LLAVA
Open-source multimodal vision-language model
ABOUT
Understanding and reasoning about visual content — images, diagrams, screenshots — is a fundamental capability that most language models lack. Traditional approaches required separate vision and language pipelines, making it difficult to have natural conversations about visual information. LLaVA solves this by combining a vision encoder (CLIP) with a large language model (Vicuna/Llama) through a simple projection layer, enabling the model to understand images and engage in multi-turn conversations about visual content. The model achieves strong performance on visual question answering, image captioning, and multimodal reasoning tasks while being fully open-source and reproducible. For developers building multimodal applications, LLaVA provides a foundation that can be fine-tuned for specific visual understanding tasks.