Efficient Fine Tuning

Efficient fine-tuning aims to adapt large pre-trained models to specific downstream tasks while minimizing computational cost and memory usage. Current research focuses on parameter-efficient fine-tuning methods, employing techniques like low-rank adaptation (LoRA), adapters, and various forms of sparse training to update only a small subset of model parameters. These advancements are crucial for deploying large models on resource-constrained devices and accelerating the training process for diverse applications, impacting fields ranging from natural language processing and computer vision to medical image analysis and weather forecasting. The ultimate goal is to achieve performance comparable to full fine-tuning with significantly reduced resource requirements.