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peft

馃 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.

Last verification date: 2026-07-07 Verification method: source evidence, semantic profile, public page gate, and static build acceptance.

Publication status 路 2026-07-07

What is peft?

01

Quick decision

Use this section to decide whether the project is worth a deeper read.
Best forUsers who want source-backed project understanding before installing it.

Match the project to your task before installing it.

Capabilityprompt, recipe, host_instruction, eval, preflight

馃 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.

Repositoryhuggingface/peft

21k stars 路 2.3k forks

02

What it can do

Translate the upstream project into concrete capabilities the user can judge before installing.
1

Introduction, Installation & Quickstart

Related topics: Supported PEFT Methods and Tuner Architecture, Configuration, Training Workflow & Troubleshooting

Source: https://github.com/huggingface/peft / Human Manual
2

Supported PEFT Methods and Tuner Architecture

Related topics: Introduction, Installation & Quickstart, Configuration, Training Workflow & Troubleshooting

Source: https://github.com/huggingface/peft / Human Manual
3

Configuration, Training Workflow & Troubleshooting

Related topics: Supported PEFT Methods and Tuner Architecture, Integrations, Quantization, Merging & Examples

Source: https://github.com/huggingface/peft / Human Manual
4

Integrations, Quantization, Merging & Examples

Related topics: Introduction, Installation & Quickstart, Configuration, Training Workflow & Troubleshooting

Source: https://github.com/huggingface/peft / Human Manual
5

Doramagic Pitfall Log

Source-linked risks stay visible on the manual page so the preview does not read like a recommendation.

Source: Doramagic discovery, validation, and Project Pack records

Sources: https://github.com/huggingface/peft, Human Manual, Project Pack evidence, and downstream validation signals.

03

Community Discussion Evidence

Project-level external discussion stays visible on the detail page, not only inside the manual.
Stars21k stars
Forks2.3k forks
Contributors304 contributors
Licenseunknown

Community Discussion Evidence

12 source-linked items

Review these external discussions before using peft with real data or production workflows. They are review inputs, not standalone proof that the project is production-ready.

04

How to start

Only source-backed commands are shown here. Verify them in an isolated environment first.
1

Try the prompt first

Test the workflow without installing the upstream project.

preview
2

Read the Human Manual

Understand inputs, outputs, limits, and failure modes.

manual
3

Take context to your AI host

Use the compiled assets in your preferred AI environment.

context
4

Run sandbox verification

Confirm install commands and rollback before using a primary environment.

verify
pip install peft

Official start command 路 https://github.com/huggingface/peft#readme 路 verified: yes

05

Human Manual

The English page must expose the real manual, not a short placeholder.

8+ sections 路 Human Manual

peft Manual

PEFT (Parameter-Efficient Fine-Tuning) wraps a pretrained base model with adapter layers configured through a single PeftConfig instance. The workflow has three well-defined phases: config...

Open the full manual
  1. https://github.com/huggingface/peft Project Manual
  2. Table of Contents
  3. Introduction, Installation & Quickstart
  4. Related Pages
  5. What is PEFT
  6. Installation
  7. Public API Surface
  8. Quickstart: Fine-Tuning with LoRA
1

Introduction, Installation & Quickstart

Related topics: Supported PEFT Methods and Tuner Architecture, Configuration, Training Workflow & Troubleshooting

Source: https://github.com/huggingface/peft / Human Manual
2

Supported PEFT Methods and Tuner Architecture

Related topics: Introduction, Installation & Quickstart, Configuration, Training Workflow & Troubleshooting

Source: https://github.com/huggingface/peft / Human Manual
3

Configuration, Training Workflow & Troubleshooting

Related topics: Supported PEFT Methods and Tuner Architecture, Integrations, Quantization, Merging & Examples

Source: https://github.com/huggingface/peft / Human Manual
4

Integrations, Quantization, Merging & Examples

Related topics: Introduction, Installation & Quickstart, Configuration, Training Workflow & Troubleshooting

Source: https://github.com/huggingface/peft / Human Manual
5

Doramagic Pitfall Log

Source-linked risks stay visible on the manual page so the preview does not read like a recommendation.

Source: Doramagic discovery, validation, and Project Pack records

06

AI Context Pack and portable assets

After deciding to continue, take the project context into your own AI host.

Complete pack plus user-owned assets

These files are planning and verification assets for Claude Code, Codex, Gemini, Cursor, ChatGPT, and other AI hosts.

07

Preflight checks

Treat this page as a planning asset, not proof that your local environment is ready.

08

Pitfall Log and verification risks

Doramagic surfaces high-risk items before users treat a candidate capability as verified.
high

Configuration risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Configuration risk requires verification

Upgrade or migration may change expected behavior: 0.17.0: SHiRA, MiSS, LoRA for MoE, and more

medium

Configuration risk requires verification

Developers may misconfigure credentials, environment, or host setup: Proposal: FIM-guided adaptive LoRA rank allocation (FimConfig + initialize_lora_fim_ranks)

medium

Configuration risk requires verification

Developers may misconfigure credentials, environment, or host setup: [BUG] peft 0.19 target_modules (str) use `set`

medium

Configuration risk requires verification

Upgrade or migration may change expected behavior: v0.15.0

medium

Capability evidence risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Maintenance risk requires verification

Upgrade or migration may change expected behavior: 0.16.0: LoRA-FA, RandLoRA, C鲁A, and much more

medium

Maintenance risk requires verification

May increase setup, validation, or first-run risk for the user.