PEFT review

Q: How do I install PEFT?

Run pip install peft. The peft package needs Python 3.10.0 or newer.

PEFT is a Hugging Face Python library for adapting large pretrained models by fine-tuning only a small number of extra parameters.

Updated Nov 1, 2026. Review written Oct 22, 2026 from the project's own sources; usage data refreshed every Monday.

TL;DR

PEFT is a Python library from Hugging Face that implements parameter-efficient fine-tuning methods such as LoRA, IA3, and soft prompts. It is for developers who want to adapt large models with lower compute and storage costs. PEFT integrates with Transformers, Diffusers, and Accelerate, and the resulting adapter checkpoints are often only a few MBs.

21.9kGitHub stars
+28new stars, week of Oct 25
2.2Mdownloads a week, +6% vs 4-week avg
#34in Inference and training this week

PEFT at a glance

Category: Inference and training
Maintained by: Hugging Face (organization, @huggingface on GitHub)
License: Apache 2.0
Language: Python, Makefile, Dockerfile
Repository: github.com/huggingface/peft
Website: huggingface.co/docs/peft
PyPI package: peft 0.21.2
Latest release: v0.21.2 on GitHub, Oct 1, 2026
First published: Nov 25, 2022

What PEFT does

PEFT wraps a base model and a configuration such as LoraConfig with get_peft_model, so only a small fraction of parameters is trained.
PEFT implements many adaptation methods, including LoRA and its variants, AdaLoRA, IA3, LoHa, LoKr, OFT, prefix tuning, and prompt tuning.
PEFT saves trained adapters as small checkpoints, often a few MBs instead of GBs, which can be loaded onto the base model for inference.
PEFT can be combined with quantization, for example QLoRA, to fine-tune LLMs on consumer GPUs.
PEFT applies to diffusion models, for example training Stable Diffusion with LoRA through the Diffusers integration.
PEFT supports RLHF and DPO training of LLMs through the TRL library.

How it works

PEFT runs as a Python library on top of PyTorch. You load a model with Transformers, define a PEFT configuration, and wrap the model with get_peft_model before training. For inference, PeftModel.from_pretrained loads a saved adapter onto the base model. Transformers models can also call add_adapter, load_adapter, and set_adapter directly. Accelerate handles distributed training and inference.

How to install and start using PEFT

With pip

pip install peft

Requirements

Python 3.10.0 or later.
PyTorch, which the library is built on.
Tensor Parallel support in v0.21.1 requires Transformers 5.17.0 or later.

First steps

Install PEFT with pip, then load a base model with Transformers AutoModelForCausalLM.from_pretrained.
Create a LoraConfig, setting values such as r, lora_alpha, and task_type, and wrap the model with get_peft_model.
Call print_trainable_parameters to check how many parameters will be trained, then train, for example with the Transformers Trainer.
Save the adapter with save_pretrained, and load it later for inference with PeftModel.from_pretrained.

Who PEFT is for

Developers fine-tuning LLMs on limited GPU memory or consumer hardware.
Teams adapting one base model to many tasks or datasets who want small per-task checkpoints.
Users of Transformers, Diffusers, Accelerate, or TRL who want adapter-based training.
Researchers who need reference implementations of many parameter-efficient fine-tuning methods.

Limitations to know

The Transformers integration does not include all PEFT features, such as merging the adapter into the base model.
Some models are not supported out of the box, so you must configure the model manually to enable PEFT.
PEFT is tied to the PyTorch and Hugging Face ecosystem, and some features depend on specific Transformers versions.

Is PEFT growing? Usage data

In the week of Oct 25–31, 2026, PEFT ranked #303 on the Devtoolwatch Board and #34 in Inference and training, with a momentum score of 31 out of 100. Its main package had 2.2M downloads that week, +6% against its own 4-week average.

New GitHub stars per week for the last 12 weeks, from GitHub's stargazer history (today's stargazers by the week they starred).

Show the weekly numbers

New GitHub stars per week for PEFT
Week of	New stars
Aug 9, 2026	45
Aug 16, 2026	34
Aug 23, 2026	31
Aug 30, 2026	35
Sep 6, 2026	32
Sep 13, 2026	39
Sep 20, 2026	29
Sep 27, 2026	29
Oct 4, 2026	30
Oct 11, 2026	28
Oct 18, 2026	54
Oct 25, 2026	28

On Hacker News

Qwen 3.8 27B, 1438 points and 793 comments (Aug 14, 2026)
Kimi-K3 on HuggingFace, 1382 points and 544 comments (Jul 27, 2026)
DeepSeek-v3.2: Pushing the frontier of open large language models [pdf], 982 points and 465 comments (Dec 1, 2025)
DeepSeek-v4.1-Flash-1017, 959 points and 721 comments (Oct 17, 2026)
GLM-5.3 is now open-weight, 806 points and 280 comments (Aug 28, 2026)

The Board ranks tools by four signals each week: new stars, new stars for the repo's size, added downloads and download growth beyond the market. How the ranking works.

PEFT alternatives

Other LLM inference and training tools on the Board in the week of Oct 25–31, 2026, by momentum:

Inference and training ranked by momentum, the week of Oct 25–31, 2026
Rank	Tool	Stars	Downloads/wk
#1	dsh-our-free-modeldsh-our-free-model is a dsh plugin that provides DeepSeek V4.1 Flash, Kimi K3 and other models without login, sign-up or API key.	54.3k	–
#2	magpiemagpie is a local gateway and app that lets coding agents like Claude Code, Codex and Gemini CLI use any provider's model.	26.9k	–
#3	StrataStrata is a local inference engine and app that runs large open models like Qwen3.8-Flash-Next on consumer hardware with…	37.1k	–
#4	edge0edge0 is an open-source streaming inference framework that runs large sparse mixture-of-experts models on consumer hardware by streaming…	8.9k	–
#5	OmniRouteOmniRoute is an open-source AI gateway that routes requests across many model providers through one OpenAI-compatible endpoint, with…	83.2k	106k
#6	CLIProxyAPICLIProxyAPI is a Go proxy server that exposes CLI coding accounts like Codex, Claude Code and Gemini CLI through OpenAI, Gemini and…	57.1k	–

All LLM inference and training tools, ranked

Recent PEFT releases

v0.21.2, Oct 1, 2026
v0.21.1, Sep 29, 2026
v0.21.0, Sep 15, 2026
v0.20.0, Jul 28, 2026
v0.19.1, Apr 16, 2026

PEFT FAQ

Is PEFT free and open source?

Yes. PEFT's source code is on GitHub (huggingface/peft) under the Apache 2.0 license, so you can use it for free. Its website also links to a pricing page (https://huggingface.co/pricing) for any paid plans.

How do I install PEFT?

Run pip install peft. The peft package needs Python 3.10.0 or newer.

Is PEFT still maintained?

Yes. The huggingface/peft repository was last updated on Oct 21, 2026, and the latest release, v0.21.2 on GitHub, came out on Oct 1, 2026.

How popular is PEFT?

PEFT has 21.9k GitHub stars as of Nov 1, 2026. It gained 28 new stars in the week of Oct 25–31, 2026, against a 12-week average of 35 a week. Its main package had 2.2M downloads that week, +6% against its own 4-week average. That made it #34 among LLM inference and training tools on the Devtoolwatch Board.

What are the best alternatives to PEFT?

Other LLM inference and training tools on the Devtoolwatch Board in the week of Oct 25–31, 2026, by momentum: dsh-our-free-model (#1), magpie (#2), Strata (#3), edge0 (#4), OmniRoute (#5). The alternatives table above has their numbers.

Is LoRA the same as PEFT?

No. PEFT is a library that implements many parameter-efficient fine-tuning methods, and LoRA is one of them. PEFT also includes LoRA variants such as DoRA, along with IA3, prefix tuning, prompt tuning, OFT, and others.

Can PEFT be used with QLoRA?

Yes. PEFT can be combined with quantization to cut memory use further. The README links a guide to fine-tuning Llama-2-7b with QLoRA and TRL on a 16GB GPU.

How does PEFT compare with full fine-tuning?

PEFT trains only a small number of extra parameters instead of all of them. This lowers compute and storage costs. The sources say recent PEFT methods reach performance comparable to fully fine-tuned models, with checkpoints of a few MBs instead of GBs.

Can PEFT be used for supervised fine-tuning and RLHF?

Yes. PEFT works with the Transformers Trainer for supervised training and with the TRL library for RLHF and DPO, including supervised finetuning, reward modeling, and RL finetuning examples.

Does PEFT work with diffusion models?

Yes. Through the Diffusers integration, PEFT can train Stable Diffusion with LoRA. This lowers memory needs and produces small final checkpoints.

Sources

PEFT README badge

Maintainers can show the tool's weekly Board rank in their README:

[![Devtoolwatch](https://index.devtoolwatch.com/badge/peft.svg)](https://index.devtoolwatch.com/tools/peft/)

How this page is made: Devtoolwatch writes each review from the project's README, releases, package metadata and website, with an AI model, then checks every claim against the source it cites and drops what the sources don't support. The numbers come from Devtoolwatch's daily GitHub snapshots, GitHub's stargazer history, and the npm and PyPI download APIs. Spotted an error? Email [email protected].