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peft

huggingface/peft

State-of-the-art Parameter-Efficient Fine-Tuning

GitHub Website

GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026

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22k stars2.5k forksLast push Sep 18, 2026 Python Apache-2.0

Decision brief

PEFT focuses on advanced techniques for efficiently tuning parameters in large models with Python.

Good fit when

When you need to fine-tune large language models but are constrained by compute resources or want to avoid overfitting.
If your project requires state-of-the-art parameter-efficient methods that integrate well with popular frameworks like Transformers and PyTorch.

Avoid when

If you require a tool that supports training from scratch, as PEFT is specifically designed for fine-tuning purposes only.
When working on models where the full fine-tuning of all parameters is feasible or preferred due to ample compute resources and no concern over overfitting.

Observed Jul 11, 2026 · Source: enrich:decision_facts

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Maintenance and security

Full trust report

Maintenance: Very active (1d since push); As of Sep 20, 2026
Provenance: Not a fork · Organization account; As of Sep 20, 2026
Security (OSV): No lockfile; As of Jul 11, 2026

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Backing

Company context for Hugging Face. Display-only - separate from trust and ranking.

Company: Hugging Face·GitHub org profile·Jul 11, 2026
Employees: 160·Wikidata (P1128 employees)·Jul 11, 2026
Funding: $235,000,000 (2023-08)·GraphCanon curated seed (public press)·Jul 11, 2026
Commercial model: OSS + managed cloud·GraphCanon curated seed·Jul 11, 2026

Install

pip install peft

PyPI

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

🤗 PEFT provides state-of-the-art methods for efficient parameter fine-tuning in large models.

Capability facts

Languages: python
Source: github.language+pyproject.toml · Sep 20, 2026

Graph entities

Hugging Facecompany

README

High performance on consumer hardware Consider the memory requirements for training the following models on the ought/raft/twitter complaints dataset with an A100 80GB GPU with more than 64GB of CPU RAM. Model Full Finetuning PEFT LoRA PyTorch PEFT LoRA DeepSpeed with CPU Offload...

For agents

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