Chinese researchers have recently made significant advancements in the field of instruction tuning for large language models (LLM). They have introduced ImageBind-LLM, a multimodal instruction tuning method that fine-tunes large language models through ImageBind. This approach leverages visual-language data to adjust multimodal instructions, supporting various instruction modes and offering enhanced scalability and generalization capabilities. The four key features of ImageBind-LLM include support for multiple instruction modes, efficient tuning methods, progressive knowledge infusion, and a visual cache model. This research provides new methods and insights for improving the multimodal instruction response capabilities of large language models, demonstrating practical application potential.