Technology

PEFT

PEFT (Parameter-Efficient Fine-Tuning) is a set of techniques for rapidly adapting large pre-trained models (LLMs, vision models) to new tasks by updating only a small, critical subset of parameters.

PEFT is your solution for scaling model customization without the massive resource drain of full fine-tuning. It works by freezing the majority of the original model's weights and introducing a minimal number of new, trainable parameters: think LoRA or adapters. This approach drastically cuts down on computational cost and storage. For example, a full Stable Diffusion fine-tune is gigabytes; a PEFT adapter like LoRA can be a mere 8.8MB, yet deliver comparable performance. The Hugging Face PEFT library integrates seamlessly with Transformers and Diffusers, making it accessible to train and deploy state-of-the-art models even on consumer-grade hardware.