Mlx-Vlm

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MLX-VLM is an open-source coding & development tool that enables inference and fine-tuning of Vision Language Models (VLMs) and Omni Models on Apple Silicon Macs using MLX. It supports multi-modal generation including image, audio, and text.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is mlx-vlm?

MLX-VLM is a comprehensive open-source package designed for developers and researchers working with Vision Language Models (VLMs) and Omni Models on Apple Silicon Macs. Leveraging the MLX framework, it facilitates both inference and fine-tuning of these advanced AI models directly on macOS. The tool offers a flexible command-line interface (CLI) for quick generation tasks, a Gradio-based chat UI for interactive use, and a FastAPI server for robust API integration and continuous batching. Key features include support for multi-modal inputs (image, audio, text), speculative decoding for faster generation, and KV cache quantization to optimize memory usage. It also provides detailed documentation for various supported models, making it a powerful solution for local VLM development.

Best used for

Ideal for developers and data scientists who need to perform inference and fine-tuning of Vision Language Models and Omni Models on Apple Silicon Macs. Especially valuable for those looking to leverage multi-modal inputs (image, audio, text) and optimize model performance with features like speculative decoding and KV cache quantization.

Common actions

run AI models

fine-tune AI models

generate multi-modal content

optimize model performance

deploy AI server

"AI Agents"face swappingautomated workflowdeepfakecollaborationworkflowsopen-sourcegithub copilotlow-code/no-code

Capabilities

Key features

VLM inference
VLM fine-tuning
Multi-modal generation
Speculative decoding
KV cache quantization
Gradio chat UI
FastAPI server

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What types of models does MLX-VLM support?

MLX-VLM supports Vision Language Models (VLMs) and Omni Models, which include capabilities for handling audio and video in addition to images and text. It is designed to work with various pre-trained models, offering detailed documentation for specific architectures like DeepSeek-OCR, Qwen, Gemma, and more.

Can MLX-VLM be used for both inference and fine-tuning?

Yes, MLX-VLM is built to facilitate both inference and fine-tuning of supported VLM and Omni Models. This allows users to not only run pre-trained models but also adapt them to specific tasks or datasets directly on their Mac devices using the MLX framework.

How does MLX-VLM optimize performance and memory usage?

MLX-VLM incorporates several optimization techniques, including speculative decoding (DFlash) to speed up generation by 2-3x, and KV cache quantization (uniform or TurboQuant) to significantly reduce memory consumption during continuous batching. These features are crucial for efficient operation on local hardware.

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