Parameter-Efficient Fine-Tuning (PEFT)

Parameter-Efficient Fine-Tuning (PEFT) refers to techniques that allow for the adaptation of pre-trained models with minimal adjustments to their parameters. This approach is particularly useful in scenarios where computational resources are limited or when quick adaptations to new tasks are required. PEFT methods typically freeze most of the model's parameters and only update a small subset, making the fine-tuning process faster and more efficient. Common use cases include adapting large language models for specific applications like sentiment analysis or domain-specific question answering without the need for extensive retraining.