PKU-YuanGroup/LLaVA-CoT

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2132 stars · 81 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

LLaVA-CoT is an 11B vision language model with training, inference, and dataset-generation code for spontaneous step-by-step multimodal reasoning, released with pretrained weights on Hugging Face. It outperforms several larger commercial VLMs on six multimodal benchmarks and was accepted to ICCV 2025.

Use cases

• run a vision language model that reasons step by step
• fine-tune a multimodal model for chain-of-thought reasoning
• evaluate a VLM on multimodal benchmarks
• generate a reasoning dataset for vision language model training
• compare open VLM against GPT-4o-mini and Gemini
• deploy a gradio demo for image question answering

When to choose

• you need an open 11B VLM with systematic reasoning and available weights
• you want reproducible training and dataset generation code for multimodal CoT
• you need a research baseline for multimodal reasoning benchmarks

When to avoid

• you need a small model that runs without a GPU
• you want a production-ready commercial-grade VLM API
• you need non-English or non-reasoning-focused multimodal tasks

Facets

library · maturity active

machine-learning deep-learning llm-inference nlp computer-vision rag artificial-intelligence large-language-models computer-vision deep-learning python vision-language-model chain-of-thought multimodal-reasoning llava model-weights iccv-2025 natural-language-processing gpu linux

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