Docs nits (#428)

* Add link to colab and studio lab * Rewording: paragraph -> section * Add explaination about hallucination * Add link to notebooks
huggingface · Jan 23, 2024 · b643d7f · b643d7f
1 parent ff293f0
commit b643d7f
Show file tree

Hide file tree

Showing 5 changed files with 37 additions and 7 deletions.
diff --git a/docs/source/tutorials/fine_tune_bert.mdx b/docs/source/tutorials/fine_tune_bert.mdx
@@ -16,6 +16,8 @@ limitations under the License.
 
 # Fine-tune BERT for Text Classification on AWS Trainium
 
+*There is a notebook version of that tutorial [here](https://github.com/huggingface/optimum-neuron/blob/main/notebooks/text-classification/notebook.ipynb)*.
+
 This tutorial will help you to get started with [AWS Trainium](https://aws.amazon.com/machine-learning/trainium/?nc1=h_ls) and Hugging Face Transformers. It will cover how to set up a Trainium instance on AWS, load & fine-tune a transformers model for text-classification
 
 You will learn how to:

diff --git a/docs/source/tutorials/fine_tune_llama_7b.mdx b/docs/source/tutorials/fine_tune_llama_7b.mdx
@@ -16,6 +16,8 @@ limitations under the License.
 
 # Fine-tune and Test Llama 2 7B on AWS Trainium 
 
+*There is a notebook version of that tutorial [here](https://github.com/huggingface/optimum-neuron/blob/main/notebooks/text-generation/llama2-7b-fine-tuning.ipynb)*.
+
 This tutorial will teach you how to fine-tune open LLMs like [Llama 2](https://huggingface.co/meta-llama/Llama-2-7b-hf) on AWS Trainium. In our example, we are going to leverage Hugging Face https://huggingface.co/docs/optimum-neuron/index, [Transformers](https://huggingface.co/docs/transformers/index) and https://huggingface.co/docs/datasets/index.
 
 You will learn how to:

diff --git a/docs/source/tutorials/llama2-13b-chatbot.mdx b/docs/source/tutorials/llama2-13b-chatbot.mdx
@@ -47,7 +47,7 @@ When exporting the model, we will specify two sets of parameters:
 
 Depending on your choice of parameters and inferentia host, this may take from a few minutes to more than an hour.
 
-For your convenience, we host a pre-compiled version of that model on the Hugging Face hub, so you can skip the export and start using the model immediately in paragraph 2.
+For your convenience, we host a pre-compiled version of that model on the Hugging Face hub, so you can skip the export and start using the model immediately in section 2.
 
 
 ```python
@@ -129,7 +129,7 @@ using a *inf2.24xlarge* instance.
 
 Once your model has been exported, you can generate text using the transformers library, as it has been described in [detail in this post](https://huggingface.co/blog/how-to-generate).
 
-If as suggested you skipped the first paragraph, don't worry: we will use a precompiled model already present on the hub instead.
+If as suggested you skipped the first section, don't worry: we will use a precompiled model already present on the hub instead.
 
 
 ```python
@@ -249,3 +249,9 @@ print(chat("My favorite color is blue. My favorite fruit is strawberry.", histor
 print(chat("Name a fruit that is on my favorite colour.", history, max_tokens))
 print(chat("What is the colour of my favorite fruit ?", history, max_tokens))
 ```
+
+<Warning>
+
+While very powerful, Large language models can sometimes *hallucinate*. We call *hallucinations* generated content that is irrelevant or made-up but presented by the model as if it was accurate. This is a flaw of LLMs and is not a side effect of using them on Trainium / Inferentia.
+
+</Warning>
diff --git a/docs/source/tutorials/stable_diffusion.mdx b/docs/source/tutorials/stable_diffusion.mdx
@@ -18,6 +18,8 @@ limitations under the License.
 
 ## Stable Diffusion
 
+*There is a notebook version of that tutorial [here](https://github.com/huggingface/optimum-neuron/blob/main/notebooks/stable-diffusion/stable-diffusion-txt2img.ipynb)*.
+
 🤗 `Optimum` extends `Diffusers` to support inference on the second generation of Neuron devices(powering Trainium and Inferentia 2). It aims at inheriting the ease of Diffusers on Neuron.
 
 To get started, make sure you have [configured your inf2 / trn1 instance](../installation), and installed optimum:
@@ -173,6 +175,8 @@ image.save("cat_on_bench.png")
 
 ## Stable Diffusion XL
 
+*There is a notebook version of that tutorial [here](https://github.com/huggingface/optimum-neuron/blob/main/notebooks/stable-diffusion/stable-diffusion-xl-txt2img.ipynb)*.
+
 Stable Diffusion XL (SDXL) is a latent diffusion model for text-to-image. Compared to the previous versions of Stable Diffusion models, it improves the quality of generated images with a times larger UNet.
 
 ### Compile Stable Diffusion XL
@@ -465,4 +469,4 @@ Inf2 instances contain one or more Neuron devices, and each Neuron device includ
 
 </Tip>
 
-Are there any other stable diffusion features that you want us to support in 🤗`Optimum-neuron`? Please file an issue to [`Optimum-neuron` Github repo](https://github.com/huggingface/optimum-neuron) or discuss with us on [HuggingFace’s community forum](https://discuss.huggingface.co/c/optimum/), cheers 🤗 !
+Are there any other stable diffusion features that you want us to support in 🤗`Optimum-neuron`? Please file an issue to [`Optimum-neuron` Github repo](https://github.com/huggingface/optimum-neuron) or discuss with us on [HuggingFace’s community forum](https://discuss.huggingface.co/c/optimum/), cheers 🤗 !
diff --git a/notebooks/text-generation/llama2-13b-chatbot.ipynb b/notebooks/text-generation/llama2-13b-chatbot.ipynb
@@ -19,7 +19,7 @@
     "\n",
     "## Prerequisite: Setup AWS environment\n",
     "\n",
-    "*you can skip that paragraph if you are already running this notebook on your instance.*\n",
+    "*you can skip that section if you are already running this notebook on your instance.*\n",
     "\n",
     "In this example, we will use the *inf2.48xlarge* instance with 12 Neuron devices, corresponding to 24 Neuron Cores and the [Hugging Face Neuron Deep Learning AMI](https://aws.amazon.com/marketplace/pp/prodview-gr3e6yiscria2).\n",
     "\n",
@@ -91,7 +91,7 @@
     "\n",
     "Depending on your choice of parameters and inferentia host, this may take from a few minutes to more than an hour.\n",
     "\n",
-    "For your convenience, we host a pre-compiled version of that model on the Hugging Face hub, so you can skip the export and start using the model immediately in paragraph 2."
+    "For your convenience, we host a pre-compiled version of that model on the Hugging Face hub, so you can skip the export and start using the model immediately in section 2."
    ]
   },
   {
@@ -226,7 +226,7 @@
     "\n",
     "Once your model has been exported, you can generate text using the transformers library, as it has been described in [detail in this post](https://huggingface.co/blog/how-to-generate).\n",
     "\n",
-    "If as suggested you skipped the first paragraph, don't worry: we will use a precompiled model already present on the hub instead."
+    "If as suggested you skipped the first section, don't worry: we will use a precompiled model already present on the hub instead."
    ]
   },
   {
@@ -418,6 +418,22 @@
    "source": [
     "print(chat(\"What is the colour of my favorite fruit ?\", history, max_tokens))"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38df6da1",
+   "metadata": {},
+   "source": [
+    "**Warning**: While very powerful, Large language models can sometimes *hallucinate*. We call *hallucinations* generated content that is irrelevant or made-up but presented by the model as if it was accurate. This is a flaw of LLMs and is not a side effect of using them on Trainium / Inferentia."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f8b4dc6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -436,7 +452,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.9.16"
   }
  },
  "nbformat": 4,