Added PEFT docs (#1133)

Co-authored-by: Omar Sanseviero <[email protected]>
Co-authored-by: Pedro Cuenca <[email protected]>

docs/hub/_toctree.yml

@@ -79,6 +79,8 @@
         title: OpenCLIP
       - local: paddlenlp
         title: PaddleNLP
+      - local: peft
+        title: peft
       - local: rl-baselines3-zoo
         title: RL-Baselines3-Zoo
       - local: sample-factory

docs/hub/models-libraries.md

@@ -22,6 +22,7 @@ The table below summarizes the supported libraries and their level of integratio
 | [NeMo](https://github.com/NVIDIA/NeMo)                                      | Conversational AI toolkit built for researchers                                      | ✅ | ✅ | ✅ | ❌ |
 | [OpenCLIP](https://github.com/mlfoundations/open_clip)                      | Library for open-source implementation of OpenAI's CLIP                              | ❌ | ❌ | ✅ | ✅ |  
 | [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP)                      | Easy-to-use and powerful NLP library built on PaddlePaddle                           | ✅ | ✅ | ✅ | ✅ |
+| [PEFT](https://github.com/huggingface/peft)                      | Cutting-edge Parameter Efficient Fine-tuning Library                           | ✅ | ✅ | ✅ | ✅ |
 | [Pyannote](https://github.com/pyannote/pyannote-audio)                      | Neural building blocks for speaker diarization.                                      | ❌ | ❌ | ✅ | ❌ |
 | [PyCTCDecode](https://github.com/kensho-technologies/pyctcdecode)           | Language model supported CTC decoding for speech recognition                         | ❌ | ❌ | ✅ | ❌ |
 | [Pythae](https://github.com/clementchadebec/benchmark_VAE)                  | Unified framework for Generative Autoencoders in Python                              | ❌ | ❌ | ✅ | ✅ |

docs/hub/peft.md

@@ -0,0 +1,77 @@
+# Using PEFT at Hugging Face
+
+🤗 [Parameter-Efficient Fine-Tuning (PEFT)](https://huggingface.co/docs/peft/index) is a library for efficiently adapting pre-trained language models to various downstream applications without fine-tuning all the model’s parameters. 
+
+## Exploring PEFT on the Hub
+
+You can find PEFT models by filtering at the left of the [models page](https://huggingface.co/models?library=peft&sort=trending).
+
+
+## Installation
+
+To get started, you can check out the [Quick Tour in the PEFT docs](https://huggingface.co/docs/peft/quicktour). To install, follow the [PEFT installation guide](https://huggingface.co/docs/peft/install).
+You can also use the following one-line install through pip:
+
+```
+$ pip install peft
+```
+
+## Using existing models
+
+All PEFT models can be loaded from the Hub. To use a PEFT model you also need to load the base model that was fine-tuned, as shown below. Every fine-tuned model has the base model in it's model card.
+
+```py
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel, PeftConfig
+
+base_model = "mistralai/Mistral-7B-v0.1"
+adapter_model = "dfurman/Mistral-7B-Instruct-v0.2"
+
+model = AutoModelForCausalLM.from_pretrained(base_model)
+model = PeftModel.from_pretrained(model, adapter_model)
+tokenizer = AutoTokenizer.from_pretrained(base_model)
+
+model = model.to("cuda")
+model.eval()
+```
+
+Once loaded, you can pass your inputs to the tokenizer to prepare them, and call `model.generate()` in regular `transformers` fashion.
+
+```py
+inputs = tokenizer("Tell me the recipe for chocolate chip cookie", return_tensors="pt")
+
+with torch.no_grad():
+    outputs = model.generate(input_ids=inputs["input_ids"].to("cuda"), max_new_tokens=10)
+    print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0])
+```
+
+It outputs the following:
+
+```text
+Tell me the recipe for chocolate chip cookie dough.
+
+1. Preheat oven to 375 degrees F (190 degrees C).
+2. In a large bowl, cream together 1/2 cup (1 stick) of butter or margarine, 1/2 cup granulated sugar, and 1/2 cup packed brown sugar.
+3. Beat in 1 egg and 1 teaspoon vanilla extract.
+4. Mix in 1 1/4 cups all-purpose flour.
+5. Stir in 1/2 teaspoon baking soda and 1/2 teaspoon salt.
+6. Fold in 3/4 cup semisweet chocolate chips.
+7. Drop by
+```
+
+If you want to load a specific PEFT model, you can click `Use in PEFT` in the model card and you will be given a working snippet!
+
+<div class="flex justify-center">
+<img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/peft_repo_light_new.png"/>
+<img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/peft_repo.png"/>
+</div>
+<div class="flex justify-center">
+<img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/peft_snippet_light.png"/>
+<img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/peft_snippet.png"/>
+</div>
+
+## Additional resources
+
+* PEFT [repository](https://github.com/huggingface/peft)
+* PEFT [docs](https://huggingface.co/docs/peft/index)
+* PEFT [models](https://huggingface.co/models?library=peft&sort=trending)