EvolvingLMMs-Lab/LLaVA-OneVision-2

Health v2 · maintenance only

Adoption not part of the score

1195 stars · 80 forks observed · 2026-09-03

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

A fully open framework for training multimodal large language models, releasing models, datasets, and training recipes for the LLaVA-OneVision family. It includes an 8B-class video MLLM with codec-aligned dense video input and a codec-aligned vision encoder.

Use cases

• train a vision-language model from scratch
• build a video understanding MLLM
• reproduce open multimodal model training
• fine-tune LLaVA-OneVision on my own data
• get an open alternative to Qwen-VL
• train a vision encoder for image and video
• run reinforcement learning post-training for multimodal reasoning

When to choose

• you need fully reproducible open weights, data, and recipes for multimodal training
• you want strong video comprehension on long footage with an 8B model
• you want an efficient codec-aligned vision encoder that uses fewer visual tokens

When to avoid

• you only need to run inference with a hosted API
• you lack GPU resources for large-scale multimodal training
• you need a small lightweight model for edge devices

Facets

framework · maturity active

machine-learning deep-learning llm-training image-processing video-processing nlp large-language-models machine-learning computer-vision deep-learning artificial-intelligence python vision-language-model multimodal llava qwen3 video-understanding open-models model-training gpu linux docker

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markdown · JSON · MCP: product_card(name="EvolvingLMMs-Lab/LLaVA-OneVision-2")