Yuhua Ke

An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models

Multimodal Large Language Models (MLLMs) fine-tuned with multimodal instruction-following data have demonstrated formidable capabilities in multimodal tasks. However, fine-tuning all parameters of MLLMs has become challenging due to the rapid growth of the overall model’s parameters. To address this…