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Releases: huggingface/peft

v0.6.2 Patch Release: Refactor of adapter deletion API and fixes to `ModulesToSaveWrapper` when using Low-level API

14 Nov 05:55
@pacman100 pacman100
v0.6.2
32357c2
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v0.6.2 Patch Release: Refactor of adapter deletion API and fixes to `ModulesToSaveWrapper` when using Low-level API

This patch release refactors the adapter deletion API and fixes to ModulesToSaveWrapper when using Low-level API.

Refactor adapter deletion

Fix ModulesToSaveWrapper when using Low-level API

What's Changed

What's Changed

New Contributors

Full Changelog: v0.6.1...v0.6.2

Contributors

winglian, BenjaminBossan, and 3 other contributors
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0.6.1 Patch Release: compatibility of Adaptation Prompt with transformers 4.35.0

09 Nov 14:49
@younesbelkada younesbelkada
v0.6.1
1c8a5f2
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0.6.1 Patch Release: compatibility of Adaptation Prompt with transformers 4.35.0

This patch release fixes the compatbility issues with Adaptation Prompt that users faced with transformers 4.35.0. Moreover, it fixes an issue with token classification PEFT models when saving them using safetensors

Adaptation prompt fixes

  • FIX: Skip adaption prompt tests with new transformers versions by @BenjaminBossan in #1077
  • FIX: fix adaptation prompt CI and compatibility with latest transformers (4.35.0) by @younesbelkada in #1084

Safetensors fixes:

What's Changed

New Contributors

Full Changelog: v0.6.0...v0.6.1

Contributors

BenjaminBossan, pacman100, and 4 other contributors
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🧨 Diffusers now uses 🤗 PEFT, new tuning methods, better quantization support, higher flexibility and more

03 Nov 09:42
@BenjaminBossan BenjaminBossan
v0.6.0
02f0a4c
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🧨 Diffusers now uses 🤗 PEFT, new tuning methods, better quantization support, higher flexibility and more

Highlights

Integration with diffusers

🧨 Diffusers now leverage PEFT as a backend for LoRA inference for Stable Diffusion models (#873, #993, #961). Relevant PRs on 🧨 Diffusers are huggingface/diffusers#5058, huggingface/diffusers#5147, huggingface/diffusers#5151 and huggingface/diffusers#5359. This helps in unlocking a vast number of practically demanding use cases around adapter-based inference 🚀. Now you can do the following with easy-to-use APIs and it supports different checkpoint formats (Diffusers format, Kohya format ...):

  1. use multiple LoRAs
  2. switch between them instantaneously
  3. scale and combine them
  4. merge/unmerge
  5. enable/disable

For details, refer to the documentation at Inference with PEFT.

New tuning methods

Other notable additions

  • Allow merging of LoRA weights when using 4bit and 8bit quantization (bitsandbytes), thanks to @jiqing-feng (#851, #875)
  • IA³ now supports 4bit quantization thanks to @His-Wardship (#864)
  • We increased the speed of adapter layer initialization: This should be most notable when creating a PEFT LoRA model on top of a large base model (#887, #915, #994)
  • More fine-grained control when configuring LoRA: It is now possible to have different ranks and alpha values for different layers (#873)

Experimental features

  • For some adapters like LoRA, it is now possible to activate multiple adapters at the same time (#873)

Breaking changes

  • It is no longer allowed to create a LoRA adapter with rank 0 (r=0). This used to be possible, in which case the adapter was ignored.

What's Changed

As always, a bunch of small improvements, bug fixes and doc improvements were added. We thank all the external contributors, both new and recurring. Below is the list of all changes since the last release.

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Contributors

tmm1, kovalexal, and 23 other contributors
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GPTQ Quantization, Low-level API

22 Aug 11:22
@pacman100 pacman100
v0.5.0
4df9c5a
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GPTQ Quantization, Low-level API

GPTQ Integration

Now, you can finetune GPTQ quantized models using PEFT. Here are some examples of how to use PEFT with a GPTQ model: colab notebook and finetuning script.

Low-level API

Enables users and developers to use PEFT as a utility library, at least for injectable adapters (LoRA, IA3, AdaLoRA). It exposes an API to modify the model in place to inject the new layers into the model.

Support for XPU and NPU devices

Leverage the support for more devices for loading and fine-tuning PEFT adapters.

  • Support XPU adapter loading by @abhilash1910 in #737
  • Support Ascend NPU adapter loading by @statelesshz in #772

Mix-and-match LoRAs

Stable support and new ways of merging multiple LoRAs. There are currently 3 ways of merging loras supported: linear, svd and cat.

  • Added additional parameters to mixing multiple LoRAs through SVD, added ability to mix LoRAs through concatenation by @kovalexal in #817

What's Changed

New Contributors

Full Changelog: v0.4.0...v0.5.0

Contributors

BlackHC, kovalexal, and 14 other contributors
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QLoRA, IA3 PEFT method, support for QA and Feature Extraction tasks, AutoPeftModelForxxx for simplified UX , LoRA for custom models with new added utils

18 Jul 08:12
@pacman100 pacman100
v0.4.0
0769587
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QLoRA, IA3 PEFT method, support for QA and Feature Extraction tasks, AutoPeftModelForxxx for simplified UX , LoRA for custom models with new added utils

QLoRA Support:

QLoRA uses 4-bit quantization to compress a pretrained language model. The LM parameters are then frozen and a relatively small number of trainable parameters are added to the model in the form of Low-Rank Adapters. During finetuning, QLoRA backpropagates gradients through the frozen 4-bit quantized pretrained language model into the Low-Rank Adapters. The LoRA layers are the only parameters being updated during training. For more details read the blog Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA

New PEFT methods: IA3 from T-Few paper

To make fine-tuning more efficient, IA3 (Infused Adapter by Inhibiting and Amplifying Inner Activations) rescales inner activations with learned vectors. These learned vectors are injected into the attention and feedforward modules in a typical transformer-based architecture. These learned vectors are the only trainable parameters during fine-tuning, and thus the original weights remain frozen. Dealing with learned vectors (as opposed to learned low-rank updates to a weight matrix like LoRA) keeps the number of trainable parameters much smaller. For more details, read the paper Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Support for new tasks: QA and Feature Extraction

Addition of PeftModelForQuestionAnswering and PeftModelForFeatureExtraction classes to support QA and Feature Extraction tasks, respectively. This enables exciting new use-cases with PEFT, e.g., LoRA for semantic similarity tasks.

  • feat: Add PeftModelForQuestionAnswering by @sjrl in #473
  • add support for Feature Extraction using PEFT by @pacman100 in #647

AutoPeftModelForxxx for better and Simplified UX

Introduces a new paradigm, AutoPeftModelForxxx intended for users that want to rapidly load and run peft models.

from peft import AutoPeftModelForCausalLM

peft_model = AutoPeftModelForCausalLM.from_pretrained("ybelkada/opt-350m-lora")

LoRA for custom models

Not a transformer model, no problem, we have got you covered. PEFT now enables the usage of LoRA with custom models.

New LoRA utilities

Improvements to add_weighted_adapter method to support SVD for combining multiple LoRAs when creating new LoRA.
New utils such as unload and delete_adapter providing users much better control about how they deal with the adapters.

  • [Core] Enhancements and refactoring of LoRA method by @pacman100 in #695

PEFT and Stable Diffusion

PEFT is very extensible and easy to use for performing DreamBooth of Stable Diffusion. Community has added conversion scripts to be able to use PEFT models with Civitai/webui format and vice-versa.

  • LoRA for Conv2d layer, script to convert kohya_ss LoRA to PEFT by @kovalexal in #461
  • Added Civitai LoRAs conversion to PEFT, PEFT LoRAs conversion to webui by @kovalexal in #596
  • [Bugfix] Fixed LoRA conv2d merge by @kovalexal in #637
  • Fixed LoraConfig alpha modification on add_weighted_adapter by @kovalexal in #654

What's Changed

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Contributors

hotchpotch, pminervini, and 34 other contributors
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Docs, Testing Suite, Multi Adapter Support, New methods and examples

03 May 22:19
@pacman100 pacman100
v0.3.0
7120c9a
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Docs, Testing Suite, Multi Adapter Support, New methods and examples

Brand new Docs

With task guides, conceptual guides, integration guides, and code references all available at your fingertips, 🤗 PEFT's docs (found at https://huggingface.co/docs/peft) provide an insightful and easy-to-follow resource for anyone looking to how to use 🤗 PEFT. Whether you're a seasoned pro or just starting out, PEFT's documentation will help you to get the most out of it.

Comprehensive Testing Suite

Comprised of both unit and integration tests, it rigorously tests core features, examples, and various models on different setups, including single and multiple GPUs. This commitment to testing helps ensure that PEFT maintains the highest levels of correctness, usability, and performance, while continuously improving in all areas.

Multi Adapter Support

PEFT just got even more versatile with its new Multi Adapter Support! Now you can train and infer with multiple adapters, or even combine multiple LoRA adapters in a weighted combination. This is especially handy for RLHF training, where you can save memory by using a single base model with multiple adapters for actor, critic, reward, and reference. And the icing on the cake? Check out the LoRA Dreambooth inference example notebook to see this feature in action.

New PEFT methods: AdaLoRA and Adaption Prompt

PEFT just got even better, thanks to the contributions of the community! The AdaLoRA method is one of the exciting new additions. It takes the highly regarded LoRA method and improves it by allocating trainable parameters across the model to maximize performance within a given parameter budget. Another standout is the Adaption Prompt method, which enhances the already popular Prefix Tuning by introducing zero init attention.

New LoRA utilities

Good news for LoRA users! PEFT now allows you to merge LoRA parameters into the base model's parameters, giving you the freedom to remove the PEFT wrapper and apply downstream optimizations related to inference and deployment. Plus, you can use all the features that are compatible with the base model without any issues.

What's Changed

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Contributors

cyberfox, bigeagle, and 25 other contributors
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v0.2.0

10 Mar 06:10
@pacman100 pacman100
v0.2.0
a478ab9
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v0.2.0

Whisper large tuning using PEFT LoRA+INT-8 on T4 GPU in Colab notebooks

We tested PEFT on @OpenAI's Whisper Large model and got:
i) 5x larger batch sizes
ii) Less than 8GB GPU VRAM
iii) Best part? Almost no degredation to WER 🤯

Without PEFT:

  • OOM on a T4 GPU ❌
  • 6GB checkpoint ❌
  • 13.64 WER ✅

With PEFT:

  • Train on a T4 GPU ✅
  • 60MB checkpoint ✅
  • 14.01 WER ✅
  • adding whisper large peft+int8 training example by @pacman100 in #95

prepare_for_int8_training utility

This utility enables preprocessing the base model to be ready for INT8 training.

disable_adapter() context manager

Enables to disable adapter layers to get the outputs from the frozen base models.
An exciting application of this feature allows only a single model copy to be used for policy model and reference model generations in RLHF.

What's Changed

New Contributors

Significant community contributions

The following contributors have made significant changes to the library over the last release:

Full Changelog: v0.1.0...v0.2.0

Contributors

zphang, mrm8488, and 10 other contributors
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v0.1.0 Initial release

10 Feb 18:56
@pacman100 pacman100
v0.1.0
29357d4
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v0.1.0 Initial release

Initial release of 🤗 PEFT. Checkout the main README to learn more about it!

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