Instruction tuning large language models (LLMs) using machine-generatedinstruction-following data has improved zero-shot capabilities on new tasks,but the idea is less explored in the multimodal field. In this paper, wepresent the first attempt to use language-only GPT-4 to generate multimodallanguage-image instruction-following data. By instruction tuning on suchgenerated data, we introduce LLaVA: Large Language and Vision Assistant, anend-to-end trained large multimodal model that connects a vision encoder andLLM for general-purpose visual and language understanding.Our early experimentsshow that LLaVA demonstrates impressive multimodel chat abilities, sometimesexhibiting the behaviors of multimodal GPT-4 on unseen images/instructions, andyields a 85.1% relative score compared with GPT-4 on a synthetic multimodalinstruction-following dataset. When fine-tuned on Science QA, the synergy ofLLaVA and GPT-4 achieves a new state-of-the-art accuracy of 92.53%. We makeGPT-4 generated visual instruction tuning data, our model and code basepublicly available.