Blaizzy

    Blaizzy/mlx-vlm

    MLX-VLM is a package for inference and fine-tuning of Vision Language Models (VLMs) on your Mac using MLX.

    llm
    apple-silicon
    florence2
    idefics
    llava
    local-ai
    Python
    MIT
    5.7K stars
    872 forks
    5.7K GitHub watchers
    Updated Nov 3, 2026
    View on GitHub

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    Where it stands

    Rank, growth pace and release cadence relative to everything GStars tracks.

    5 releases in the last 90 days, the latest 5 days ago, with 198 contributors on GitHub.

    Peaked at #39 in the weekly ranking the week of Apr 5, its best in the past year.

    Use Cases & Benefits

    • Provides inference and fine-tuning of Vision Language Models and Omni Models on Mac using MLX for multi-modal inputs including images, audio, and video.
    • Enables local, efficient multi-modal model deployment with support for multi-image chat, video understanding, and audio processing on Apple Silicon Macs.
    • Use for building interactive chatbots that analyze and describe multiple images simultaneously in a single conversation.
    • Use for developing applications that generate captions, summaries, or analyses of video content using supported vision-language models.
    • Use for fine-tuning vision-language models locally with LoRA and QLoRA techniques to customize model behavior on specific datasets.

    Should you depend on it?

    License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.

    License

    MIT

    Permissive: commercial use, modification and redistribution are allowed with attribution.

    Community files

    • README: present
    • Contributing guide: present

    Watch-outs

    No watch-outs from GStars' checks.

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    From the README

    MLX-VLM is a package for inference and fine-tuning of Vision Language Models (VLMs) and Omni Models (VLMs with audio and video support) on your Mac using MLX.

    Excerpt from the Blaizzy/mlx-vlm README on GitHub (MIT license). © its authors.Read the full README on GitHub

    Daily snapshots

    Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots

    Forks

    872+174

    Open issues

    270+123

    Compared with peers in LLM

    Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth

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