JIA-Lab-research/MGM

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3327 stars · 276 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Official PyTorch implementation of Mini-Gemini, a multimodal vision-language model framework built on LLaVA that supports dense and MoE LLMs from 2B to 34B. It combines dual vision encoders for high-resolution image understanding, reasoning, and image generation in a single model.

Use cases

• run a vision language model for image understanding and reasoning
• generate images from text and image prompts
• fine-tune a multimodal LLM on custom data
• evaluate vision-language models on benchmarks
• try a high-resolution image chat demo
• compare dense and MoE multimodal models

When to choose

• you need a research-grade VLM with both image understanding and generation
• you want to build on or extend LLaVA-style architectures
• you need high-resolution visual reasoning with dual encoders
• you want pretrained checkpoints from 2B to 34B including LLaMA3-based variants

When to avoid

• you need a production-ready, commercially supported multimodal API
• you only need text-only LLM inference
• you lack a GPU or cannot install flash-attention
• you need lightweight on-device inference

Facets

library · maturity maintenance

llm-inference llm-training machine-learning image-processing chatbot large-language-models computer-vision deep-learning artificial-intelligence python vision-language-model multimodal image-generation image-understanding llava research-code moe gpu linux docker

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