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huggingface/peft

PEFT is a state-of-the-art library for Parameter-Efficient Fine-Tuning, drastically reducing the computational and storage costs of adapting large pretrained models.

Core Features

Enables efficient adaptation of large pretrained models.

Significantly reduces computational and storage costs.

Achieves performance comparable to full fine-tuning.

Seamless integration with Hugging Face Transformers, Diffusers, and Accelerate.

Supports various PEFT methods like LoRA, Adapters, Soft prompts, and IA3.

Quick Start

pip install peft

Detailed Introduction

PEFT (Parameter-Efficient Fine-Tuning) addresses the challenge of fine-tuning massive pretrained models, which is often prohibitively expensive. By focusing on tuning only a small subset of model parameters, PEFT methods dramatically cut down on computational and storage requirements while maintaining high performance. This makes advanced model adaptation accessible on more modest hardware, democratizing the use of large language models and other deep learning architectures. The library integrates deeply with the Hugging Face ecosystem, providing a robust and user-friendly solution for efficient model customization.