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 \n",
- "
Exercise: Process a dataset for SFT \n",
- "
Take a dataset from the Hugging Face hub and process it for SFT.
\n",
- "
Difficulty Levels
\n",
- "
🐢 Convert the `HuggingFaceTB/smoltalk` dataset into chatml format.
\n",
- "
🐕 Convert the `openai/gsm8k` dataset into chatml format.
\n",
+ "
练习：为 SFT 准备数据集 \n",
+ "
从 HuggingFace 上找一个数据集，然后为后续 SFT 转化它
\n",
+ "
难度等级
\n",
+ "
🐢 将 `HuggingFaceTB/smoltalk` 数据集转换为 ChatML 形式
\n",
+ "
🐕 将 `openai/gsm8k` 数据集转换为 ChatML 形式
\n",
"
\n",
- "
Exercise: Fine-Tuning SmolLM2 with SFTTrainer \n",
- "
Take a dataset from the Hugging Face hub and finetune a model on it.
\n",
- "
Difficulty Levels
\n",
- "
🐢 Use the `HuggingFaceTB/smoltalk` dataset
\n",
- "
🐕 Try out the `bigcode/the-stack-smol` dataset and finetune a code generation model on a specific subset `data/python`.
\n",
- "
🦁 Select a dataset that relates to a real world use case your interested in
\n",
+ "
练习：用 SFTTrainer 微调 SmolLM2 \n",
+ "
从 HuggingFace 上选取一个数据集然后微调模型
\n",
+ "
难度等级
\n",
+ "
🐢 使用 `HuggingFaceTB/smoltalk` 数据集
\n",
+ "
🐕 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
\n",
+ "
🦁 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
\n",
"
\n",
- "
Bonus Exercise: Generate with fine-tuned model \n",
- "
🐕 Use the fine-tuned to model generate a response, just like with the base example..
\n",
+ "
额外练习：用微调过的模型来生成 \n",
+ "
🐕 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
\n",
"
\n",
- "
Exercise: Aligning SmolLM2 with DPOTrainer \n",
- "
Take a dataset from the Hugging Face hub and align a model on it.
\n",
- "
Difficulty Levels
\n",
- "
🐢 Use the `trl-lib/ultrafeedback_binarized` dataset
\n",
- "
🐕 Try out the `argilla/ultrafeedback-binarized-preferences` dataset
\n",
- "
🦁 Select a dataset that relates to a real-world use case you’re interested in, or use the model you trained in \n",
- " 1_instruction_tuning
\n",
+ "
练习：用 DPOTrainer 对 SmolLM2 做对齐训练 \n",
+ "
从 Hugging Face hub 上找一个数据集并在上面对齐模型。
\n",
+ "
难度等级
\n",
+ "
🐢 使用 `trl-lib/ultrafeedback_binarized` 数据集
\n",
+ "
🐕 使用 `argilla/ultrafeedback-binarized-preferences` 数据集
\n",
+ "
🦁 选用你感兴趣的真实世界数据集，或者使用你在上一章 SFT 训练好的模型
\n",
"
\n",
- "
🦁 change the model to the path or repo id of the model you trained in 1_instruction_tuning
\n",
+ "
🦁 如果你想使用第一章 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
\n",
"
\n",
- "
Exercise: Aligning SmolLM2 with ORPOTrainer \n",
- "
Take a dataset from the Hugging Face hub and align a model on it.
\n",
- "
Difficulty Levels
\n",
- "
🐢 Use the `trl-lib/ultrafeedback_binarized` dataset
\n",
- "
🐕 Try out the `argilla/ultrafeedback-binarized-preferences` dataset
\n",
- "
🦁 Try on a subset of mlabonne's `orpo-dpo-mix-40k` dataset
\n",
+ "
练习：借助 ORPOTrainer 对齐 SmolLM2 模型 \n",
+ "
从 Hugging Face hub 上找一个数据集并在上面对齐模型。
\n",
+ "
难度等级
\n",
+ "
🐢 使用 `trl-lib/ultrafeedback_binarized` 数据集
\n",
+ "
🐕 尝试使用 `argilla/ultrafeedback-binarized-preferences` 数据集
\n",
+ "
🦁 尝试使用 `mlabonne/orpo-dpo-mix-40k` 数据集的一个子集进行训练
\n",
"
\n",
- "
Exercise: Define LoRA parameters for finetuning \n",
- "
Take a dataset from the Hugging Face hub and finetune a model on it.
\n",
- "
Difficulty Levels
\n",
- "
🐢 Use the general parameters for an abitrary finetune
\n",
- "
🐕 Adjust the parameters and review in weights & biases.
\n",
- "
🦁 Adjust the parameters and show change in inference results.
\n",
+ "
练习：为微调定义好 LoRA 相关参数 \n",
+ "
从 Hugging Face hub 找一个合适的数据然后微调模型
\n",
+ "
难度等级
\n",
+ "
🐢 使用默认的 LoRA 超参数直接微调
\n",
+ "
🐕 改变一些超参数，学习通过 weights & biases 平台查看
\n",
+ "
🦁 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
\n",
"
\n",
- "
Bonus Exercise: Load LoRA Adapter \n",
- "
Use what you learnt from the ecample note book to load your trained LoRA adapter for inference.
\n",
- "
\n",
+ "
额外练习：载入自己训练的 LoRA Adapter \n",
+ "
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
\n",
+ "
\n",
- "
Exercise: Evaluate Your Own Model \n",
- "
Now that you've seen how to evaluate models on specific domains, try evaluating a model on a domain that interests you.
\n",
- "
Difficulty Levels
\n",
- "
🐢 Use the existing medical domain tasks but evaluate a different model from the Hugging Face hub
\n",
- "
🐕 Create a new domain evaluation by selecting different MMLU tasks (e.g., computer science, mathematics, physics)
\n",
- "
🦁 Create a custom evaluation task using LightEval's task framework and evaluate models on your specific domain
\n",
+ "
练习：评测你的模型 \n",
+ "
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。
\n",
+ "
难度等级
\n",
+ "
🐢 从 Hugging Face hub 另找一个模型，在已有的医学领域测试模型
\n",
+ "
🐕 选择一个不同的 MMLU 任务（如 comupter science、mathematics、physcics），进行模型测试
\n",
+ "
🦁 用 LightEval 自定义评测任务，测试你选的模型
\n",
"
\n",
- "
Exercise: Fine-Tuning SmolVLM with SFTTrainer \n",
- "
Take a dataset from the Hugging Face hub and finetune a model on it.
\n",
- "
Difficulty Levels
\n",
- "
🐢 Use the `HuggingFaceM4/ChartQA` dataset for SFT training run.
\n",
- "
🐕 Use the fine-tuned to model generate a response, and improve upon the base example.
\n",
- "
🦁 Try out the other datasets and show improvement.
\n",
+ "
练习：用 SFTTrainer 微调 SmolVLM \n",
+ "
从 Hugging Face hub 找一个数据，在该数据集上微调模型
\n",
+ "
难度等级
\n",
+ "
🐢 用 `HuggingFaceM4/ChartQA` 数据集进行 SFT
\n",
+ "
🐕 用微调过的模型生成回答，和未微调的模型进行效果对比
\n",
+ "
🦁 尝试使用不同的数据集进行微调训练，并对比效果
\n",
"
\n",
" \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：
-- **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
-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.
-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。
-## 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 "
+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。
-## 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",
"\n",
- "
Exercise: Generate a dataset for instruction tuning \n",
- "
Now that you've seen how to generate a dataset for instruction tuning, try generating a dataset for instruction tuning.
\n",
- "
Difficulty Levels
\n",
- "
🐢 Generate an instruction tuning dataset
\n",
- "
🐕 Generate a dataset for instruction tuning with seed data
\n",
- "
🦁 Generate a dataset for instruction tuning with seed data and with instruction evolution
\n",
+ "
练习：为指令微调生成数据集\t \n",
+ "
通过学习，你可以尝试为指令微调生成数据集
\n",
+ "
难度等级
\n",
+ "
🐢 为指令微调生成数据集
\n",
+ "
🐕 借助种子数据为指令微调生成数据集
\n",
+ "
🦁 借助种子数据和 EvolInstruct 为指令微调生成数据集
\n",
"
\n",
- "
Exercise: Generate a dataset for preference alignment \n",
- "
Now that you've seen how to generate a dataset for preference alignment, try generating a dataset for preference alignment.
\n",
- "
Difficulty Levels
\n",
- "
🐢 Generate a dataset for preference alignment
\n",
- "
🐕 Generate a dataset for preference alignment with response evolution
\n",
- "
🦁 Generate a dataset for preference alignment with response evolution and model pooling
\n",
+ "
练习：为偏好对齐生成数据集 \n",
+ "
通过学习，你可以尝试为偏好对齐生成数据集
\n",
+ "
难度等级
\n",
+ "
🐢 为偏好对齐生成数据集
\n",
+ "
🐕 为偏好对齐生成数据集，对回答进行迭代优化
\n",
+ "
🦁 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
\n",
"
-
Participation is open, free, and now!
-
This course is open and peer reviewed. To get involved with the course open a pull request and submit your work for review. Here are the steps:
+
欢迎参与！
+
本课程完全免费开放，并已经过同行评审。你可以通过 pull request 参与课程，并提交你的成果。具体步骤如下：
- Fork the repo here
- Read the material, make changes, do the exercises, add your own examples.
- Open a PR on the december_2024 branch
- Get it reviewed and merged
+ 在 这里 fork 一下代码仓库
+ 阅读学习材料，完成练习，提交你的成果。
+ 向 december_2024 分支上提出 PR
+ 等待 PR 通过
-
This should help you learn and to build a community-driven course that is always improving.
+
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
\n",
- "
Ejercicio: Procesar un conjunto de datos para SFT (Fine-Tuning Supervisado) \n",
- "
Toma un conjunto de datos desde el repositorio de Hugging Face y procésalo para SFT.
\n",
- "
Niveles de dificultad
\n",
- "
🐢 Convierte el conjunto de datos 'HuggingFaceTB/smoltalk' al formato chatml.
\n",
- "
🐕 Convierte el conjunto de datos 'openai/gsm8k' al formato chatml.
\n",
- "
\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from IPython.core.display import display, HTML\n",
- "\n",
- "display(\n",
- " HTML(\n",
- " \"\"\"\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: 🐢 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": {
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- "base_uri": "https://localhost:8080/",
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- "display(\n",
- " HTML(\n",
- " \"\"\"\n",
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- " )\n",
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- {
- "cell_type": "code",
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- "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": {
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- },
- "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",
- "\n",
- "
Ejercicio: Fine-Tuning de SmolLM2 con SFTTrainer \n",
- "
Toma un conjunto de datos desde el repositorio de Hugging Face y realiza un fine-tuning de un modelo con él.
\n",
- "
Niveles de dificultad
\n",
- "
🐢 Usa el conjunto de datos `HuggingFaceTB/smoltalk`
\n",
- "
🐕 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`.
\n",
- "
🦁 Selecciona un conjunto de datos que esté relacionado con un caso de uso del mundo real que te interese.
\n",
- "
\n",
- "
Ejercicio adicional: Generar con el modelo ajustado \n",
- "
🐕 Utiliza el modelo ajustado para generar una respuesta, de la misma forma que con el ejemplo base.
\n",
- "
\n",
- "
Ejercicio: Alineando SmolLM2 con DPOTrainer \n",
- "
Toma un dataset del Hub de Hugging Face y alinea un modelo sobre él.
\n",
- "
Niveles de dificultad
\n",
- "
🐢 Usa el dataset `trl-lib/ultrafeedback_binarized`
\n",
- "
🐕 Prueba el dataset `argilla/ultrafeedback-binarized-preferences`
\n",
- "
🦁 Selecciona un dataset relacionado con un caso de uso real que te interese, o usa el modelo que entrenaste en \n",
- " 1_instruction_tuning
\n",
- "
\n",
- "
🦁 cambia la ruta del modelo o el ID del repositorio del modelo que entrenaste en 1_instruction_tuning
\n",
- "
\n",
- "
Ejercicio: Alinea SmolLM2 con DPOTrainer \n",
- "
Elige un dataset del hub de Hugging Face y alinea un modelo con él.
\n",
- "
Niveles de Dificultad
\n",
- "
\n",
- " 🐢 Usa el dataset `trl-lib/ultrafeedback_binarized` \n",
- " 🐕 Prueba el dataset `argilla/ultrafeedback-binarized-preferences` \n",
- " 🦁 Trabaja con un subconjunto del dataset `mlabonne/orpo-dpo-mix-40k` \n",
- " \n",
- "
")
-peft_model_id = ""
-model = PeftModel.from_pretrained(base_model, peft_model_id)
-```
-
-
-
-## 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",
- " \n",
- "
Ejercicio: Define los parámetros de LoRA para fine-tuning \n",
- "
Escoge un conjunto de datos de Hugging Face y realiza fine-tuning a un modelo con él.
\n",
- "
Niveles de dificultad
\n",
- "
🐢 Usa los parámetros generales para un fine-tuning arbitrario
\n",
- "
🐕 Ajusta los parámetros y revisalos en weights & biases.
\n",
- "
🦁 Ajusta los parámetros y muestra el cambio en los resultados de inferencia.\n",
- "
\n",
- "
Ejercicio Adicional: Carga el Adaptador LoRA \n",
- " Utiliza lo que aprendiste del notebook de ejemplo para cargar tu adaptador LoRA entrenado para la inferencia.
\n",
- ""
- ]
- },
- {
- "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",
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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/",
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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",
- "# Carga el modelo Lora\n",
- "model = PeftModel.from_pretrained(model, peft_model_id)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
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- "outputs": [],
- "source": [
- "batch = tokenizer(\"Machine learning es: \", 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/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 \n",
- "
Ejercicio: Evalúa tu propio modelo \n",
- "
Ahora que has visto cómo evaluar modelos en dominios específicos, intenta evaluar un modelo en un dominio que te interese.
\n",
- "
Niveles de dificultad
\n",
- "
🐢 Usa las tareas existentes del dominio médico pero evalúa un modelo diferente del repositorio de Hugging Face
\n",
- "
🐕 Crea una nueva evaluación de dominio seleccionando diferentes tareas de MMLU (por ejemplo, ciencias de la computación, matemáticas, física)
\n",
- "
🦁 Crea una tarea de evaluación personalizada utilizando el marco de tareas de LightEval y evalúa modelos en tu dominio específico
\n",
- "
-
-
-
-
¡La participación es abierta, gratuita y ahora!
-
Este curso es abierto y revisado por la comunidad. Para participar, simplemente abre un pull request y envía tu trabajo para su revisión. Sigue estos pasos:
-
- Haz un fork del repositorio aquí
- Lee el material, haz cambios, completa los ejercicios y agrega tus ejemplos.
- Abre un PR en la rama december_2024
- Haz que se revise y se fusione
-
-
Este proceso te ayudará a aprender y a construir un curso dirigido por la comunidad que mejora constantemente.
-
\n",
- "
演習: SFTTrainerを使用したSmolLM2の微調整 \n",
- "
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。
\n",
- "
難易度レベル
\n",
- "
🐢 `HuggingFaceTB/smoltalk`データセットを使用
\n",
- "
🐕 `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。
\n",
- "
🦁 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。
\n",
- "
\n",
- "
追加の演習: 微調整されたモデルでの生成 \n",
- "
🐕 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。
\n",
- "
\n",
- "
演習: SFTTrainerを使用したSmolLM2の微調整 \n",
- "
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。
\n",
- "
難易度レベル
\n",
- "
🐢 `HuggingFaceTB/smoltalk`データセットを使用
\n",
- "
🐕 `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。
\n",
- "
🦁 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。
\n",
- "
\n",
- "
追加の演習: 微調整されたモデルでの生成 \n",
- "
🐕 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。
\n",
- "
")
-peft_model_id = ""
-model = PeftModel.from_pretrained(base_model, peft_model_id)
-```
-
-
-
-## 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",
- "\n",
- "
演習: 微調整のためのLoRAパラメータを定義 \n",
- "
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。
\n",
- "
難易度レベル
\n",
- "
🐢 一般的なパラメータを使用して任意の微調整を行う
\n",
- "
🐕 パラメータを調整し、重みとバイアスでレビューする
\n",
- "
🦁 パラメータを調整し、推論結果の変化を示す
\n",
- "
ボーナス演習: LoRAアダプタの読み込み \n",
- " 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。
\n",
- ""
- ]
- },
- {
- "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 "
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- ""
- ]
- },
- "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: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: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": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "['User: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",
- "\n",
- " \n",
- " Your browser does not support the video tag.\n",
- " "
- ]
- },
- {
- "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.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": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- ""
- ]
- },
- "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: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: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": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "['User: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",
- "\n",
- " \n",
- " Your browser does not support the video tag.\n",
- " "
- ]
- },
- {
- "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.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：
-
-
-
-- **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": ""},
- {"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。
-
-
-
-## 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 "
-
-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。
-
-
-
-## 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",
- "\n",
- "
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 \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。
\n",
- "
難易度レベル
\n",
- "
🐢 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
\n",
- "
🐕 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
\n",
- "
🦁 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
\n",
- "
\n",
- "
演習: 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。
\n",
- "
難易度レベル
\n",
- "
🐢 嗜好調整のためのデータセットを生成する
\n",
- "
🐕 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
\n",
- "
🦁 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
\n",
- "
-
-
-
-
参加は無料で、今すぐ始められます！
-
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、プルリクエストを開く 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：
-
- リポジトリをフォークします こちら
- 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。
- december_2024ブランチでプルリクエストを開きます
- レビューを受けてマージされます
-
-
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。
-
\n",
- "
연습: SFT를 위한 데이터 가공하기 \n",
- "
Hugging Face 허브에서 데이터셋을 가져온 후 SFT를 적용할 수 있게 가공해보세요.
\n",
- "
난이도
\n",
- "
🐢 `HuggingFaceTB/smoltalk` 데이터셋을 chatml 형식으로 변환하기
\n",
- "
🐕 `openai/gsm8k` 데이터셋을 chatml 형식으로 변환하기
\n",
- "
\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from IPython.core.display import display, HTML\n",
- "\n",
- "display(\n",
- " HTML(\n",
- " \"\"\"\n",
- "\"\"\"\n",
- " )\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 241,
- "referenced_widgets": [
- "c2d74a42fb574b8892d0a288fd92f0a6",
- "056b9ef5706843b19cd62fce75743afb",
- "17b4d81e40564a53bb79be9fbef4918e",
- "951f60cddcb84dfdbbdf2058369f0541",
- "646484cf7a36444daebe1dfe4a0e4150",
- "e2f0c39ce1c046e8acb150dfbfaf5aa8",
- "7eb12d70d2b542a7b651c7680f590279",
- "ea1f9cb22abf4e7d9f6e76fc86c03387",
- "00c9f5ca71b84df4b26acee72c97fefb",
- "505f96bc0c7843bcb1498ba1c1ba5f06",
- "635cc2881a1e4b8788bb26c356740e04",
- "a6ee323c13904525a99c6f092ba96e18",
- "67fffe7d6f8c4963972b408529e05532",
- "0055b6b628934affaf88bc58a1572bb6",
- "aafbbb9fc5164fa3a88193bfd33d2f79",
- "606e39d53ed64967a60337418c71c595",
- "26b15fa18b1b4963a1ba76a76675e7ee",
- "db09ab1f79db4f3a8de77f0348eca0f7",
- "de04f344a8d4428e8ba1836a563d8aa1",
- "03c09673186046d799d6f487d6623e6b",
- "1cc682330b24431b8812c73041e987d0",
- "dafe748a452148038779f6a62a22a4ec",
- "addad1c100024c44a0959978153da9a8",
- "9bea2a23db644ad19b708d10e35d54ee",
- "d1174b127571420593971166fbb1966b",
- "add90ed3746d4293a1b71198137a892c",
- "8def25e6389f4e6192b517b6e80aa05e",
- "c9747e7a810f413ba1ea108307e3ad1d",
- "d0ea49d1d90f4d34bf2ae70efa96946e",
- "59d0997b85614384bbfebeee928340b6",
- "269920491c134501873e0110367bc984",
- "384d26051c04460e8870a3ffe9406c48",
- "8e8a0e89a50646c897e546c4077db79e",
- "ff60308921f9432683acbcd6d29fb78f",
- "3bc8f6339f4e4a3b961d810255c5573e",
- "4780ad263ec04b1a97525d985e102049",
- "488feef55878426bbf1c753c6d58735b",
- "560ba45d70ca431dadeb327d234c330a",
- "04d0a6f74af346f7bc696951949063c8",
- "2a18ce941b0f4cef8307988ef898b47f",
- "194e3fda3635466b998f96e3dc22746a",
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- "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)"
- ]
- },
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- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 381
- },
- "id": "81fQeazltFlu",
- "outputId": "36cf7148-9881-4f13-d0ce-76c82c4ab219"
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- "outputs": [
- {
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- ""
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- "output_type": "display_data"
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- "source": [
- "display(\n",
- " HTML(\n",
- " \"\"\"\n",
- "\"\"\"\n",
- " )\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true,
- "id": "bWUSv7NMtFlu"
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- "outputs": [],
- "source": [
- "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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- "id": "qlXCuRKotFlu"
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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",
- "\n",
- "
연습: SFTTrainer를 활용한 SmolLM2 미세 조정 \n",
- "
Hugging Face 허브에서 가져온 데이터셋으로 모델은 미세 조정해보세요.
\n",
- "
난이도
\n",
- "
🐢 `HuggingFaceTB/smoltalk` 데이터셋 사용해보기
\n",
- "
🐕 `bigcode/the-stack-smol` 데이터셋의 하위 집합인 `data/python`을 활용해 코드 생성 모델 미세 조정해보기
\n",
- "
🦁 실제 사용 사례와 관련된 관심 있는 데이터셋 사용해보기
\n",
- "
\n",
- "
추가 연습: 미세 조정 모델로 응답 생성하기 \n",
- "
🐕 기본 예제에서와 마찬가지로 미세 조정된 모델을 사용하여 응답을 생성해보세요.
\n",
- "
")
-peft_model_id = ""
-model = PeftModel.from_pretrained(base_model, peft_model_id)
-```
-
-
-
-## 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",
- "\n",
- "
연습: 미세 조정을 위한 LoRA 파라미터 정의하기 \n",
- "
Hugging Face 허브에서 가져온 데이터셋으로 모델을 미세 조정 해보세요.
\n",
- "
난이도
\n",
- "
🐢 일반적인 파라미터 값을 사용하여 임의로 미세 조정 진행하기
\n",
- "
🐕 파라미터를 조정하고 가중치와 편향 검토해보기
\n",
- "
🦁 파라미터를 조정하고 추론 결과 변화 확인하기
\n",
- "
추가 연습: LoRA 어댑터 불러오기 \n",
- " 예제 노트북에서 배운 내용을 활용하여 학습된 LoRA 어댑터를 불러오고 추론에 사용하세요.
\n",
- ""
- ]
- },
- {
- "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",
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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": [
- "## 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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- ]
- },
- "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",
- "# LoRA 모델 불러오기\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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- },
- "gpuClass": "standard",
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- "language": "python",
- "name": "python3"
- },
- "language_info": {
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- },
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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
index 8d6abe0f..00000000
--- a/ko/README.md
+++ /dev/null
@@ -1,93 +0,0 @@
-
-
-# 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.
-
-
-
-
-
-
지금 바로 참여하세요!
-
이 과정은 열려 있으며 다른 사용자와의 상호 검토를 진행할 수 있습니다. 이 과정에 참여하려면 pull request(PR) 를 열고 검토 받을 수 있도록 결과물을 제출하세요. 다음 단계를 따르면 됩니다:
-
- 여기 에서 레포지토리를 fork하세요.자료를 읽고, 바꿔 보고, 실습해보고, 나만의 예제를 추가해보세요.
- december-2024 브랜치에 PR을 보내세요.
- 검토가 끝나면 december-2024 브랜치에 병합됩니다.
-
-
이 과정은 학습에 도움이 될 뿐만 아니라 지속적으로 발전하는 커뮤니티 기반 코스를 형성하는 데에도 기여할 것입니다.
-
\n",
- "
Exercício: Processe um conjunto de dados para SFT \n",
- "
Pegue um conjunto de dados do hub do Hugging Face e processe-o para SFT.
\n",
- "
Níveis de Dificuldade
\n",
- "
🐢 Converta o conjunto de dados `HuggingFaceTB/smoltalk` para o formato chatml.
\n",
- "
🐕 Converta o conjunto de dados `openai/gsm8k` para o formato chatml.
\n",
- "
\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from IPython.core.display import display, HTML\n",
- "\n",
- "display(\n",
- " HTML(\n",
- " \"\"\"\n",
- "\"\"\"\n",
- " )\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 241,
- "referenced_widgets": [
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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: 🐢 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,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 381
- },
- "id": "81fQeazltFlu",
- "outputId": "36cf7148-9881-4f13-d0ce-76c82c4ab219"
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(\n",
- " HTML(\n",
- " \"\"\"\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",
- "\n",
- "
Exercício: Ajuste fino do SmolLM2 com SFTTrainer \n",
- "
Pegue um conjunto de dados do hub do Hugging Face e faça um ajuste fino em um modelo.
\n",
- "
Níveis de Dificuldade
\n",
- "
🐢 Use o conjunto de dados `HuggingFaceTB/smoltalk`.
\n",
- "
🐕 Experimente o conjunto de dados `bigcode/the-stack-smol` e ajuste um modelo de geração de código no subconjunto específico `data/python`.
\n",
- "
🦁 Selecione um conjunto de dados relacionado a um caso de uso do mundo real que seja do seu interesse.
\n",
- "
\n",
- "
Exercício bônus: Gerar com o modelo ajustado \n",
- "
🐕 Use o ajuste fino para modelar e gerar uma resposta, assim como no exemplo básico.
\n",
- "
\n",
- "
Exercício: Alinhamento do SmolLM2 com o DPOTrainer \n",
- "
Pegue um conjunto de dados do hub do Hugging Face e alinhe um modelo a ele.
\n",
- "
Níveis de Dificuldade
\n",
- "
🐢 Use o conjunto de dados `trl-lib/ultrafeedback_binarized`.
\n",
- "
🐕 Experimente o conjunto de dados `argilla/ultrafeedback-binarized-preferences.
\n",
- "
🦁 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",
- " 1_instruction_tuning .
\n",
- "
\n",
- "
🦁 altere o modelo para o caminho ou o ID do repositório do modelo em que você treinou o 1_instruction_tuning .
\n",
- "
\n",
- "
Exercício: Alinhamento do SmolLM2 com o DPOTrainer \n",
- "
Pegue um conjunto de dados do hub do Hugging Face e alinhe um modelo a ele.
\n",
- "
Níveis de Dificuldade
\n",
- "
🐢 Use o conjunto de dados `trl-lib/ultrafeedback_binarized`.
\n",
- "
🐕 Experimente usar o conjunto de dados `argilla/ultrafeedback-binarized-preferences.
\n",
- "
🦁 Experimente usar um subconjunto do conjunto de dados `orpo-dpo-mix-40k` de mlabonne.
\n",
- "
")
-peft_model_id = ""
-model = PeftModel.from_pretrained(base_model, peft_model_id)
-```
-
-
-
-## 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",
- " \n",
- "
Exercício: Definir parâmetros LoRA para ajuste fino \n",
- "
Escolha um conjunto de dados do hub do Hugging Face e ajuste um modelo com ele.
\n",
- "
Níveis de Dificuldade
\n",
- "
🐢 Use os parâmetros gerais para um ajuste fino arbitrário.
\n",
- "
🐕 Ajuste os parâmetros e revise em pesos & biases.
\n",
- "
🦁 Ajuste os parâmetros e mostre a mudança nos resultados de inferência.
\n",
- "
Exercício Bônus: Carregar Adaptador LoRA \n",
- " Use o que você aprendeu no caderno de exemplo para carregar seu adaptador LoRA treinado para inferência.
\n",
- ""
- ]
- },
- {
- "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",
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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"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
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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",
- "# Load the Lora model\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": {
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- },
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- "language": "python",
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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 \n",
- "
Exercício: Avalie Seu Próprio Modelo \n",
- "
Agora que você viu como avaliar modelos em domínios específicos, experimente avaliar um modelo de um domínio que lhe interessa.
\n",
- "
Níveis de Dificuldade
\n",
- "
🐢 Use as tarefas existentes do domínio médico, mas avalie um modelo diferente do hub do Hugging Face
\n",
- "
🐕 Crie uma nova avaliação de domínio selecionando diferentes tarefas do MMLU (por exemplo, ciência da computação, matemática, física)
\n",
- "
🦁 Crie uma tarefa de avaliação personalizada usando o framework de tarefas do LightEval e avalie modelos no seu domínio específico
\n",
- "
-
-
-
-
A participação é aberta a todos, gratuita e já está disponível!
-
Este curso é aberto e avaliado por pares (peer reviewed). Para começar o curso, abra um pull request (PR) e envie seu trabalho para revisão. Aqui estão as etapas:
-
- Dê um fork no repositório aqui
- Leia o material, faça alterações, faça os exercícios, adicione seus próprios exemplos
- Abra um PR no branch december_2024
- Tenha seu material revisado e mesclado no branch principal
-
-
Isso deve te ajudar a aprender e a construir um curso feito pela comunidade, que está sempre melhorando.
-
\n",
- "
Bài tập: Xử lý dataset cho SFT \n",
- "
Lấy một dataset từ Hugging Face hub và xử lý nó cho SFT.
\n",
- "
Các bài tập
\n",
- "
🐢 Chuyển đổi dataset `HuggingFaceTB/smoltalk` sang định dạng chatml .
\n",
- "
🐕 Chuyển đổi dataset `openai/gsm8k` sang định dạng chatml .
\n",
- "
\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from IPython.core.display import display, HTML\n",
- "\n",
- "display(\n",
- " HTML(\n",
- " \"\"\"\n",
- "\"\"\"\n",
- " )\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
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- },
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- "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)"
- ]
- },
- {
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- "execution_count": null,
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- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 381
- },
- "id": "81fQeazltFlu",
- "outputId": "36cf7148-9881-4f13-d0ce-76c82c4ab219"
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(\n",
- " HTML(\n",
- " \"\"\"\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",
- "\n",
- "
Bài tập: Fine-Tuning SmolLM2 với SFTTrainer \n",
- "
Chọn một bộ dự liệu từ Hugging Face hub và huấn luyện một mô hình trên bộ dữ liệu đó.
\n",
- "
Các bài tập
\n",
- "
🐢 Sử dụng bộ dữ liệu `HuggingFaceTB/smoltalk`
\n",
- "
🐕 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`.
\n",
- "
🦁 Chọn một bộ dữ liệu liên quan đến một lĩnh vực mà bạn quan tâm
\n",
- "
\n",
- "
Bài tập thêm: Sinh văn bản với mô hình vừa được huấn luyện \n",
- "
🐕 Sử dụng mô hình đã được huấn luyện để sinh ra phản hồi, giống như với ví dụ ban đầu.
\n",
- "
\n",
- "
Bài tập: Tinh chỉnh SmolLM2 với DPOTrainer \n",
- "
Lấy một tập dữ liệu từ Hugging Face hub và tinh chỉnh ưu tiên cho mô hình.
\n",
- "
Các bài tập
\n",
- "
🐢 Sử dụng bộ dữ liệu `trl-lib/ultrafeedback_binarized`
\n",
- "
🐕 Thử nghiệm với bộ dữ liệu `argilla/ultrafeedback-binarized-preferences`
\n",
- "
🦁 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",
- " 1_instruction_tuning
\n",
- "
\n",
- "
🦁 thay đổi đường dẫn mô hình thành đường dẫn hoặc repo id của mô hình bạn đã huấn luyện trong 1_instruction_tuning
\n",
- "
\n",
- "
Bài tập: Tinh chỉnh SmolLM2 với DPOTrainer \n",
- "
Lấy một tập bộ liệu từ Hugging Face hub và điều chỉnh mô hình trên bộ dữ liệu đó.
\n",
- "
Các bài tập
\n",
- "
🐢 Sử dụng bộ dữ liệu `trl-lib/ultrafeedback_binarized`
\n",
- "
🐕 Thử nghiệm với bộ dữ liệu `argilla/ultrafeedback-binarized-preferences`
\n",
- "
🦁 Thử nghiệm với một phần của bộ dữ liệu `orpo-dpo-mix-40k` của mlabonne
\n",
- "
")
-peft_model_id = ""
-model = PeftModel.from_pretrained(base_model, peft_model_id)
-```
-
-
-
-## 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",
- "\n",
- "
Bài tập: Xác định tham số LoRA cho tinh chỉnh \n",
- "
Lấy một bộ dữ liệu từ Hugging Face hub và tinh chỉnh một mô hình trên nó.
\n",
- "
Mức độ khó
\n",
- "
🐢 Sử dụng các tham số chung cho một bản tinh chỉnh tùy ý
\n",
- "
🐕 Điều chỉnh các tham số và so sánh trong weights & biases.
\n",
- "
🦁 Điều chỉnh các tham số và cho thấy sự thay đổi trong kết quả suy luận.
\n",
- "
Bài tập Bonus: Tải LoRA Adapter \n",
- " Sử dụng những gì bạn đã học được từ notebook ví dụ để tải adapter LoRA đã huấn luyện của bạn cho suy luận.
\n",
- ""
- ]
- },
- {
- "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",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 446,
- "referenced_widgets": [
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- "outputId": "92fcd520-ad2c-4488-8f66-664ffc566b89"
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- "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"
- ]
- },
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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",
- "# Tải Lora adapter\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/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.
-
-
-
-## 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 \n",
- "
Bài tập: Đánh giá mô hình của riêng bạn \n",
- "
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.
\n",
- "
Các mức độ khó
\n",
- "
🐢 Sử dụng các tác vụ lĩnh vực y tế hiện có nhưng đánh giá một mô hình khác từ Hugging Face Hub
\n",
- "
🐕 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)
\n",
- "
🦁 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
\n",
- "
\n",
- "
Bài tập: Tinh chỉnh SmolVLM với SFTTrainer \n",
- "
Hãy chọn một tập dữ liệu từ Hugging Face Hub và tinh chỉnh một mô hình trên đó.
\n",
- "
Các mức độ khó
\n",
- "
🐢 Sử dụng tập dữ liệu HuggingFaceM4/ChartQA để chạy huấn luyện SFT.
\n",
- "
🐕 Sử dụng mô hình đã được tinh chỉnh để tạo ra phản hồi và cải thiện dựa trên ví dụ cơ bản.
\n",
- "
🦁 Thử các tập dữ liệu khác và cho thấy sự cải thiện.
\n",
- "
"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{'image': , '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: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": [
- {
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- "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: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: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": [
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- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "['User: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",
- "\n",
- " \n",
- " Your browser does not support the video tag.\n",
- " "
- ]
- },
- {
- "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.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:
-
-
-
-- **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
-
-
-
-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.
-
-
-
-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": ""},
- {"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.
-
-
-
-## 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 " # 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.
-
-
-
-## 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",
- " \n",
- "
Bài tập: Tạo tập dữ liệu hướng dẫn \n",
- "
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.
\n",
- "
Các mức độ khó
\n",
- "
🐢 Tạo tập dữ liệu tinh chỉnh hướng dẫn
\n",
- "
🐕 Tạo tập dữ liệu để tinh chỉnh hướng dẫn với dữ liệu hạt giống (seed data)
\n",
- "
🦁 Tạo tập dữ liệu để tinh chỉnh hướng dẫn với dữ liệu hạt giống và với sự tiến hóa hướng dẫn (instruction evolution)
\n",
- "
\n",
- "
Bài tập: Tạo tập dữ liệu để điều chỉnh sở thích \n",
- "
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.
\n",
- "
Các mức độ khó
\n",
- "
🐢 Tạo tập dữ liệu để điều chỉnh sở thích
\n",
- "
🐕 Tạo tập dữ liệu để điều chỉnh sở thích với sự tiến hóa phản hồi (response evolution)
\n",
- "
🦁 Tạo tập dữ liệu để điều chỉnh sở thích với sự tiến hóa phản hồi và tổng hợp mô hình (model pooling)
\n",
- "
s
-
-
-
-
Tham gia học ngay!
-
Khoá học này mở và được đánh giá bởi cộng đồng. Để tham gia khoá học hãy tạo Pull Request (PR) và gửi bài làm của bạn để được review. Các bước thực hiện:
-
- Fork repo tại đây
- Đọc tài liệu, thực hiện thay đổi, làm bài tập, thêm ví dụ của riêng bạn
- Tạo PR trên nhánh december_2024
- Nhận review và merge
-
-
Điều này sẽ giúp bạn học tập và xây dựng một khoá học có cộng đồng tham gia và luôn được cải thiện.
-
