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huggingface/peft

A state-of-the-art library for Parameter-Efficient Fine-Tuning (PEFT) of large pretrained models, drastically reducing computational and storage costs.

Core Features

Implements various Parameter-Efficient Fine-Tuning (PEFT) methods.

Significantly reduces computational and storage requirements for model adaptation.

Achieves performance comparable to full fine-tuning with fewer parameters.

Seamlessly integrates with Hugging Face Transformers, Diffusers, and Accelerate.

Enables fine-tuning large models on consumer-grade hardware.

Quick Start

pip install peft

Detailed Introduction

Fine-tuning large pretrained models is often prohibitively expensive. PEFT (Parameter-Efficient Fine-Tuning) offers a solution by enabling efficient adaptation of these models to diverse downstream tasks, requiring only a small fraction of parameters to be tuned. This approach dramatically cuts down on computational and storage costs, making large model fine-tuning accessible even on consumer hardware. The PEFT library provides state-of-the-art techniques that deliver performance comparable to full fine-tuning, while integrating smoothly with the Hugging Face ecosystem for simplified training and inference workflows.