Parameter-Efficient Fine-Tuning (PEFT)
Parameter-Efficient Fine-Tuning (PEFT) is a machine learning approach that adapts a pretrained model to a specific task by updating only a small portion of its parameters instead of fine-tuning the entire model.
What is Parameter-Efficient Fine-Tuning (PEFT)?
PEFT methods keep most of the pretrained model's parameters frozen and introduce or modify a smaller set of trainable parameters. Techniques such as Low-Rank Adaptation (LoRA), adapters, and prompt tuning can significantly reduce the memory, computation, and storage required for model customization.
Why is Parameter-Efficient Fine-Tuning (PEFT) Important?
PEFT makes it more practical to customize large models with limited computational resources. It can reduce training costs while allowing organizations to maintain multiple task-specific adaptations of a shared pretrained model.
Common use cases
PEFT is commonly used for adapting large language models, domain-specific AI applications, instruction tuning, image generation, and other scenarios requiring efficient model customization.