huggingface/peft

What it solves

Fine-tuning large pretrained models is typically prohibitively expensive in terms of compute and storage because it requires updating all model parameters. PEFT solves this by allowing users to adapt large models to specific downstream tasks by training only a tiny fraction of the model's parameters, significantly reducing the hardware requirements and storage space needed for checkpoints.

How it works

Instead of full fine-tuning, PEFT implements various parameter-efficient methods (such as LoRA, IA3, and soft prompting) that add or modify a small number of extra parameters. These "adapters" are trained while the base model remains largely frozen. The library integrates with the Hugging Face ecosystem, including Transformers for training and inference, Diffusers for managing adapters in image generation models, and Accelerate for distributed training across different hardware.

Who it’s for

It is designed for developers and researchers who want to fine-tune large language models (LLMs) or diffusion models on consumer-grade hardware or with limited computational resources.

Highlights

• Massive Resource Savings: Reduces GPU memory usage and storage requirements (e.g., reducing a 11GB model checkpoint to 19MB).
• Consumer Hardware Compatibility: Enables training of large models (like 12B parameter models) on single GPUs that would otherwise run out of memory.
• Quantization Support: Can be combined with quantization (like QLoRA) to further lower precision and memory needs.
• Hugging Face Integration: Works seamlessly with Transformers, Diffusers, Accelerate, and TRL for RLHF (Reinforcement Learning from Human Feedback).