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Hugging Face hires oMLX creator Jun Kim to grow the MLX ecosystem

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Hugging Face hires oMLX creator Jun Kim to grow the MLX ecosystem

Hugging Face announced that Jun Kim, the creator and maintainer of oMLX, has joined the company to support the MLX community. MLX is Apple's framework for local AI, built especially for Apple Silicon; Hugging Face says it has backed MLX since Awni and Angelos gave it to the company as a Christmas present in 2023, and that Hugging Face's Hub is where people find MLX models and share their own. For oMLX itself, Hugging Face frames the move as a graduation from a side project to a fully maintained and funded one, which it expects to bring stability and faster development. oMLX stays licensed under Apache 2.0, and Jun continues to lead it as before. Hugging Face says its broader goal for MLX is to let the community run local AI in any form, backed by the tools and building blocks to do so. It expects oMLX to work as a testbed for new ideas while building on dependencies it already relies on, such as mlx-lm and mlx-vlm, and says it wants to upstream work to those projects where it makes sense. The company also points to existing collaboration with mlx-lm, mlx-vlm and LMStudio, and says it hopes to strengthen ties with Cheng, Prince and Yagil and their teams. One concrete focus Hugging Face names is speeding up the path from a model defined for the transformers library to a reference MLX implementation that different engines can then build on, each free to focus on its own distinguishing features. The post does not give a date for when Jun joined, nor any figures for the funding oMLX now receives, nor Jun's role or employer before this move.

Key facts

  • Jun Kim, creator and maintainer of oMLX, has joined Hugging Face to support the MLX community.
  • oMLX moves from a side project to a fully maintained, funded one, while staying Apache 2.0 licensed with Jun still leading it.
  • Hugging Face says it has supported MLX, Apple's local-AI framework for Apple Silicon, since receiving it as a 2023 Christmas gift from Awni and Angelos.
  • Hugging Face expects oMLX to serve as a testbed for new ideas built on dependencies like mlx-lm and mlx-vlm, and wants to upstream work to them.
  • A named focus is speeding the transition from a transformers model definition to a reference MLX implementation usable by different engines.

Why it matters

Hugging Face is putting paid, dedicated resources behind a project, oMLX, that until now ran as a side effort by a single maintainer. That shift, from unfunded side job to a maintained project backed by a company that already hosts the MLX model ecosystem on its Hub, is the kind of move that determines whether a community tool keeps up with demand or stalls on one person's spare time.

Who it affects

Developers building local AI on Apple Silicon using MLX, and specifically anyone relying on oMLX, gain a maintainer with company backing instead of a volunteer. The move also touches the maintainers of adjacent projects Hugging Face names as collaborators, mlx-lm, mlx-vlm and LMStudio, along with Cheng, Prince and Yagil, since Hugging Face says it wants to deepen work with their teams and upstream code where it fits.

How to use it

oMLX remains Apache 2.0 licensed, so its terms for use and modification do not change under the new arrangement. Hugging Face names one concrete workstream: a quicker path from a model defined in the transformers library to a reference implementation in MLX, meant to let different inference engines each build on that reference rather than duplicate the work.

How solid is it

This is Hugging Face's own announcement of a hire and a funding decision it made, not a third-party report, so the core fact, that Jun Kim has joined and oMLX is now funded, carries the company's direct authority. The post gives no date, no funding figures and no detail on Jun's prior role, so those specifics cannot be verified beyond what Hugging Face chose to state.

Risks and caveats

The announcement is framed entirely in intentions and hopes, strengthening relationships, upstreaming work where it makes sense, unblocking the community, without committed timelines, budget figures or deliverables attached to any of it. How much changes for oMLX in practice will depend on follow-through that this post does not itself demonstrate.

“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.”

— Hugging Face