huggingface/peft

Health v2 · maintenance only

Adoption not part of the score

21598 stars · 2452 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

PEFT is a Python library from Hugging Face providing state-of-the-art parameter-efficient fine-tuning methods (LoRA, adapters, soft prompts, IA3) for adapting large pretrained models. It integrates with Transformers, Diffusers, and Accelerate to make fine-tuning large models feasible on consumer hardware.

Use cases

• fine-tune an LLM with LoRA on a single GPU
• train a diffusion model adapter for custom image styles
• reduce GPU memory needed to fine-tune large models
• swap multiple task adapters on one base model
• fine-tune Llama with QLoRA and quantization
• train only a small fraction of model parameters for a downstream task

When to choose

• you want to fine-tune large language or diffusion models cheaply on limited hardware
• you use the Hugging Face Transformers/Diffusers ecosystem
• you need to train and store many task-specific adapters from one base model
• you want a reference implementation of LoRA and other PEFT methods

When to avoid

• you need to fully fine-tune all model parameters
• you work outside PyTorch/Hugging Face tooling
• you only need inference of pre-trained models without adaptation

Facets

library · maturity stable

llm-training machine-learning deep-learning sdk large-language-models machine-learning deep-learning artificial-intelligence python cross-platform lora parameter-efficient-fine-tuning adapters hugging-face pytorch transformers diffusion qlora prompt-tuning gpu

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