Tuning Method

Tuning methods optimize the performance of machine learning models, particularly large language and vision-language models, for specific tasks and hardware. Current research emphasizes efficient tuning techniques, such as parameter-efficient fine-tuning (PEFT) and methods that decouple the tuning process from the base model architecture, addressing computational cost and generalization challenges. These advancements are crucial for deploying powerful models in resource-constrained environments and specialized domains like medicine and finance, improving both the efficiency and applicability of AI systems.