Pre Trained Model Weight

Pre-trained model weights are foundational to many modern machine learning systems, serving as starting points for adapting large models to specific downstream tasks. Current research focuses on improving the efficiency of fine-tuning these weights, exploring techniques like low-rank adaptation (LoRA) and novel methods that leverage singular vectors or nonlinear transformations to achieve significant performance gains with minimal parameter updates. These advancements are crucial for reducing computational costs and memory requirements, enabling wider deployment of powerful models across diverse applications, from natural language processing to medical image analysis.