Source URL: https://simonwillison.net/2024/Sep/29/mlx-vlm/#atom-everything
Source: Simon Willison’s Weblog
Title: mlx-vlm
Feedly Summary: mlx-vlm
The MLX ecosystem of libraries for running machine learning models on Apple Silicon continues to expand. Prince Canuma is actively developing this library for running vision models such as Qwen-2 VL and Pixtral and LLaVA using Python running on a Mac.
I used uv to run it against this image with this shell one-liner:
uv run –with mlx-vlm \
python -m mlx_vlm.generate \
–model Qwen/Qwen2-VL-2B-Instruct \
–max-tokens 1000 \
–temp 0.0 \
–image https://static.simonwillison.net/static/2024/django-roadmap.png \
–prompt “Describe image in detail, include all text"
This first downloaded 4.1GB to my ~/.cache/huggingface/hub/models–Qwen–Qwen2-VL-2B-Instruct folder and then output this result, which starts:
The image is a horizontal timeline chart that represents the release dates of various software versions. The timeline is divided into years from 2023 to 2029, with each year represented by a vertical line. The chart includes a legend at the bottom, which distinguishes between different types of software versions. […]
Via Chris Zubak-Skees
Tags: vision-llms, apple, python, generative-ai, uv, ai, llms, mlx
AI Summary and Description: Yes
Summary: The text discusses the MLX ecosystem, highlighting new libraries developed for machine learning models on Apple Silicon, particularly focusing on vision models. It presents a practical shell command to run the Qwen-2 VL model and provides insight into the usage of generative AI for image analysis.
Detailed Description: The provided text is relevant to several categories, particularly in the realms of AI, Generative AI, and LLM Security. The focus is on the MLX ecosystem, which enables the operation of various machine learning models on Apple’s architecture, emphasizing user engagement through practical coding examples.
– **Expansion of MLX Ecosystem**: The MLX ecosystem is growing, providing libraries that facilitate running various machine learning models specifically optimized for Apple Silicon.
– **Model Application**: One noteworthy library in the MLX ecosystem is being actively developed by Prince Canuma, aimed at executing vision models like Qwen-2 VL, Pixtral, and LLaVA using Python.
– **Practical Example Provided**: A shell command illustrates how to run the model on an image, showcasing hands-on implementation:
– **Command Explanation**:
– `uv run –with mlx-vlm \`: Instantiates the model execution with MLX VLM.
– `python -m mlx_vlm.generate \`: Indicates the generative aspect.
– `–model Qwen/Qwen2-VL-2B-Instruct \`: Specifies the model for processing.
– `–max-tokens 1000 \`: Limits the output verbosity.
– `–temp 0.0 \`: Indicates batch processing parameters.
– `–image https://…png \`: The URL of the image to analyze.
– `–prompt “Describe image in detail, include all text”`: Defines the task for the AI.
– **Model Output**: The result from the execution is a descriptive analysis of the image, demonstrating practical generative AI utilization in flattening real-world tasks.
– **Relevance to Professionals**: For security, compliance, and infrastructure professionals, engaging with such ML libraries can help integrate AI capabilities safely within existing ecosystems, taking encryption and privacy standards into account when handling sensitive data through image processing tasks.
This context surrounds the ongoing advancements in AI framework development and the convergence of hardware optimization (Apple Silicon) with sophisticated machine learning techniques, all while emphasizing the necessity of robust integrations in modern technological environments.