PEFT

PEFT (Parameter-Efficient Fine-Tuning) is a powerful library designed to adapt large pretrained models for various applications without the need to fine-tune all parameters. This approach significantly reduces computational and storage costs by only adjusting a small number of additional parameters, while still achieving performance levels comparable to fully fine-tuned models. This makes it feasible to train and store large language models on consumer hardware.

Integrated with the Transformers, Diffusers, and Accelerate libraries, PEFT offers a streamlined process for loading, training, and using large models for inference. Users benefit from faster examples with accelerated inference and can collaborate on models, datasets, and Spaces within the Hugging Face community. The documentation provides comprehensive guides, including tutorials, method guides, and API references, ensuring a smooth experience for developers and researchers alike.