Jun Kim, oMLX creator and maintainer, joins Hugging Face to support the MLX community
Quick Answer
Jun Kim, the creator of oMLX, has joined Hugging Face to enhance the MLX community, focusing on local AI optimized for Apple Silicon.
Quick Take
This transition aims to stabilize and accelerate oMLX development while maintaining its Apache 2.0 license, ultimately facilitating local AI implementation across various platforms.
Key Points
- Jun Kim transitions oMLX to a fully funded project at Hugging Face.
- MLX serves as a framework for local AI, particularly on Apple Silicon.
- oMLX will remain under Apache 2.0 license with Jun as the lead.
- Focus on streamlining transformers model integration with MLX.
- Collaboration with projects like mlx-lm and mlx- is expected to strengthen.
📖 Reader Mode
~2 min readWe are super excited to welcome Jun as our newest team member 🔥. We are completely invested in local AI, and MLX is a central piece of the ecosystem. We are delighted that Jun chose us to set up home and continue contributing to MLX.
MLX is Apple's framework for local AI, especially optimized for Apple Silicon. We are big MLX supporters since it was the Christmas present from Awni and Angelos in 2023, and proud that Hugging Face is the Hub where people find MLX models and contribute their own. Usage of open, local AI is accelerating, and we believe in a healthy ecosystem where people can find the tools that work for them.
What is the impact for oMLX?
Stability, and hopefully faster development! Graduating from a side job to a fully maintained and funded project will allow Jun to better guide the contributors and build for the long-term. oMLX stays Apache 2.0, and Jun keeps leading it as before.
What is the impact for MLX at large?
Our end goal is to unblock the community to run local AI in any shape or form, and provide the tools and building blocks to make that happen. We expect oMLX to serve as a testbed for new ideas, while leveraging the foundational work of the dependencies it already relies upon, such as mlx-lm or mlx-vlm. We believe that strong modeling and inference libraries help the community, so we'd love to upstream work to wherever it makes sense. We have been collaborating with many projects mlx-lm, mlx-vlm, LMStudio, and we hope we can strengthen the relationship with Cheng, Prince, Yagil, and their teams to better serve the community together.
Concretely, one focus area is the quick transition from a transformers model definition to a reference MLX implementation that can be consumed by different engines, so each one can focus on the unique features they provide. The transformers library has become the reference for ML model definitions, we want to streamline the process to make new transformers models run on MLX.
We are incredibly excited about the future.
Welcome, Jun! 🙌
— Originally published at huggingface.co
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