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Mgm

Mgm is a LLaVA-based vision-language model framework that supports dense and mixture-of-experts language models from 2B to 34B for image understanding, reasoning and generation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Mgm — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Mgm when you need a documented framework for image input and vision-language inference, or want to explore image reasoning and generation with its listed model variants.

Who it is for

  • Developers building image-question-answering or image-generation experiments.
  • Researchers exploring or comparing vision-language model variants.
  • Users who want to try the project through its linked hosted demo.

Documented uses

  • Ask questions about an image using a vision-language model.
  • Build experiments that combine image input with multimodal reasoning.
  • Explore image generation with a model that also handles image understanding.
  • Compare dense and mixture-of-experts checkpoints across image tasks.
Before you choose

Requirements & limits

The repository links hosted demonstrations, including a Hugging Face Space described as a 13B-HD version. It documents local CLI and Gradio use. The code is Apache-2.0 licensed; the README lists a separate CC BY-NC 4.0 data license.

  • The framework lists dense and mixture-of-experts models from 2B to 34B, with standard and HD checkpoint variants.
  • Local CLI inference and a Gradio interface are documented; the latter uses a controller, web server and model worker.
  • The 2B version requires Transformers 4.38.0 or later.
  • Generation and OCR-assisted CLI use require diffusers and PaddleOCR respectively.
  • The repository identifies different licenses for code and data.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Run command-line image inference

Use the documented CLI to submit an image to a local MGM checkpoint for inference.

Before you start

  • MGM repository installed in a Python 3.10 Conda environment, following the README installation steps.
  • A local model checkpoint, such as work_dirs/MGM/MGM-13B-HD.
  • For generation, install diffusers; for the optional OCR path, install PaddleOCR.

Steps

  1. Clone the MGM repository and create and activate the Conda environment with Python 3.10.

  2. From the repository directory, install the package with pip install -e .

  3. Run the CLI with the chosen model path and image file. Add --gen for generation or --ocr for the OCR-assisted path, installing the corresponding package first.

Expected outcome

The CLI starts image inference using the specified model checkpoint; the README documents options for image understanding, generation and OCR-assisted use.

Guide limitations

  • The README says quantized inference may be less accurate than full-precision inference.
  • The OCR path requires PaddleOCR and generation requires diffusers.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
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Sources checked
3 November 2026
Original website title
JIA-Lab-research/MGM · Official homepage title · retained in its source language
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