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[docs] Update index and quicktour (huggingface#1191)
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28 changes: 16 additions & 12 deletions docs/source/_toctree.yml
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- title: Task guides
sections:
- local: task_guides/image_classification_lora
title: Image classification using LoRA
- local: task_guides/seq2seq-prefix-tuning
title: Prefix tuning for conditional generation
- local: task_guides/clm-prompt-tuning
title: Prompt tuning for causal language modeling
- local: task_guides/semantic_segmentation_lora
title: Semantic segmentation using LoRA
- local: task_guides/ptuning-seq-classification
title: P-tuning for sequence classification
- local: task_guides/dreambooth_lora
title: Dreambooth fine-tuning with LoRA
- local: task_guides/token-classification-lora
title: LoRA for token classification
- local: task_guides/int8-asr
title: int8 training for automatic speech recognition
- local: task_guides/semantic-similarity-lora
title: Semantic similarity with LoRA
- title: LoRA
sections:
- local: task_guides/image_classification_lora
title: Image classification
- local: task_guides/semantic_segmentation_lora
title: Semantic segmentation
- local: task_guides/token-classification-lora
title: Token classification
- local: task_guides/semantic-similarity-lora
title: Semantic similarity
- local: task_guides/int8-asr
title: int8 training for automatic speech recognition
- local: task_guides/dreambooth_lora
title: DreamBooth

- title: Developer guides
sections:
Expand Down Expand Up @@ -59,6 +61,8 @@

- title: Reference
sections:
- local: package_reference/auto_class
title: AutoPeftModel
- local: package_reference/peft_model
title: PEFT model
- local: package_reference/config
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# PEFT

🤗 PEFT, or Parameter-Efficient Fine-Tuning (PEFT), is a library for efficiently adapting pre-trained language models (PLMs) to various downstream applications without fine-tuning all the model's parameters.
PEFT methods only fine-tune a small number of (extra) model parameters, significantly decreasing computational and storage costs because fine-tuning large-scale PLMs is prohibitively costly.
Recent state-of-the-art PEFT techniques achieve performance comparable to that of full fine-tuning.
🤗 PEFT (Parameter-Efficient Fine-Tuning) is a library for efficiently adapting large pretrained models to various downstream applications without fine-tuning all of a model's parameters because it is prohibitively costly. PEFT methods only fine-tune a small number of (extra) model parameters - significantly decreasing computational and storage costs - while yielding performance comparable to a fully fine-tuned model. This makes it more accessible to train and store large language models (LLMs) on consumer hardware.

PEFT is seamlessly integrated with 🤗 Accelerate for large-scale models leveraging DeepSpeed and [Big Model Inference](https://huggingface.co/docs/accelerate/usage_guides/big_modeling).
PEFT is integrated with the Transformers, Diffusers, and Accelerate libraries to provide a faster and easier way to load, train, and use large models for inference.

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## Supported methods

1. LoRA: [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/pdf/2106.09685.pdf)
2. Prefix Tuning: [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://aclanthology.org/2021.acl-long.353/), [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://arxiv.org/pdf/2110.07602.pdf)
3. P-Tuning: [GPT Understands, Too](https://arxiv.org/pdf/2103.10385.pdf)
4. Prompt Tuning: [The Power of Scale for Parameter-Efficient Prompt Tuning](https://arxiv.org/pdf/2104.08691.pdf)
5. AdaLoRA: [Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning](https://arxiv.org/abs/2303.10512)
6. [LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention](https://github.com/ZrrSkywalker/LLaMA-Adapter)
7. IA3: [Infused Adapter by Inhibiting and Amplifying Inner Activations](https://arxiv.org/abs/2205.05638)

## Supported models

The tables provided below list the PEFT methods and models supported for each task. To apply a particular PEFT method for
a task, please refer to the corresponding Task guides.

### Causal Language Modeling

| Model | LoRA | Prefix Tuning | P-Tuning | Prompt Tuning | IA3 |
|--------------| ---- | ---- | ---- | ---- | ---- |
| GPT-2 | ✅ | ✅ | ✅ | ✅ | ✅ |
| Bloom | ✅ | ✅ | ✅ | ✅ | ✅ |
| OPT | ✅ | ✅ | ✅ | ✅ | ✅ |
| GPT-Neo | ✅ | ✅ | ✅ | ✅ | ✅ |
| GPT-J | ✅ | ✅ | ✅ | ✅ | ✅ |
| GPT-NeoX-20B | ✅ | ✅ | ✅ | ✅ | ✅ |
| LLaMA | ✅ | ✅ | ✅ | ✅ | ✅ |
| ChatGLM | ✅ | ✅ | ✅ | ✅ | ✅ |

### Conditional Generation

| Model | LoRA | Prefix Tuning | P-Tuning | Prompt Tuning | IA3 |
| --------- | ---- | ---- | ---- | ---- | ---- |
| T5 | ✅ | ✅ | ✅ | ✅ | ✅ |
| BART | ✅ | ✅ | ✅ | ✅ | ✅ |

### Sequence Classification

| Model | LoRA | Prefix Tuning | P-Tuning | Prompt Tuning | IA3 |
| --------- | ---- | ---- | ---- | ---- | ---- |
| BERT | ✅ | ✅ | ✅ | ✅ | ✅ |
| RoBERTa | ✅ | ✅ | ✅ | ✅ | ✅ |
| GPT-2 | ✅ | ✅ | ✅ | ✅ | |
| Bloom | ✅ | ✅ | ✅ | ✅ | |
| OPT | ✅ | ✅ | ✅ | ✅ | |
| GPT-Neo | ✅ | ✅ | ✅ | ✅ | |
| GPT-J | ✅ | ✅ | ✅ | ✅ | |
| Deberta | ✅ | | ✅ | ✅ | |
| Deberta-v2 | ✅ | | ✅ | ✅ | |

### Token Classification

| Model | LoRA | Prefix Tuning | P-Tuning | Prompt Tuning | IA3 |
| --------- | ---- | ---- | ---- | ---- | --- |
| BERT | ✅ | ✅ | | | |
| RoBERTa | ✅ | ✅ | | | |
| GPT-2 | ✅ | ✅ | | | |
| Bloom | ✅ | ✅ | | | |
| OPT | ✅ | ✅ | | | |
| GPT-Neo | ✅ | ✅ | | | |
| GPT-J | ✅ | ✅ | | | |
| Deberta | ✅ | | | | |
| Deberta-v2 | ✅ | | | | |

### Text-to-Image Generation

| Model | LoRA | Prefix Tuning | P-Tuning | Prompt Tuning | IA3 |
| --------- | ---- | ---- | ---- | ---- | ---- |
| Stable Diffusion | ✅ | | | | |


### Image Classification

| Model | LoRA | Prefix Tuning | P-Tuning | Prompt Tuning | IA3 |
| --------- | ---- | ---- | ---- | ---- | ---- | ---- |
| ViT | ✅ | | | | |
| Swin | ✅ | | | | |

### Image to text (Multi-modal models)

We have tested LoRA for [ViT](https://huggingface.co/docs/transformers/model_doc/vit) and [Swin](https://huggingface.co/docs/transformers/model_doc/swin) for fine-tuning on image classification.
However, it should be possible to use LoRA for any [ViT-based model](https://huggingface.co/models?pipeline_tag=image-classification&sort=downloads&search=vit) from 🤗 Transformers.
Check out the [Image classification](/task_guides/image_classification_lora) task guide to learn more. If you run into problems, please open an issue.

| Model | LoRA | Prefix Tuning | P-Tuning | Prompt Tuning | IA3 |
| --------- | ---- | ---- | ---- | ---- | ---- |
| Blip-2 | ✅ | | | | |


### Semantic Segmentation

As with image-to-text models, you should be able to apply LoRA to any of the [segmentation models](https://huggingface.co/models?pipeline_tag=image-segmentation&sort=downloads).
It's worth noting that we haven't tested this with every architecture yet. Therefore, if you come across any issues, kindly create an issue report.

| Model | LoRA | Prefix Tuning | P-Tuning | Prompt Tuning | IA3 |
| --------- | ---- | ---- | ---- | ---- | ---- |
| SegFormer | ✅ | | | | |

<iframe
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http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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-->

# AutoPeftModels

The `AutoPeftModel` classes loads the appropriate PEFT model for the task type by automatically inferring it from the configuration file. They are designed to quickly and easily load a PEFT model in a single line of code without having to worry about which exact model class you need or manually loading a [`PeftConfig`].

## AutoPeftModel

[[autodoc]] auto.AutoPeftModel
- from_pretrained

## AutoPeftModelForCausalLM

[[autodoc]] auto.AutoPeftModelForCausalLM

## AutoPeftModelForSeq2SeqLM

[[autodoc]] auto.AutoPeftModelForSeq2SeqLM

## AutoPeftModelForSequenceClassification

[[autodoc]] auto.AutoPeftModelForSequenceClassification

## AutoPeftModelForTokenClassification

[[autodoc]] auto.AutoPeftModelForTokenClassification

## AutoPeftModelForQuestionAnswering

[[autodoc]] auto.AutoPeftModelForQuestionAnswering

## AutoPeftModelForFeatureExtraction

[[autodoc]] auto.AutoPeftModelForFeatureExtraction
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