LLaVA-NeXT
University of Wisconsin-Madison
• Framework: UnknownLLaVA-NeXT is a next-generation multimodal large language model developed by the University of Wisconsin–Madison, building upon the LLaVA (Large Language and Vision Assistant) framework. It combines visual perception and language understanding to interpret and reason over text, images, and charts. Powered by open LLMs such as Mistral and Llama 3, LLaVA-NeXT supports visual question answering, document parsing, chart interpretation, and multimodal dialogue. The model introduces improved visual grounding, faster inference, and enhanced multimodal alignment, achieving state-of-the-art results across multiple vision-language benchmarks. It is widely used in research and enterprise applications for AI assistants that see, read, and reason.
LLaVA-NeXT AI Model
Model Performance Statistics
Views
Released
Last Checked
Version
- Visual reasoning
- Document understanding
- Image QA
- Parameter Count
- N/A
Dataset Used
COCO, Visual Genome, OCR-VQA
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