Parameter Efficient Fine Tuning

Parameter-efficient fine-tuning (PEFT) aims to adapt large pre-trained models to specific downstream tasks while minimizing the number of trainable parameters, thus reducing computational costs and memory requirements. Current research focuses on improving the efficiency and effectiveness of PEFT methods, exploring techniques like low-rank matrix and tensor decompositions (e.g., LoRA, its variants, and tensor-based adaptations), selective layer training, and novel parameter initialization strategies. These advancements are significant because they enable the deployment of large language models and other foundation models on resource-constrained devices and facilitate more efficient and sustainable model adaptation for diverse applications.