phép huấn luyện hiệu quả trên phần cứng có tài nguyên hạn chế.
-
-### PEFT & LoRA
-
-Như đã giới thiệu trong Bài 3, LoRA (Low-Rank Adaptation) tập trung vào việc học các ma trận phân rã hạng thấp (rank-decomposition matrices) nhỏ gọn trong khi vẫn giữ nguyên trọng số của mô hình gốc. Điều này làm giảm đáng kể số lượng tham số có thể huấn luyện, giảm đáng kể yêu cầu tài nguyên. LoRA, khi được tích hợp với PEFT (Parameter-Efficient Fine-Tuning), cho phép tinh chỉnh các mô hình lớn bằng cách chỉ điều chỉnh một tập hợp con nhỏ các tham số có thể huấn luyện. Phương pháp này đặc biệt hiệu quả cho các thích ứng theo nhiệm vụ cụ thể, giảm hàng tỷ tham số có thể huấn luyện xuống chỉ còn hàng triệu trong khi vẫn duy trì hiệu suất.
-
-### Tối ưu hóa Kích thước Batch (Batch Size)
-
-Để tối ưu hóa kích thước batch cho quá trình tinh chỉnh, hãy bắt đầu với một giá trị lớn và giảm nếu xảy ra lỗi out-of-memory (OOM). Bù lại bằng cách tăng `gradient_accumulation_steps`, duy trì hiệu quả tổng kích thước batch trên nhiều lần cập nhật. Ngoài ra, hãy bật `gradient_checkpointing` để giảm mức sử dụng bộ nhớ bằng cách tính toán lại các trạng thái trung gian trong quá trình lan truyền ngược (backward pass), đánh đổi thời gian tính toán để giảm yêu cầu bộ nhớ kích hoạt. Những chiến lược này tối đa hóa việc sử dụng phần cứng và giúp khắc phục các hạn chế về bộ nhớ.
-
-```python
-from transformers import TrainingArguments
-
-training_args = TrainingArguments(
-    output_dir="./fine_tuned_model",  # Thư mục cho các checkpoint của mô hình
-    per_device_train_batch_size=4,   # Kích thước batch trên mỗi thiết bị (GPU/TPU)
-    num_train_epochs=3,              # Tổng số epoch huấn luyện
-    learning_rate=5e-5,              # Tốc độ học
-    save_steps=1000,                 # Lưu checkpoint sau mỗi 1000 bước
-    bf16=True,                       # Sử dụng mixed precision để huấn luyện
-    gradient_checkpointing=True,     # Bật để giảm mức sử dụng bộ nhớ kích hoạt
-    gradient_accumulation_steps=16,  # Tích lũy gradient qua 16 bước
-    logging_steps=50                 # Ghi nhật ký các số liệu sau mỗi 50 bước
-)
-```
-
-## **Tinh chỉnh có Giám sát (Supervised Fine-Tuning - SFT)**
-
-Tinh chỉnh có Giám sát (SFT) điều chỉnh Mô hình Ngôn ngữ Thị giác (VLM) đã được huấn luyện trước cho các nhiệm vụ cụ thể bằng cách tận dụng các tập dữ liệu được gán nhãn có chứa các đầu vào được ghép nối, chẳng hạn như hình ảnh và văn bản tương ứng. Phương pháp này nâng cao khả năng của mô hình để thực hiện các chức năng cụ thể theo miền (domain-specific) hoặc theo nhiệm vụ (task-specific), chẳng hạn như trả lời câu hỏi bằng hình ảnh, chú thích hình ảnh hoặc diễn giải biểu đồ.
-
-### **Tổng quan**
-
-SFT là cần thiết khi bạn cần một VLM chuyên về một lĩnh vực cụ thể hoặc giải quyết các vấn đề cụ thể mà khả năng chung của mô hình cơ sở có thể không đáp ứng được. Ví dụ: nếu mô hình gặp khó khăn với các đặc điểm hình ảnh độc đáo hoặc thuật ngữ chuyên ngành, SFT cho phép mô hình tập trung vào các lĩnh vực này bằng cách học từ dữ liệu được gán nhãn.
-
-Mặc dù SFT rất hiệu quả, nó có những hạn chế đáng chú ý:
-
-- **Phụ thuộc vào dữ liệu**: Cần có các tập dữ liệu được gán nhãn chất lượng cao phù hợp với nhiệm vụ.
-- **Tài nguyên tính toán**: Tinh chỉnh các VLM lớn đòi hỏi nhiều tài nguyên.
-- **Nguy cơ quá khớp (Overfitting)**: Các mô hình có thể mất khả năng khái quát hóa nếu được tinh chỉnh quá hẹp.
-
-Tuy vậy, SFT vẫn là một kỹ thuật mạnh mẽ để nâng cao hiệu suất của mô hình trong các bối cảnh cụ thể.
-
-### **Cách sử dụng**
-
-1. **Chuẩn bị dữ liệu**: Bắt đầu với tập dữ liệu được gán nhãn ghép nối hình ảnh với văn bản, chẳng hạn như câu hỏi và câu trả lời. Ví dụ: trong các tác vụ như phân tích biểu đồ, tập dữ liệu `HuggingFaceM4/ChartQA` bao gồm hình ảnh biểu đồ, truy vấn và câu trả lời ngắn gọn.
-
-2. **Thiết lập mô hình**: Tải VLM đã được huấn luyện trước phù hợp với nhiệm vụ, chẳng hạn như `HuggingFaceTB/SmolVLM-Instruct`, và một bộ xử lý (processor) để chuẩn bị đầu vào văn bản và hình ảnh. Điều chỉnh cấu hình của mô hình để phù hợp với phần cứng của bạn.
-
-3. **Quá trình tinh chỉnh**:
-   - **Định dạng dữ liệu**: Cấu trúc tập dữ liệu thành định dạng giống như chatbot, ghép nối các câu lệnh hệ thống (system messages), các truy vấn của người dùng và các câu trả lời tương ứng.
-   - **Cấu hình huấn luyện**: Sử dụng các công cụ như `TrainingArguments` của Hugging Face hoặc `SFTConfig` của TRL để thiết lập các tham số huấn luyện. Chúng bao gồm kích thước batch, tốc độ học và các bước tích lũy gradient để tối ưu hóa việc sử dụng tài nguyên.
-   - **Kỹ thuật tối ưu hóa**: Sử dụng **gradient checkpointing** để tiết kiệm bộ nhớ trong quá trình huấn luyện. Sử dụng mô hình đã lượng tử hóa để giảm yêu cầu bộ nhớ và tăng tốc độ tính toán.
-   - Sử dụng `SFTTrainer` từ thư viện TRL, để hợp lý hóa quá trình huấn luyện.
-
-## Tối ưu hóa theo Sở thích (Preference Optimization)
-
-Tối ưu hóa theo Sở thích, đặc biệt là Tối ưu hóa Sở thích Trực tiếp (Direct Preference Optimization - DPO), huấn luyện Mô hình Ngôn ngữ Thị giác (VLM) để phù hợp với sở thích của con người. Thay vì tuân theo các hướng dẫn được xác định trước một cách nghiêm ngặt, mô hình học cách ưu tiên các đầu ra mà con người chủ quan thích hơn. Phương pháp này đặc biệt hữu ích cho các tác vụ liên quan đến phán đoán sáng tạo, lý luận sắc thái hoặc các câu trả lời có thể chấp nhận được khác nhau.
-
-### **Tổng quan**
-
-Tối ưu hóa theo Sở thích giải quyết các tình huống trong đó sở thích chủ quan của con người là trung tâm của sự thành công của nhiệm vụ. Bằng cách tinh chỉnh trên các tập dữ liệu mã hóa sở thích của con người, DPO nâng cao khả năng của mô hình trong việc tạo ra các phản hồi phù hợp với ngữ cảnh và phong cách với mong đợi của người dùng. Phương pháp này đặc biệt hiệu quả cho các tác vụ như viết sáng tạo, tương tác với khách hàng hoặc các tình huống có nhiều lựa chọn.
-
-Mặc dù có những lợi ích, Tối ưu hóa theo Sở thích có những thách thức:
-
-- **Chất lượng dữ liệu**: Cần có các tập dữ liệu được chú thích theo sở thích chất lượng cao, thường làm cho việc thu thập dữ liệu trở thành một nút thắt cổ chai.
-- **Độ phức tạp**: Việc huấn luyện có thể liên quan đến các quy trình phức tạp như lấy mẫu theo cặp các sở thích và cân bằng tài nguyên tính toán.
-
-Các tập dữ liệu sở thích phải nắm bắt được các sở thích rõ ràng giữa các đầu ra ứng viên. Ví dụ: một tập dữ liệu có thể ghép nối một câu hỏi với hai câu trả lời—một câu trả lời được ưu tiên và câu trả lời kia ít được chấp nhận hơn. Mô hình học cách dự đoán câu trả lời được ưu tiên, ngay cả khi nó không hoàn toàn chính xác, miễn là nó phù hợp hơn với đánh giá của con người.
-
-### **Cách sử dụng**
-
-1. **Chuẩn bị tập dữ liệu**
-   Một tập dữ liệu được gán nhãn sở thích là rất quan trọng để huấn luyện. Mỗi ví dụ thường bao gồm một lời nhắc (prompt) (ví dụ: một hình ảnh và câu hỏi) và hai câu trả lời ứng viên: một câu được chọn (ưa thích) và một câu bị từ chối. Ví dụ:
-
-   - **Câu hỏi**: Có bao nhiêu gia đình?
-     - **Bị từ chối**: Hình ảnh không cung cấp bất kỳ thông tin nào về các gia đình.
-     - **Được chọn**: Hình ảnh cho thấy một bảng gồm 18.000 gia đình.
-
-   Tập dữ liệu dạy cho mô hình ưu tiên các câu trả lời phù hợp hơn, ngay cả khi chúng không hoàn hảo.
-
-2. **Thiết lập mô hình**
-   Tải VLM đã được huấn luyện trước và tích hợp nó với thư viện TRL của Hugging Face, hỗ trợ DPO và bộ xử lý để chuẩn bị đầu vào văn bản và hình ảnh. Định cấu hình mô hình để học có giám sát và phù hợp với phần cứng của bạn.
-
-3. **Quy trình huấn luyện**
-   Việc huấn luyện bao gồm việc định cấu hình các tham số cụ thể cho DPO. Dưới đây là bản tóm tắt về quy trình:
-
-   - **Định dạng tập dữ liệu**: Cấu trúc từng mẫu với lời nhắc, hình ảnh và câu trả lời ứng viên.
-   - **Hàm mất mát (Loss Function)**: Sử dụng hàm mất mát dựa trên sở thích để tối ưu hóa mô hình để chọn đầu ra được ưu tiên.
-   - **Huấn luyện hiệu quả**: Kết hợp các kỹ thuật như lượng tử hóa, tích lũy gradient và bộ điều hợp LoRA (LoRA adapters) để tối ưu hóa bộ nhớ và tính toán.
-
-## Tài liệu tham khảo
-
-- [Hugging Face Learn: Tinh chỉnh có giám sát VLMs](https://huggingface.co/learn/cookbook/fine_tuning_vlm_trl)
-- [Hugging Face Learn: Tinh chỉnh có giám sát SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_smol_vlm_sft_trl)
-- [Hugging Face Learn: Tinh chỉnh tối ưu hóa tùy chọn SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_vlm_dpo_smolvlm_instruct)
-- [Hugging Face Blog: Tối ưu hóa tùy chọn cho VLMs](https://huggingface.co/blog/dpo_vlm)
-
-## Các bước tiếp theo
-
-⏩ Thử [vlm_finetune_sample.ipynb](./notebooks/vlm_finetune_sample.ipynb) để triển khai phương pháp thống nhất này để căn chỉnh tùy chọn.
diff --git a/vi/5_vision_language_models/vlm_usage.md b/vi/5_vision_language_models/vlm_usage.md
deleted file mode 100644
index 84b6348b..00000000
--- a/vi/5_vision_language_models/vlm_usage.md
+++ /dev/null
@@ -1,76 +0,0 @@
-# Mô hình Ngôn ngữ Thị giác (Visual Language Models)
-
-Mô hình Ngôn ngữ Thị giác (VLMs) thu hẹp khoảng cách giữa hình ảnh và văn bản, cho phép thực hiện các tác vụ nâng cao như tạo chú thích cho ảnh, trả lời câu hỏi dựa trên hình ảnh hoặc hiểu mối quan hệ giữa dữ liệu văn bản và hình ảnh. Kiến trúc của chúng được thiết kế để xử lý liền mạch cả hai phương thức.
-
-### Kiến trúc
-
-VLMs kết hợp các thành phần xử lý hình ảnh với các mô hình sinh văn bản để đạt được sự hiểu biết thống nhất. Các yếu tố chính trong kiến trúc của chúng là:
-
-![Kiến trúc VLM](./images/VLM_Architecture.png)
-
-- **Bộ mã hóa hình ảnh (Image Encoder)**: Biến đổi hình ảnh thô thành các biểu diễn số học nhỏ gọn. Các bộ mã hóa được huấn luyện trước như CLIP hoặc vision transformers (ViT) thường được sử dụng.
-- **Bộ chiếu nhúng (Embedding Projector)**: Ánh xạ các đặc trưng của hình ảnh vào không gian tương thích với các phép nhúng văn bản, thường sử dụng các lớp dày đặc (dense layers) hoặc các phép biến đổi tuyến tính.
-- **Bộ giải mã văn bản (Text Decoder)**: Hoạt động như thành phần sinh ngôn ngữ, chuyển thông tin đa phương thức đã hợp nhất thành văn bản mạch lạc. Ví dụ bao gồm các mô hình sinh (generative models) như Llama hoặc Vicuna.
-- **Bộ chiếu đa phương thức (Multimodal Projector)**: Cung cấp một lớp bổ sung để trộn lẫn các biểu diễn hình ảnh và văn bản. Nó rất quan trọng đối với các mô hình như LLaVA để thiết lập các kết nối mạnh mẽ hơn giữa hai phương thức.
-
-Hầu hết các VLMs tận dụng các bộ mã hóa hình ảnh và bộ giải mã văn bản đã được huấn luyện trước và căn chỉnh chúng thông qua việc tinh chỉnh bổ sung trên các tập dữ liệu hình ảnh-văn bản được ghép nối. Cách tiếp cận này giúp việc huấn luyện hiệu quả đồng thời cho phép các mô hình khái quát hóa một cách hiệu quả.
-
-### Cách sử dụng
-
-![Quy trình VLM](./images/VLM_Process.png)
-
-VLMs được áp dụng cho một loạt các tác vụ đa phương thức. Khả năng thích ứng của chúng cho phép chúng hoạt động trong các lĩnh vực đa dạng với các mức độ tinh chỉnh khác nhau:
-
-- **Chú thích hình ảnh (Image Captioning)**: Tạo mô tả cho hình ảnh.
-- **Trả lời câu hỏi bằng hình ảnh (Visual Question Answering - VQA)**: Trả lời câu hỏi về nội dung của hình ảnh.
-- **Truy xuất đa phương thức (Cross-Modal Retrieval)**: Tìm văn bản tương ứng cho một hình ảnh nhất định hoặc ngược lại.
-- **Ứng dụng sáng tạo (Creative Applications)**: Hỗ trợ thiết kế, tạo tác phẩm nghệ thuật hoặc tạo nội dung đa phương tiện hấp dẫn.
-
-![Sử dụng VLM](./images/VLM_Usage.png)
-
-Việc huấn luyện và tinh chỉnh VLMs phụ thuộc vào các tập dữ liệu chất lượng cao ghép nối hình ảnh với chú thích văn bản. Các công cụ như thư viện `transformers` của Hugging Face cung cấp quyền truy cập thuận tiện vào các VLMs đã được huấn luyện trước và quy trình làm việc được sắp xếp hợp lý để tinh chỉnh tùy chỉnh.
-
-### Định dạng trò chuyện (Chat Format)
-
-Nhiều VLMs được cấu trúc để tương tác theo kiểu chatbot, nâng cao khả năng sử dụng. Định dạng này bao gồm:
-
-- Một **câu lệnh hệ thống (system message)** đặt vai trò hoặc ngữ cảnh cho mô hình, chẳng hạn như "Bạn là trợ lý phân tích dữ liệu hình ảnh."
-- **Truy vấn của người dùng (user queries)** kết hợp đầu vào văn bản và hình ảnh liên quan.
-- **Phản hồi của trợ lý (assistant responses)** cung cấp đầu ra văn bản bắt nguồn từ phân tích đa phương thức.
-
-Cấu trúc hội thoại này trực quan và phù hợp với mong đợi của người dùng, đặc biệt là đối với các ứng dụng tương tác như dịch vụ khách hàng hoặc các công cụ giáo dục.
-
-Dưới đây là ví dụ về cách hiển thị đầu vào được định dạng:
-
-```json
-[
-    {
-        "role": "system",
-        "content": [{"type": "text", "text": "Bạn là một Mô hình Ngôn ngữ Thị giác chuyên giải thích dữ liệu hình ảnh từ hình ảnh biểu đồ..."}]
-    },
-    {
-        "role": "user",
-        "content": [
-            {"type": "image", "image": "<image_data>"},
-            {"type": "text", "text": "Giá trị cao nhất trong biểu đồ cột là bao nhiêu?"}
-        ]
-    },
-    {
-        "role": "assistant",
-        "content": [{"type": "text", "text": "42"}]
-    }
-]
-```
-
-**Làm việc với nhiều hình ảnh và video**
-
-VLMs cũng có thể xử lý nhiều hình ảnh hoặc thậm chí video bằng cách điều chỉnh cấu trúc đầu vào để chứa các đầu vào hình ảnh tuần tự hoặc song song. Đối với video, các khung hình có thể được trích xuất và xử lý như các hình ảnh riêng lẻ, trong khi vẫn duy trì thứ tự thời gian.
-
-## Tài liệu tham khảo
-
-- [Hugging Face Blog: Mô hình Ngôn ngữ Thị giác](https://huggingface.co/blog/vlms)
-- [Hugging Face Blog: SmolVLM](https://huggingface.co/blog/smolvlm)
-
-## Các bước tiếp theo
-
-⏩ Thử [vlm_usage_sample.ipynb](./notebooks/vlm_usage_sample.ipynb) để thử các cách sử dụng khác nhau của SMOLVLM.
diff --git a/vi/6_synthetic_datasets/README.md b/vi/6_synthetic_datasets/README.md
deleted file mode 100644
index fe933bd4..00000000
--- a/vi/6_synthetic_datasets/README.md
+++ /dev/null
@@ -1,39 +0,0 @@
-# Tạo tập dữ liệu giả lập (Synthetic Datasets)
-
-Dữ liệu giả lập (synthetic data) là dữ liệu được tạo ra nhân tạo mô phỏng việc sử dụng trong thế giới thực. Nó cho phép khắc phục các hạn chế về dữ liệu bằng cách mở rộng hoặc nâng cao các tập dữ liệu. Mặc dù dữ liệu giả lập đã được sử dụng cho một số trường hợp, các mô hình ngôn ngữ lớn đã làm cho các tập dữ liệu giả lập trở nên phổ biến hơn cho việc huấn luyện trước, huấn luyện sau và đánh giá các mô hình ngôn ngữ.
-
-Chúng ta sẽ sử dụng [`distilabel`](https://distilabel.argilla.io/latest/), một thư viện (framework) tạo dữ liệu giả lập và phản hồi AI cho các kỹ sư, những người cần các quy trình (pipeline) nhanh, đáng tin cậy và có thể mở rộng dựa trên các bài báo nghiên cứu đã được xác minh. Để tìm hiểu sâu hơn về package và các phương pháp hay nhất, hãy xem [tài liệu](https://distilabel.argilla.io/latest/).
-
-## Tổng quan về Mô-đun
-
-Dữ liệu giả lập cho các mô hình ngôn ngữ có thể được phân loại thành ba loại: hướng dẫn (instructions), sở thích (preferences) và phê bình (critiques). Chúng ta sẽ tập trung vào hai loại đầu tiên, tập trung vào việc tạo ra các tập dữ liệu để tinh chỉnh hướng dẫn (instruction tuning) và điều chỉnh sở thích (preference alignment). Trong cả hai loại, chúng ta sẽ đề cập đến các khía cạnh của loại thứ ba, tập trung vào việc cải thiện dữ liệu hiện có bằng các phê bình và viết lại của mô hình.
-
-![Phân loại dữ liệu giả lập](./images/taxonomy-synthetic-data.png)
-
-## Nội dung
-
-### 1. [Tập dữ liệu hướng dẫn](./instruction_datasets.md)
-
-Tìm hiểu cách tạo tập dữ liệu hướng dẫn để tinh chỉnh hướng dẫn. Chúng ta sẽ khám phá việc tạo các tập dữ liệu tinh chỉnh hướng dẫn thông qua các lời nhắc (prompting) cơ bản và sử dụng các kỹ thuật nhắc nhở tinh tế hơn từ các bài báo. Các tập dữ liệu tinh chỉnh hướng dẫn với dữ liệu mẫu (seed data) để học trong ngữ cảnh (in-context learning) có thể được tạo ra thông qua các phương pháp như `SelfInstruct` và `Magpie`. Ngoài ra, chúng ta sẽ khám phá sự tiến hóa hướng dẫn thông qua `EvolInstruct`. [Bắt đầu học](./instruction_datasets.md).
-
-### 2. [Tập dữ liệu ưu tiên](./preference_datasets.md)
-
-Tìm hiểu cách tạo tập dữ liệu sở thích để điều chỉnh sở thích. Chúng ta sẽ xây dựng dựa trên các phương pháp và kỹ thuật được giới thiệu trong phần 1, bằng cách tạo thêm các phản hồi. Tiếp theo, chúng ta sẽ học cách cải thiện các phản hồi đó bằng lời nhắc `EvolQuality`. Cuối cùng, chúng ta sẽ khám phá cách đánh giá các phản hồi bằng lời nhắc `UltraFeedback`, lời nhắc này sẽ tạo ra điểm số và phê bình, cho phép chúng ta tạo các cặp sở thích. [Bắt đầu học](./preference_datasets.md).
-
-### Notebook bài tập
-
-| Tiêu đề | Mô tả | Bài tập | Liên kết | Colab |
-|-------|-------------|----------|------|-------|
-| Tập dữ liệu hướng dẫn | Tạo tập dữ liệu để tinh chỉnh hướng dẫn | 🐢 Tạo tập dữ liệu tinh chỉnh hướng dẫn <br> 🐕 Tạo tập dữ liệu tinh chỉnh hướng dẫn với dữ liệu hạt giống <br> 🦁 Tạo tập dữ liệu tinh chỉnh hướng dẫn với dữ liệu hạt giống và với sự tiến hóa hướng dẫn | [Liên kết](./notebooks/instruction_sft_dataset.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb) |
-| Tập dữ liệu ưu tiên | Tạo tập dữ liệu để điều chỉnh sở thích | 🐢 Tạo tập dữ liệu điều chỉnh sở thích <br> 🐕 Tạo tập dữ liệu điều chỉnh sở thích với sự tiến hóa phản hồi <br> 🦁 Tạo tập dữ liệu điều chỉnh sở thích với sự tiến hóa phản hồi và phê bình | [Liên kết](./notebooks/preference_alignment_dataset.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/6_synthetic_datasets/notebooks/preference_alignment_dataset.ipynb) |
-
-## Tài liệu tham khảo
-
-- [Tài liệu Distilabel](https://distilabel.argilla.io/latest/)
-- [Trình tạo dữ liệu tổng hợp là ứng dụng UI](https://huggingface.co/blog/synthetic-data-generator)
-- [SmolTalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk)
-- [Self-instruct](https://arxiv.org/abs/2212.10560)
-- [Evol-Instruct](https://arxiv.org/abs/2304.12244)
-- [Magpie](https://arxiv.org/abs/2406.08464)
-- [UltraFeedback](https://arxiv.org/abs/2310.01377)
-- [Deita](https://arxiv.org/abs/2312.15685)
\ No newline at end of file
diff --git a/vi/6_synthetic_datasets/images/pipeline.png b/vi/6_synthetic_datasets/images/pipeline.png
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diff --git a/vi/6_synthetic_datasets/instruction_datasets.md b/vi/6_synthetic_datasets/instruction_datasets.md
deleted file mode 100644
index 228a586e..00000000
--- a/vi/6_synthetic_datasets/instruction_datasets.md
+++ /dev/null
@@ -1,203 +0,0 @@
-# Tạo tập dữ liệu hướng dẫn
-
-Trong [chương về tinh chỉnh hướng dẫn (instruction tuning)](../1_instruction_tuning/README.md), chúng ta đã học về việc tinh chỉnh mô hình với Tinh chỉnh có giám sát (Supervised Fine-tuning). Trong phần này, chúng ta sẽ khám phá cách tạo tập dữ liệu hướng dẫn cho SFT. Chúng ta sẽ khám phá việc tạo các tập dữ liệu tinh chỉnh hướng dẫn thông qua việc nhắc nhở (prompting) cơ bản và sử dụng các kỹ thuật tinh tế hơn từ các bài báo. Các tập dữ liệu tinh chỉnh hướng dẫn với dữ liệu hạt giống (seed data) để học trong ngữ cảnh (in-context learning) có thể được tạo ra thông qua các phương pháp như SelfInstruct và Magpie. Ngoài ra, chúng ta sẽ khám phá sự tiến hóa hướng dẫn thông qua EvolInstruct. Cuối cùng, chúng ta sẽ khám phá cách tạo tập dữ liệu để tinh chỉnh hướng dẫn bằng cách sử dụng quy trình (pipeline) distilabel.
-
-## Từ lời nhắc đến dữ liệu
-
-Dữ liệu giả lập (Synthetic data) nghe có vẻ phức tạp, nhưng nó có thể được đơn giản hóa thành việc tạo dữ liệu thông qua việc nhắc nhở hiệu quả để trích xuất kiến thức từ mô hình. Đổi lại, bạn có thể coi đây là một cách để tạo dữ liệu cho một tác vụ cụ thể. Thách thức là nhắc nhở một cách hiệu quả trong khi đảm bảo dữ liệu đa dạng và mang tính đại diện. May mắn thay, nhiều bài báo đã khám phá vấn đề này và chúng ta sẽ khám phá một số bài báo hữu ích trong khóa học này. Trước hết, chúng ta sẽ khám phá cách tạo dữ liệu giả lập thông qua việc nhắc nhở thủ công.
-
-### Nhắc nhở cơ bản (Basic Prompting)
-
-Hãy bắt đầu với một ví dụ cơ bản và tải mô hình [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) bằng cách sử dụng tích hợp `transformers` của thư viện `distilabel`. Chúng ta sẽ sử dụng lớp `TextGeneration` để tạo ra một `lời nhắc` (prompt) tổng hợp và sử dụng nó để tạo ra một `phần hoàn thành` (completion).
-
-Tiếp theo, chúng ta sẽ tải mô hình bằng thư viện `distilabel`.
-
-```python
-from distilabel.llms import TransformersLLM
-from distilabel.steps.tasks import TextGeneration
-
-llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
-gen = TextGeneration(llm=llm)
-gen.load()
-```
-
-> **Note:**  
-> `Distilabel` tải `llm` vào bộ nhớ, vì vậy, khi làm việc trong notebook, chúng ta cần `gen.unload()` sau khi hoàn thành để tránh các vấn đề về bộ nhớ.
-
-Bây giờ chúng ta sẽ sử dụng `llm` để tạo ra một `lời nhắc` để tinh chỉnh hướng dẫn.
-
-```python
-next(gen.process([{"instruction": "Tạo một câu hỏi về Khóa học Smol của Hugging Face về các mô hình AI nhỏ."}]))
-# Ví dụ: Mục đích của Khóa học Smol là gì?
-```
-
-Cuối cùng, chúng ta có thể sử dụng cùng một `lời nhắc` đó làm đầu vào để tạo ra một `phần hoàn thành`.
-
-```python
-next(gen.process([{"instruction": "Mục đích của Khóa học Smol là gì?"}]))
-# Ví dụ: Khóa học Smol là một nền tảng được thiết kế để học các khái niệm khoa học máy tính.
-```
-
-Tuyệt! Chúng ta có thể tạo ra một `lời nhắc` tổng hợp và một `phần hoàn thành` tương ứng. Việc sử dụng lại phương pháp đơn giản này trên quy mô lớn sẽ cho phép chúng ta tạo ra nhiều dữ liệu hơn, tuy nhiên, chất lượng của dữ liệu không tốt lắm và không tính đến các sắc thái của khóa học hoặc lĩnh vực của chúng ta. Ngoài ra, việc chạy lại mã hiện tại cho chúng ta thấy dữ liệu không đa dạng lắm. May mắn thay, có nhiều cách để giải quyết vấn đề này.
-
-### SelfInstruct
-
-`SelfInstruct` là một lời nhắc tạo ra các hướng dẫn mới dựa trên tập dữ liệu mẫu. Dữ liệu mẫu này có thể là một hướng dẫn đơn lẻ hoặc một đoạn ngữ cảnh. Quá trình bắt đầu với một nhóm dữ liệu mẫu ban đầu. Mô hình ngôn ngữ sau đó được nhắc để tạo ra các hướng dẫn mới dựa trên dữ liệu mẫu này bằng cách sử dụng phương pháp học trong ngữ cảnh (in-context learning). Lời nhắc được [triển khai trong distilabel](https://github.com/argilla-io/distilabel/blob/main/src/distilabel/steps/tasks/templates/self-instruct.jinja2) và một phiên bản đơn giản hóa được hiển thị bên dưới:
-
-```
-# Mô tả nhiệm vụ
-Phát triển {{ num_instructions }} truy vấn của người dùng có thể được nhận bởi ứng dụng AI đã cho và áp dụng cho ngữ cảnh được cung cấp. Nhấn mạnh sự đa dạng trong động từ và cấu trúc ngôn ngữ trong khả năng văn bản của mô hình.
-
-# Ngữ cảnh
-{{ input }}
-
-# Đầu ra
-```
-
-Để sử dụng nó, chúng ta cần truyền `llm` cho [lớp SelfInstruct](https://distilabel.argilla.io/dev/components-gallery/tasks/selfinstruct/). Hãy sử dụng văn bản từ [phần Từ lời nhắc đến dữ liệu](#prompt-to-data) làm ngữ cảnh và tạo ra một hướng dẫn mới.
-
-```python
-from distilabel.steps.tasks import SelfInstruct
-
-self_instruct = SelfInstruct(llm=llm)
-self_instruct.load()
-
-context = "<prompt_to_data_section>" # Thay thế bằng nội dung của phần Từ lời nhắc đến dữ liệu
-
-next(self_instruct.process([{"input": text}]))["instructions"][0]
-# Quá trình tạo dữ liệu tổng hợp thông qua việc nhắc nhở thủ công là gì?
-```
-
-Hướng dẫn được tạo ra đã tốt hơn rất nhiều và nó phù hợp với nội dung và lĩnh vực thực tế của chúng ta. Tuy nhiên, chúng ta có thể làm tốt hơn nữa bằng cách cải thiện lời nhắc thông qua phương pháp tiến hóa (evolution).
-
-### EvolInstruct
-
-EvolInstruct là một kỹ thuật nhắc nhở lấy một hướng dẫn đầu vào và phát triển nó thành một phiên bản tốt hơn của cùng một hướng dẫn. Phiên bản tốt hơn này được định nghĩa theo một tập hợp các tiêu chí và bổ sung các ràng buộc, đào sâu, cụ thể hóa, lập luận hoặc phức tạp hóa cho hướng dẫn ban đầu. Quá trình này có thể được lặp lại nhiều lần để tạo ra các phiên bản tiến hóa khác nhau của cùng một hướng dẫn, lý tưởng nhất là dẫn đến một phiên bản tốt hơn của hướng dẫn ban đầu. Lời nhắc được [triển khai trong distilabel](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/evol_instruct) và một phiên bản đơn giản hóa được hiển thị bên dưới:
-
-```
-Tôi muốn bạn đóng vai trò là một Trình viết lại lời nhắc (Prompt Rewriter).
-Cho một lời nhắc, hãy viết lại nó thành một phiên bản phức tạp hơn.
-Phức tạp hóa lời nhắc dựa trên các tiêu chí sau:
-{{ criteria }}
-
-# Lời nhắc
-{{ input }}
-
-# Đầu ra
-```
-
-Để sử dụng nó, chúng ta cần truyền `llm` cho [lớp EvolInstruct](https://distilabel.argilla.io/dev/components-gallery/tasks/evolinstruct/). Hãy sử dụng lời nhắc tổng hợp từ [phần SelfInstruct](#selfinstruct) làm đầu vào và phát triển nó thành một phiên bản tốt hơn. Đối với ví dụ này, chúng ta sẽ chỉ tiến hóa trong một thế hệ.
-
-```python
-from distilabel.steps.tasks import EvolInstruct
-
-evol_instruct = EvolInstruct(llm=llm, num_evolutions=1)
-evol_instruct.load()
-
-text = "Quá trình tạo dữ liệu tổng hợp thông qua việc nhắc nhở thủ công là gì"
-
-next(evol_instruct.process([{"instruction": text}]))
-# Quá trình tạo dữ liệu tổng hợp thông qua việc nhắc nhở thủ công là gì?
-# Và, làm thế nào hệ thống trí tuệ nhân tạo, GPT4, sử dụng các thuật toán học máy để thao tác dữ liệu đầu vào thành dữ liệu tổng hợp?
-```
-
-Hướng dẫn bây giờ phức tạp hơn nhưng đã mất đi một số ý nghĩa ban đầu. Vì vậy, hãy lưu ý rằng việc tiến hóa có thể là một con dao hai lưỡi và chúng ta cần cẩn thận với chất lượng của dữ liệu chúng ta tạo ra.
-
-### Magpie
-
-Magpie là một kỹ thuật dựa vào các yếu tố tự suy luận (auto-regressive) của mô hình ngôn ngữ và [mẫu trò chuyện (chat-template)](../1_instruction_tuning/chat_templates.md) đã được sử dụng trong quá trình tinh chỉnh hướng dẫn. Như bạn có thể nhớ, mẫu trò chuyện là một định dạng cấu trúc các cuộc hội thoại với các chỉ số vai trò rõ ràng (hệ thống, người dùng, trợ lý). Trong giai đoạn tinh chỉnh hướng dẫn, mô hình ngôn ngữ đã được tối ưu hóa để tái tạo định dạng này và đó chính xác là những gì `Magpie` tận dụng. Nó bắt đầu với một lời nhắc trước truy vấn (pre-query-prompt) dựa trên mẫu trò chuyện nhưng nó dừng lại trước chỉ báo tin nhắn của người dùng, ví dụ: `<|im_start|>user\n`, và sau đó nó sử dụng mô hình ngôn ngữ để tạo ra lời nhắc của người dùng cho đến khi kết thúc chỉ báo trợ lý, ví dụ: `<|im_end|>`. Cách tiếp cận này cho phép chúng ta tạo ra rất nhiều dữ liệu một cách rất hiệu quả và thậm chí có thể mở rộng quy mô lên các cuộc hội thoại nhiều lượt. Người ta giả thuyết rằng dữ liệu được tạo ra này tái tạo dữ liệu huấn luyện từ giai đoạn tinh chỉnh hướng dẫn của mô hình được sử dụng.
-
-Trong trường hợp này, các mẫu lời nhắc khác nhau cho mỗi mô hình vì chúng dựa trên định dạng mẫu trò chuyện. Nhưng chúng ta có thể đi qua một phiên bản đơn giản hóa của quá trình từng bước.
-
-```bash
-# Bước 1: cung cấp lời nhắc trước truy vấn
-<|im_start|>user\n
-
-# Bước 2: mô hình ngôn ngữ tạo ra lời nhắc của người dùng
-<|im_start|>user\n
-Mục đích của Khóa học Smol là gì?
-
-# Bước 3: dừng quá trình tạo
-<|im_end|>
-```
-
-Để sử dụng nó trong distilabel, chúng ta cần truyền `llm` cho [lớp Magpie](https://distilabel.argilla.io/dev/components-gallery/tasks/magpie/).
-
-```python
-from distilabel.steps.tasks import Magpie
-
-magpie = Magpie(llm=llm)
-magpie.load()
-
-next(magpie.process([{"system_prompt": "Bạn là một trợ lý hữu ích."}]))
-# [{
-#   "role": "user",
-#   "content": "Bạn có thể cung cấp cho tôi danh sách 3 trường đại học hàng đầu không?"
-# },
-# {
-#   "role": "assistant",
-#   "content": "3 trường đại học hàng đầu là: MIT, Yale, Stanford."
-# }]
-```
-
-Chúng ta ngay lập tức nhận được một tập dữ liệu với một `lời nhắc` và `phần hoàn thành`. Để cải thiện hiệu suất trên lĩnh vực của riêng mình, chúng ta có thể đưa thêm ngữ cảnh vào `system_prompt`. Để LLM tạo ra dữ liệu lĩnh vực cụ thể kết hợp với Magpie, nó giúp mô tả trong lời nhắc hệ thống (system prompt) các truy vấn của người dùng sẽ là gì. Điều này sau đó được sử dụng trong lời nhắc trước truy vấn trước khi chúng ta bắt đầu tạo lời nhắc của người dùng và thiên về LLM để tạo ra các truy vấn của người dùng trong lĩnh vực đó.
-
-```
-Bạn là một trợ lý AI sẽ giúp người dùng giải các bài toán.
-```
-
-Điều quan trọng là phải viết lời nhắc hệ thống như được hiển thị ở trên thay vì một cái gì đó như:
-
-```
-Bạn là một trợ lý AI tạo ra các bài toán
-```
-
-Nói chung, các mô hình ngôn ngữ ít được tối ưu hóa hơn để truyền ngữ cảnh bổ sung cho `system_prompt` vì vậy điều này không phải lúc nào cũng hoạt động tốt cho việc tùy chỉnh như các kỹ thuật khác.
-
-### Từ lời nhắc đến quy trình (pipeline)
-
-Các lớp chúng ta đã thấy cho đến nay đều là các lớp độc lập có thể được sử dụng trong một quy trình. Đây là một khởi đầu tốt, nhưng chúng ta có thể làm tốt hơn nữa bằng cách sử dụng lớp `Pipeline` để tạo tập dữ liệu. Chúng ta sẽ sử dụng bước `TextGeneration` để tạo tập dữ liệu tổng hợp để tinh chỉnh hướng dẫn. Quy trình sẽ bao gồm bước `LoadDataFromDicts` để tải dữ liệu, bước `TextGeneration` để tạo `lời nhắc` và `phần hoàn thành` cho lời nhắc đó. Chúng ta sẽ kết nối các bước và luồng dữ liệu thông qua quy trình bằng toán tử `>>`. Trong [tài liệu của distilabel](https://distilabel.argilla.io/dev/components-gallery/tasks/textgeneration/#input-output-columns), chúng ta có thể thấy các cột đầu vào và đầu ra của bước. Để đảm bảo rằng dữ liệu chảy chính xác qua quy trình, chúng ta sẽ sử dụng tham số `output_mappings` để ánh xạ các cột đầu ra với các cột đầu vào của bước tiếp theo.
-
-```python
-from distilabel.llms import TransformersLLM
-from distilabel.pipeline import Pipeline
-from distilabel.steps import LoadDataFromDicts
-from distilabel.steps.tasks import TextGeneration
-
-with Pipeline() as pipeline:
-    data = LoadDataFromDicts(data=[{"instruction": "Tạo một câu hỏi ngắn về Khóa học Smol của Hugging Face."}])
-    llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
-    gen_a = TextGeneration(llm=llm, output_mappings={"generation": "instruction"})
-    gen_b = TextGeneration(llm=llm, output_mappings={"generation": "response"})
-    data >> gen_a >> gen_b
-
-if __name__ == "__main__":
-    distiset = pipeline.run(use_cache=False)
-    print(distiset["default"]["train"][0])
-# [{
-#   "instruction": "Mục đích của Khóa học Smol là gì?",
-#   "response": "Khóa học Smol là một nền tảng được thiết kế để học các khái niệm khoa học máy tính."
-# }]
-```
-
-Bên dưới, quy trình này có rất nhiều tính năng hay. Nó tự động lưu trữ các kết quả tạo, vì vậy chúng ta không phải chạy lại các bước tạo. Thư viện có tích hợp khả năng xử lý lỗi (fault-tolerance), vì vậy nếu các bước tạo thất bại, quy trình vẫn sẽ tiếp tục chạy. Và quy trình thực hiện tất cả các bước tạo song song, vì vậy việc tạo nhanh hơn. Chúng ta thậm chí có thể trực quan hóa quy trình bằng phương thức `draw`. Ở đây bạn có thể thấy cách dữ liệu chảy qua quy trình và cách `output_mappings` được sử dụng để ánh xạ các cột đầu ra với các cột đầu vào của bước tiếp theo.
-
-![Pipeline](./images/pipeline.png)
-
-## Các phương pháp hay nhất
-
-- Đảm bảo bạn có dữ liệu hạt giống đa dạng để bao quát nhiều tình huống
-- Thường xuyên đánh giá tập dữ liệu để đảm bảo dữ liệu được tạo ra đa dạng và có chất lượng cao
-- Lặp lại trên (system)prompt để cải thiện chất lượng của dữ liệu
-
-## Các bước tiếp theo
-
-👨🏽‍💻 Lập trình -[Notebook bài tập](./notebooks/instruction_sft_dataset.ipynb) để tạo tập dữ liệu để tinh chỉnh hướng dẫn
-🧑‍🏫 Tìm hiểu - Về [tạo tập dữ liệu sở thích](./preference_datasets.md)
-
-## Tài liệu tham khảo
-
-- [Tài liệu Distilabel](https://distilabel.argilla.io/latest/)
-- [Self-instruct](https://arxiv.org/abs/2212.10560)
-- [Evol-Instruct](https://arxiv.org/abs/2304.12244)
-- [Magpie](https://arxiv.org/abs/2406.08464)
diff --git a/vi/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb b/vi/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb
deleted file mode 100644
index ef13b3e9..00000000
--- a/vi/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb
+++ /dev/null
@@ -1,120 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tạo tập dữ liệu hướng dẫn (instruction tuning)\n",
-    "\n",
-    "Notebook này sẽ hướng dẫn bạn qua quá trình tạo tập dữ liệu để tinh chỉnh hướng dẫn. Chúng ta sẽ sử dụng package `distilabel` để tạo tập dữ liệu cho việc tinh chỉnh hướng dẫn.\n",
-    "\n",
-    "Vì vậy, hãy cùng tìm hiểu sâu hơn về một số tập dữ liệu tinh chỉnh hướng dẫn.\n",
-    "\n",
-    " <div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "     <h2 style='margin: 0;color:blue'>Bài tập: Tạo tập dữ liệu hướng dẫn</h2>\n",
-    "     <p>Bây giờ bạn đã biết cách tạo tập dữ liệu để tinh chỉnh hướng dẫn, hãy thử tạo tập dữ liệu để tinh chỉnh hướng dẫn.</p>\n",
-    "     <p><b>Các mức độ khó</b></p>\n",
-    "     <p>🐢 Tạo tập dữ liệu tinh chỉnh hướng dẫn</p>\n",
-    "     <p>🐕 Tạo tập dữ liệu để tinh chỉnh hướng dẫn với dữ liệu hạt giống (seed data)</p>\n",
-    "     <p>🦁 Tạo tập dữ liệu để tinh chỉnh hướng dẫn với dữ liệu hạt giống và với sự tiến hóa hướng dẫn (instruction evolution)</p>\n",
-    " </div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "vscode": {
-     "languageId": "plaintext"
-    }
-   },
-   "source": [
-    "## Cài đặt thư viện\n",
-    "\n",
-    "Thay vì `transformers`, bạn cũng có thể cài đặt `vllm` hoặc `hf-inference-endpoints`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install \"distilabel[hf-transformers,outlines,instructor]\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Bắt đầu tạo dữ liệu\n",
-    "\n",
-    "Như chúng ta đã thấy trong nội dung khóa học trước, chúng ta có thể tạo các quy trình (pipeline) `distilabel` để tạo tập dữ liệu hướng dẫn. Quy trình tối thiểu đã được cung cấp. Đảm bảo mở rộng quy trình này để tạo tập dữ liệu lớn cho việc tinh chỉnh hướng dẫn. Thay đổi các mô hình và các đối số (generation arguments) để xem chúng ảnh hưởng như thế nào đến chất lượng của tập dữ liệu. Thử nghiệm nhỏ, mở rộng quy mô sau.\n",
-    "\n",
-    "Hãy tham khảo [thư viện các thành phần distilabel](https://distilabel.argilla.io/latest/components-gallery/) để biết thông tin về các lớp xử lý và cách sử dụng chúng.\n",
-    "\n",
-    "Một ví dụ về việc tải dữ liệu từ Hub thay vì ở dạng thư viện (dictionary) được cung cấp bên dưới.\n",
-    "\n",
-    "```python\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "with Pipeline(...) as pipeline:\n",
-    "    ...\n",
-    "\n",
-    "if __name__ == \"__main__:\n",
-    "    dataset = load_dataset(\"my-dataset\", split=\"train\")\n",
-    "    distiset = pipeline.run(dataset=dataset)\n",
-    "```\n",
-    "\n",
-    "Đừng quên đẩy tập dữ liệu của bạn lên Hub sau khi chạy quy trình!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from distilabel.llms import TransformersLLM\n",
-    "from distilabel.pipeline import Pipeline\n",
-    "from distilabel.steps import LoadDataFromDicts\n",
-    "from distilabel.steps.tasks import TextGeneration\n",
-    "\n",
-    "with Pipeline() as pipeline:\n",
-    "    data = LoadDataFromDicts(data=[{\"instruction\": \"Tạo một câu hỏi ngắn về Khóa học Smol của Hugging Face.\"}])\n",
-    "    llm = TransformersLLM(model=\"HuggingFaceTB/SmolLM2-1.7B-Instruct\")\n",
-    "    gen_a = TextGeneration(llm=llm, output_mappings={\"generation\": \"instruction\"})\n",
-    "    gen_b = TextGeneration(llm=llm, output_mappings={\"generation\": \"response\"})\n",
-    "    data >> gen_a >> gen_b\n",
-    "\n",
-    "if __name__ == \"__main__\":\n",
-    "    distiset = pipeline.run(use_cache=False)\n",
-    "    distiset.push_to_hub(\"huggingface-smol-course-instruction-tuning-dataset\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 🌯 Kết thúc\n",
-    "\n",
-    "Bây giờ bạn đã biết cách tạo tập dữ liệu để tinh chỉnh hướng dẫn. Bạn có thể sử dụng điều này để:\n",
-    "\n",
-    " - Tạo tập dữ liệu để tinh chỉnh hướng dẫn.\n",
-    " - Tạo tập dữ liệu đánh giá để tinh chỉnh hướng dẫn.\n",
-    "\n",
-    "Tiếp theo\n",
-    "\n",
-    "🧑‍🏫 Tìm hiểu - Về [tạo tập dữ liệu ưu tiên](./preference_datasets.md)\n",
-    "\n",
-    "🏋️‍♂️ Tinh chỉnh một mô hình để tinh chỉnh hướng dẫn với tập dữ liệu giả lập dựa trên [chương tinh chỉnh hướng dẫn](../../1_instruction_tuning/README.md)"
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/vi/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb b/vi/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb
deleted file mode 100644
index 109226da..00000000
--- a/vi/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb
+++ /dev/null
@@ -1,120 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tạo tập dữ liệu để điều chỉnh ưu tiên (preference alignment)\n",
-    "\n",
-    "Notebook này sẽ hướng dẫn bạn qua quá trình tạo tập dữ liệu để điều chỉnh ưu tiên. Chúng ta sẽ sử dụng thư viện `distilabel` để tạo tập dữ liệu cho việc điều chỉnh ưu tiên.\n",
-    "\n",
-    "Vì vậy, hãy cùng tìm hiểu sâu hơn về một số tập dữ liệu điều chỉnh ưu tiên.\n",
-    "\n",
-    " <div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "     <h2 style='margin: 0;color:blue'>Bài tập: Tạo tập dữ liệu để điều chỉnh sở thích</h2>\n",
-    "     <p>Bây giờ bạn đã biết cách tạo tập dữ liệu để điều chỉnh sở thích, hãy thử tạo tập dữ liệu để điều chỉnh sở thích.</p>\n",
-    "     <p><b>Các mức độ khó</b></p>\n",
-    "     <p>🐢 Tạo tập dữ liệu để điều chỉnh sở thích</p>\n",
-    "     <p>🐕 Tạo tập dữ liệu để điều chỉnh sở thích với sự tiến hóa phản hồi (response evolution)</p>\n",
-    "     <p>🦁 Tạo tập dữ liệu để điều chỉnh sở thích với sự tiến hóa phản hồi và tổng hợp mô hình (model pooling)</p>\n",
-    " </div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "vscode": {
-     "languageId": "plaintext"
-    }
-   },
-   "source": [
-    "## Cài đặt các thư viện\n",
-    "\n",
-    "Thay vì `transformers`, bạn cũng có thể cài đặt `vllm` hoặc `hf-inference-endpoints`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install \"distilabel[hf-transformers,outlines,instructor]\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Bắt đầu tổng hợp\n",
-    "\n",
-    "Như chúng ta đã thấy trong notebook trước, chúng ta có thể tạo một quy trình (pipeline) distilabel để tạo tập dữ liệu sở thích. Quy trình tối thiểu đã được cung cấp. Bạn có thể tiếp tục làm việc trên quy trình này để tạo tập dữ liệu lớn cho việc điều chỉnh sở thích. Thay đổi các mô hình, nhà cung cấp mô hình và các đối số sinh (generation arguments) để xem chúng ảnh hưởng như thế nào đến chất lượng của tập dữ liệu. Thử nghiệm nhỏ, mở rộng quy mô sau.\n",
-    "\n",
-    "Hãy xem [thư viện các thành phần distilabel](https://distilabel.argilla.io/latest/components-gallery/) để biết thông tin về các lớp xử lý và cách sử dụng chúng.\n",
-    "\n",
-    "Một ví dụ về việc tải dữ liệu từ Hub thay vì các dictionary được cung cấp bên dưới.\n",
-    "\n",
-    "```python\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "with Pipeline(...) as pipeline:\n",
-    "     ...\n",
-    "\n",
-    "if __name__ == \"__main__\":\n",
-    "     dataset = load_dataset(\"my-dataset\", split=\"train\")\n",
-    "     distiset = pipeline.run(dataset=dataset)\n",
-    "```\n",
-    "\n",
-    "Đừng quên đẩy tập dữ liệu của bạn lên Hub sau khi chạy quy trình!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from distilabel.llms import TransformersLLM\n",
-    "from distilabel.pipeline import Pipeline\n",
-    "from distilabel.steps import GroupColumns, LoadDataFromDicts\n",
-    "from distilabel.steps.tasks import TextGeneration\n",
-    "\n",
-    "with Pipeline() as pipeline:\n",
-    "    data = LoadDataFromDicts(data=[{\"instruction\": \"Dữ liệu giả lập (synthetic data) là gì?\"}])\n",
-    "    llm_a = TransformersLLM(model=\"HuggingFaceTB/SmolLM2-1.7B-Instruct\")\n",
-    "    gen_a = TextGeneration(llm=llm_a)\n",
-    "    llm_b = TransformersLLM(model=\"Qwen/Qwen2.5-1.5B-Instruct\")\n",
-    "    gen_b = TextGeneration(llm=llm_b)\n",
-    "    group = GroupColumns(columns=[\"generation\"])\n",
-    "    data >> [gen_a, gen_b] >> group\n",
-    "\n",
-    "if __name__ == \"__main__\":\n",
-    "    distiset = pipeline.run()\n",
-    "    distiset.push_to_hub(\"huggingface-smol-course-preference-tuning-dataset\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 🌯 Kết thúc\n",
-    "\n",
-    "Bây giờ bạn đã biết cách tạo tập dữ liệu để điều chỉnh ưu tiên. Bạn có thể sử dụng điều này để:\n",
-    "\n",
-    "- Tạo tập dữ liệu để điều chỉnh ưu tiên.\n",
-    "- Tạo tập dữ liệu đánh giá để điều chỉnh ưu tiên.\n",
-    "\n",
-    "Tiếp theo\n",
-    "\n",
-    "🏋️‍♂️ Tinh chỉnh một mô hình với điều chỉnh ưu tiên với tập dữ liệu giả lập dựa trên [chương tinh chỉnh ưu tiên](../../2_preference_alignment/README.md)"
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/vi/6_synthetic_datasets/preference_datasets.md b/vi/6_synthetic_datasets/preference_datasets.md
deleted file mode 100644
index 40af3373..00000000
--- a/vi/6_synthetic_datasets/preference_datasets.md
+++ /dev/null
@@ -1,155 +0,0 @@
-# Tạo tập dữ liệu ưu tiên (Preference Datasets)
-
-Trong [chương về điều chỉnh ưu tiên (preference alignment)](../2_preference_alignment/README.md), chúng ta đã học về Tối ưu hóa ưu tiên trực tiếp (Direct Preference Optimization). Trong phần này, chúng ta sẽ khám phá cách tạo tập dữ liệu ưu tiên cho các phương pháp như DPO. Chúng ta sẽ xây dựng dựa trên các phương pháp đã được giới thiệu trong phần [tạo tập dữ liệu hướng dẫn](./instruction_datasets.md). Ngoài ra, chúng ta sẽ chỉ ra cách thêm các phần hoàn thành (completions) bổ sung vào tập dữ liệu bằng cách sử dụng kỹ thuật nhắc nhở (prompting) cơ bản hoặc bằng cách sử dụng EvolQuality để cải thiện chất lượng của các phản hồi. Cuối cùng, chúng ta sẽ chỉ ra cách `UltraFeedback` có thể được sử dụng để tạo điểm số và phê bình.
-
-## Tạo nhiều phần hoàn thành (completions)
-
-Dữ liệu ưu tiên là tập dữ liệu có nhiều `phần hoàn thành` cho cùng một `hướng dẫn`. Chúng ta có thể thêm nhiều `phần hoàn thành` hơn vào tập dữ liệu bằng cách nhắc nhở (prompt) một mô hình tạo ra chúng. Khi làm điều này, chúng ta cần đảm bảo rằng phần hoàn thành thứ hai không quá giống với phần hoàn thành đầu tiên về chất lượng tổng thể và cách diễn đạt. Điều này rất quan trọng vì mô hình cần được tối ưu hóa cho một ưu tiên rõ ràng. Chúng ta muốn biết phần hoàn thành nào được ưa thích hơn phần kia, thường được gọi là `chosen` (được chọn) và `rejected` (bị từ chối). Chúng ta sẽ đi vào chi tiết hơn về việc xác định các phần hoàn thành được chọn và bị từ chối trong [phần tạo điểm số](#creating-scores).
-
-### Tổng hợp mô hình (Model pooling)
-
-Bạn có thể sử dụng các mô hình từ các họ mô hình khác nhau để tạo phần hoàn thành thứ hai, được gọi là tổng hợp mô hình. Để cải thiện hơn nữa chất lượng của phần hoàn thành thứ hai, bạn có thể sử dụng các đối số tạo khác nhau, như điều chỉnh `temperature`. Cuối cùng, bạn có thể sử dụng các mẫu lời nhắc (prompt templates) hoặc lời nhắc hệ thống (system prompts) khác nhau để tạo phần hoàn thành thứ hai nhằm đảm bảo sự đa dạng dựa trên các đặc điểm cụ thể được xác định trong mẫu. Về lý thuyết, chúng ta có thể lấy hai mô hình có chất lượng khác nhau và sử dụng mô hình tốt hơn làm phần hoàn thành `chosen`.
-
-Hãy bắt đầu với việc tổng hợp mô hình bằng cách tải các mô hình [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) và [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) bằng cách sử dụng tích hợp `transformers` của thư viện `distilabel`. Sử dụng các mô hình này, chúng ta sẽ tạo ra hai `phản hồi` tổng hợp cho một `lời nhắc` nhất định. Chúng ta sẽ tạo một quy trình (pipeline) khác với `LoadDataFromDicts`, `TextGeneration` và `GroupColumns`. Trước tiên, chúng ta sẽ tải dữ liệu, sau đó sử dụng hai bước tạo và sau đó nhóm các kết quả lại. Chúng ta kết nối các bước và luồng dữ liệu thông qua quy trình bằng toán tử `>>` và `[]`, có nghĩa là chúng ta muốn sử dụng đầu ra của bước trước làm đầu vào cho cả hai bước trong danh sách.
-
-```python
-from distilabel.llms import TransformersLLM
-from distilabel.pipeline import Pipeline
-from distilabel.steps import GroupColumns, LoadDataFromDicts
-from distilabel.steps.tasks import TextGeneration
-
-with Pipeline() as pipeline:
-    data = LoadDataFromDicts(data=[{"instruction": "Dữ liệu giả lập (synthetic data) là gì?"}])
-    llm_a = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
-    gen_a = TextGeneration(llm=llm_a)
-    llm_b = TransformersLLM(model="Qwen/Qwen2.5-1.5B-Instruct")
-    gen_b = TextGeneration(llm=llm_b)
-    group = GroupColumns(columns=["generation"])
-    data >> [gen_a, gen_b] >> group
-
-if __name__ == "__main__":
-    distiset = pipeline.run()
-    print(distiset["default"]["train"]["grouped_generation"][0])
-# {[
-#   'Dữ liệu giả lập là dữ liệu được tạo ra nhân tạo, bắt chước cách sử dụng trong thế giới thực.',
-#   'Dữ liệu giả lập đề cập đến dữ liệu đã được tạo ra một cách nhân tạo.'
-# ]}
-```
-
-Như bạn có thể thấy, chúng ta có hai `phần hoàn thành` tổng hợp cho `lời nhắc` đã cho. Chúng ta có thể tăng cường sự đa dạng bằng cách khởi tạo các bước `TextGeneration` với một `system_prompt` cụ thể hoặc bằng cách truyền các đối số tạo cho `TransformersLLM`. Bây giờ hãy xem cách chúng ta có thể cải thiện chất lượng của các `phần hoàn thành` bằng EvolQuality.
-
-### EvolQuality
-
-EvolQuality tương tự như [EvolInstruct](./instruction_datasets.md#evolinstruct) - đó là một kỹ thuật nhắc nhở nhưng nó phát triển `các phần hoàn thành` thay vì `lời nhắc` đầu vào. Tác vụ lấy cả `lời nhắc` và `phần hoàn thành` và phát triển `phần hoàn thành` thành một phiên bản phản hồi tốt hơn cho `lời nhắc` dựa trên một tập hợp các tiêu chí. Phiên bản tốt hơn này được định nghĩa theo các tiêu chí để cải thiện tính hữu ích, mức độ liên quan, đào sâu, sáng tạo hoặc chi tiết. Bởi vì điều này tự động tạo ra phần hoàn thành thứ hai, chúng ta có thể sử dụng nó để thêm nhiều `phần hoàn thành` hơn vào tập dữ liệu. Về lý thuyết, chúng ta thậm chí có thể giả định rằng sự tiến hóa tốt hơn phần hoàn thành ban đầu và sử dụng nó làm phần hoàn thành `chosen` ngay lập tức.
-
-Lời nhắc được [triển khai trong distilabel](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/evol_quality) và phiên bản đơn giản hóa được hiển thị bên dưới:
-
-```bash
-Tôi muốn bạn đóng vai trò là một Trình viết lại phản hồi (Response Rewriter).
-Cho một lời nhắc và một phản hồi, hãy viết lại phản hồi thành một phiên bản tốt hơn.
-Phức tạp hóa lời nhắc dựa trên các tiêu chí sau:
-{{ criteria }}
-
-# Lời nhắc
-{{ input }}
-
-# Phản hồi
-{{ output }}
-
-# Phản hồi được cải thiện
-```
-
-Hãy sử dụng [lớp EvolQuality](https://distilabel.argilla.io/dev/components-gallery/tasks/evolquality/) để phát triển `lời nhắc` và `phần hoàn thành` tổng hợp từ [phần Tổng hợp mô hình](#model-pooling) thành một phiên bản tốt hơn. Đối với ví dụ này, chúng ta sẽ chỉ tiến hóa trong một thế hệ.
-
-```python
-from distilabel.llms import TransformersLLM
-from distilabel.steps.tasks import EvolQuality
-
-llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
-evol_quality = EvolQuality(llm=llm, num_evolutions=1)
-evol_quality.load()
-
-instruction = "Dữ liệu giả lập (synthetic data) là gì?"
-completion = "Dữ liệu giả lập là dữ liệu được tạo ra nhân tạo, bắt chước cách sử dụng trong thế giới thực."
-
-next(evol_quality.process([{
-    "instruction": instruction,
-    "response": completion
-}]))
-# Quá trình tạo dữ liệu giả lập thông qua việc nhắc nhở thủ công bao gồm việc tạo ra các tập dữ liệu nhân tạo bắt chước các kiểu sử dụng trong thế giới thực.
-```
-
-`Phản hồi` bây giờ phức tạp hơn và cụ thể hơn cho `hướng dẫn`. Đây là một khởi đầu tốt, nhưng như chúng ta đã thấy với EvolInstruct, các thế hệ tiến hóa không phải lúc nào cũng tốt hơn. Do đó, điều quan trọng là phải sử dụng các kỹ thuật đánh giá bổ sung để đảm bảo chất lượng của tập dữ liệu. Chúng ta sẽ khám phá điều này trong phần tiếp theo.
-
-## Tạo điểm số
-
-Điểm số là thước đo mức độ phản hồi này được ưa thích hơn phản hồi khác. Nhìn chung, những điểm số này có thể là tuyệt đối, chủ quan hoặc tương đối. Đối với khóa học này, chúng ta sẽ tập trung vào hai loại đầu tiên vì chúng có giá trị nhất để tạo các tập dữ liệu ưu tiên. Việc chấm điểm này là một cách đánh giá và nhận xét bằng cách sử dụng các mô hình ngôn ngữ và do đó có một số điểm tương đồng với các kỹ thuật đánh giá mà chúng ta đã thấy trong [chương về đánh giá](../3_evaluation/README.md). Cũng như các kỹ thuật đánh giá khác, điểm số và đánh giá thường yêu cầu các mô hình lớn hơn để phù hợp hơn với ưu tiên của con người.
-
-### UltraFeedback
-
-UltraFeedback là một kỹ thuật tạo ra điểm số và phê bình cho một `lời nhắc` nhất định và `phần hoàn thành` của nó.
-
-Điểm số dựa trên chất lượng của `phần hoàn thành` theo một tập hợp các tiêu chí. Có bốn tiêu chí chi tiết: `helpfulness` (tính hữu ích), `relevance` (mức độ liên quan), `deepening` (đào sâu) và `creativity` (sáng tạo). Chúng rất hữu ích nhưng nói chung, sử dụng các tiêu chí tổng thể là một khởi đầu tốt, cho phép chúng ta đơn giản hóa quá trình tạo điểm số. Điểm số có thể được sử dụng để xác định `phần hoàn thành` nào là `chosen` và phần nào là `rejected`. Bởi vì chúng là tuyệt đối, chúng cũng có thể được sử dụng làm bộ lọc thú vị cho các giá trị ngoại lệ trong tập dữ liệu, tìm các phần hoàn thành tệ nhất hoặc các cặp có ít nhiều sự khác biệt.
-
-Các phê bình được thêm vào để cung cấp lý do cho điểm số. Chúng có thể được sử dụng làm ngữ cảnh bổ sung để giúp chúng ta hiểu sự khác biệt giữa các điểm số. Mô hình ngôn ngữ tạo ra các phê bình sâu rộng rất hữu ích, nhưng điều này cũng làm tăng thêm chi phí và độ phức tạp cho quá trình vì việc tạo ra các phê bình tốn kém hơn so với việc tạo ra một token duy nhất để đại diện cho điểm số.
-
-Lời nhắc được [triển khai trong distilabel](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/templates/ultrafeedback) và phiên bản đơn giản hóa được hiển thị bên dưới:
-
-```bash
-Đánh giá đầu ra của mô hình dựa trên các tiêu chí khác nhau: Tính hữu ích, Mức độ liên quan, Đào sâu, Sáng tạo
-Vai trò của bạn là cung cấp một đánh giá tổng thể dựa trên các yếu tố trên.
-Chấm điểm đầu ra từ 1 đến 5 về chất lượng tổng thể.
-
-Trả lời theo định dạng sau: điểm số - lý do
-
-# Đầu vào
-{{ input }}
-
-# Phản hồi
-{{ output }}
-
-# Điểm số - Lý do
-```
-
-Hãy sử dụng [lớp UltraFeedback](https://distilabel.argilla.io/dev/components-gallery/tasks/ultrafeedback/) để đánh giá `lời nhắc` và `phần hoàn thành` tổng hợp từ [phần Tổng hợp mô hình](#model-pooling).
-
-```python
-from distilabel.llms import TransformersLLM
-from distilabel.steps.tasks import UltraFeedback
-
-llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
-ultrafeedback = UltraFeedback(llm=llm)
-ultrafeedback.load()
-
-instruction = "Dữ liệu giả lập (synthetic data) là gì?"
-completion_a = "Dữ liệu giả lập là dữ liệu được tạo ra nhân tạo, bắt chước cách sử dụng trong thế giới thực."
-completion_b = "Dữ liệu giả lập đề cập đến dữ liệu đã được tạo ra một cách nhân tạo."
-
-next(ultrafeedback.process([{
-    "instruction": instruction,
-    "generations": [completion_a, completion_b]
-}]))
-# [
-#     {
-#         'ratings': [4, 5],
-#         'rationales': ['có thể cụ thể hơn', 'định nghĩa tốt'],
-#     }
-# ]
-```
-
-## Các phương pháp hay nhất
-
-- Phương pháp đánh giá điểm số tổng thể thường rẻ hơn và dễ tạo hơn so với phê bình và điểm số cụ thể
-- Sử dụng các mô hình lớn hơn để tạo điểm số và phê bình
-- Sử dụng một tập hợp đa dạng các mô hình để tạo điểm số và phê bình
-- Lặp lại cấu hình của `system_prompt` và các mô hình
-
-## Các bước tiếp theo
-
-👨🏽‍💻 Lập trình -[Notebook bài tập](./notebooks/preference_dpo_dataset.ipynb) để tạo tập dữ liệu để tinh chỉnh hướng dẫn
-
-## Tài liệu tham khảo
-
-- [Tài liệu Distilabel](https://distilabel.argilla.io/latest/)
-- [Deita](https://arxiv.org/abs/2312.15685)
-- [UltraFeedback](https://arxiv.org/abs/2310.01377)
diff --git a/vi/README.md b/vi/README.md
deleted file mode 100644
index 77913375..00000000
--- a/vi/README.md
+++ /dev/null
@@ -1,95 +0,0 @@
-![smolcourse image](../banner.png)
-
-# Khoá học Mô hình ngôn ngữ cơ bản
-
-Đây là một khoá học thực hành về việc huấn luyện các mô hình ngôn ngữ (LM) cho các trường hợp sử dụng cụ thể. Khoá học này là cách thuận tiện để bắt đầu với việc điều chỉnh các mô hình ngôn ngữ, bởi vì mọi thứ đều có thể chạy được trên hầu hết các máy tính cá nhân. Tại đây, chúng ta không cần quá nhiều tài nguyên GPUs hay các dịch vụ trả phí để học tập. Khoá học được xây dựng dựa trên series mô hình [SmolLM2](https://github.com/huggingface/smollm/tree/main), nhưng bạn có thể áp dụng các kỹ năng học được ở đây cho các mô hình lớn hơn hoặc các mô hình ngôn ngữ nhỏ khác.
-
-*Lưu ý: Vì khóa học được dịch từ bản gốc tiếng Anh, chúng tôi sẽ giữ lại một số thuật ngữ gốc nhằm tránh gây hiểu lầm.*
-
-<a href="http://hf.co/join/discord">s
-<img src="https://img.shields.io/badge/Discord-7289DA?&logo=discord&logoColor=white"/>
-</a>
-
-<div style="background: linear-gradient(to right, #e0f7fa, #e1bee7, orange); padding: 20px; border-radius: 5px; margin-bottom: 20px; color: purple;">
-    <h2>Tham gia học ngay!</h2>
-    <p>Khoá học này mở và được đánh giá bởi cộng đồng. Để tham gia khoá học <strong>hãy tạo Pull Request (PR)</strong> và gửi bài làm của bạn để được review. Các bước thực hiện:</p>
-    <ol>
-        <li>Fork repo <a href="https://github.com/huggingface/smol-course/fork">tại đây</a></li>
-        <li>Đọc tài liệu, thực hiện thay đổi, làm bài tập, thêm ví dụ của riêng bạn</li>
-        <li>Tạo PR trên nhánh december_2024</li>
-        <li>Nhận review và merge</li>
-    </ol>
-    <p>Điều này sẽ giúp bạn học tập và xây dựng một khoá học có cộng đồng tham gia và luôn được cải thiện.</p>
-</div>
-
-Chúng ta có thể thảo luận về quá trình học tập và phát triển trong [thread này](https://github.com/huggingface/smol-course/discussions/2#discussion-7602932).
-
-## Nội dung khoá học
-
-Khoá học này cung cấp phương pháp thực hành để làm việc với các mô hình ngôn ngữ nhỏ, từ huấn luyện ban đầu đến triển khai lên sản phẩm.
-
-| Bài | Mô tả | Trạng thái | Ngày phát hành |
-|--------|-------------|---------|--------------|
-| [Tinh chỉnh theo chỉ thị (Instruction Tuning)](./1_instruction_tuning) | Học về huấn luyện có giám sát (SFT), định dạng chat, và thực hiện các chỉ thị cơ bản | ✅ Sẵn sàng | 3/12/2024 |
-| [Tinh chỉnh theo sự ưu tiên (Preference Alignment)](./2_preference_alignment) | Học các kỹ thuật DPO và ORPO để huấn luyện mô hình theo sự uy tiên của người dùng | ✅ Sẵn sàng  | 6/12/2024 |
-| [Tinh chỉnh hiệu quả tham số (Parameter-efficient Fine-tuning)](./3_parameter_efficient_finetuning) | Học về LoRA, prompt tuning và các phương pháp huấn luyện hiệu quả | ✅ Sẵn sàng | 9/12/2024 |
-| [Đánh giá mô hình (Evaluation)](./4_evaluation) | Sử dụng benchmark tự động và tạo đánh giá theo lĩnh vực cụ thể | [🚧 Đang thực hiện](https://github.com/huggingface/smol-course/issues/42) | 13/12/2024 |
-| [Mô hình đa phương thức (Vision-language Models)](./5_vision_language_models) | Điều chỉnh các mô hình đa phương thức (Multimodal models) cho các tác vụ thị giác-ngôn ngữ | [🚧 Đang thực hiện](https://github.com/huggingface/smol-course/issues/49) | 16/12/2024 |
-| [Dữ liệu nhân tạo (Synthetic Datasets)](./6_synthetic_datasets) | Tạo và đánh giá tập dữ liệu tổng hợp cho huấn luyện | 📝 Đã lên kế hoạch | 20/12/2024 |
-| [Triển khai mô hình (Inference)](./7_inference) | Triển khai mô hình một cách hiệu quả | 📝 Đã lên kế hoạch | 23/12/2024 |
-
-## Tại sao lại chọn Mô hình Ngôn ngữ Nhỏ?
-
-Mặc dù các mô hình ngôn ngữ lớn đã cho thấy khả năng ấn tượng, chúng thường yêu cầu tài nguyên tính toán đáng kể và có thể quá mức cần thiết cho các ứng dụng tập trung. Các mô hình ngôn ngữ nhỏ mang lại nhiều lợi thế cho các ứng dụng theo lĩnh vực cụ thể:
-
-- **Hiệu quả:** Yêu cầu ít tài nguyên tính toán hơn đáng kể để huấn luyện và triển khai
-- **Tùy chỉnh:** Dễ dàng tinh chỉnh phù hợp cho các lĩnh vực cụ thể
-- **Kiểm soát:** Dễ dàng hiểu và kiểm soát hành vi mô hình hơn
-- **Chi phí:** Chi phí huấn luyện và triển khai thấp hơn đáng kể
-- **Bảo mật:** Có thể triển khai trên mạng cục bộ mà không cần gửi dữ liệu tới API bên ngoài
-- **Công nghệ xanh:** Sử dụng tài nguyên hiệu quả (carbon footprint giảm)
-- **Nghiên cứu học thuật dễ dàng hơn:** Khởi đầu dễ dàng cho nghiên cứu học thuật trên mô hình ngôn ngữ với ít ràng buộc về tài nghiên tính toán
-
-## Yêu cầu tiên quyết
-
-Trước khi bắt đầu, hãy đảm bảo bạn có:
-- Hiểu biết cơ bản về machine learning (ML) và xử lý ngôn ngữ tự nhiên (NLP)
-- Quen thuộc với Python, PyTorch và thư viện `transformers`
-- Hiểu về mô hình ngôn ngữ và tập dữ liệu có nhãn
-
-## Cài đặt
-
-Chúng tôi duy trì khoá học như một package để bạn có thể cài đặt dễ dàng thông qua package manager. Chúng tôi gợi ý sử dụng [uv](https://github.com/astral-sh/uv), nhưng bạn có thể sử dụng các lựa chọn thay thế như `pip` hoặc `pdm`.
-
-### Sử dụng `uv`
-
-Với `uv` đã được cài đặt, bạn có thể cài đặt khoá học như sau:
-
-```bash
-uv venv --python 3.11.0
-uv sync
-```
-
-### Sử dụng `pip`
-
-Tất cả các ví dụ chạy trong cùng một môi trường **python 3.11**, vì vậy bạn nên tạo môi trường và cài đặt dependencies như sau:
-
-```bash
-# python -m venv .venv
-# source .venv/bin/activate
-pip install -r requirements.txt
-```
-
-### Google Colab
-
-Nếu dùng **Google Colab** bạn sẽ cần cài đặt dependencies một cách linh hoạt dựa trên phần cứng bạn đang sử dụng. Như sau:
-
-```bash
-pip install -r transformers trl datasets huggingface_hub
-```
-
-## Tham gia và chia sẻ
-
-Hãy chia sẻ khoá học này, để cùng nhau phát triển cộng đồng AI Việt Nam.
-
-[![Biểu đồ sao của khoá học](https://api.star-history.com/svg?repos=huggingface/smol-course&type=Date)](https://star-history.com/#huggingface/smol-course&Date)