PEFT

PEFT is a library for parameter-efficient fine-tuning that adapts large pretrained models by tuning only a small subset of model parameters.

PEFT enables the adaptation of large pretrained models by fine-tuning a small fraction of total parameters instead of the entire model. This approach significantly decreases computational and storage costs while maintaining performance levels comparable to full fine-tuning. Users can apply these methods to various downstream applications and datasets to avoid overfitting. The library integrates with Transformers, Diffusers, Accelerate, and TRL for training and inference. It supports diverse hardware configurations, including GPUs and TPUs, and allows for the use of quantization techniques alongside adapters.

PEFT at a glance

Best for: Software engineer, Researcher
Runs on: open source
Pricing: Free
Released: November 2022
Category: AI coding tool
Website: github.com/huggingface/peft

Key features

Fine-tune small subsets of model parameters
Integration with Transformers and Diffusers libraries
Compatibility with Accelerate for distributed training
Support for quantization techniques
Tools for adapter management and switching
Reduction in checkpoint storage size
Applicability to large language models and diffusion models

Use it for

Adapting large pretrained models to specific downstream tasks
Fine-tuning models on consumer hardware with limited resources
Reducing storage requirements for model checkpoints
Training diffusion models for image generation
Implementing reinforcement learning with human feedback on LLMs

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Hugging Face Transformers — It provides a unified API for over 1M+ pretrained model checkpoints on the Hub.
Strata — Runs large AI models locally on consumer gaming PCs without server reliance.

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