我们非常激动地欢迎Jun成为我们的最新团队成员🔥。我们完全致力于本地AI，而MLX是生态系统的核心组成部分。我们很高兴Jun选择加入我们要安顿下来并继续为MLX做出贡献。

MLX是Apple针对本地AI的框架，特别优化了Apple Silicon。自2023年Awni和Angelos将MLX作为圣诞礼物以来，我们就一直是MLX的坚定支持者，并且自豪于Hugging Face成为人们发现MLX模型并提交自己贡献的平台。开放、本地AI的使用正在加速发展，我们相信一个健康的生态系统，让人们可以找到适合他们的工具。

这对oMLX有什么影响？

稳定性，以及 hopefully 更快的开发速度！从副业升级为全面维护和资助的项目将使Jun能够更好地指导贡献者并长期建设。oMLX仍保持Apache 2.0许可证，Jun将继续像以前一样领导它。

这对整体MLX有什么影响？

我们的最终目标是解除社区运行任何形式的本地AI的限制，并提供实现这一目标的工具和基础模块。我们期望oMLX作为新想法的试验场，同时利用其依赖项（如mlx-lm或mlx-vlm）的基础工作。我们相信强大的建模和推理库有助于社区，因此我们希望将工作上游化到合适的地方。我们已经与许多项目进行了合作，包括mlx-lm、mlx-vlm、LMStudio，并希望加强与Cheng、Prince、Yagil及其团队的关系，以更好地共同服务社区。

具体来说，一个重点领域是从transformers模型定义快速过渡到可以被不同引擎使用的参考MLX实现，这样每个引擎都可以专注于它们提供的独特功能。transformers库已成为ML模型定义的参考标准，我们希望简化流程，使新的transformers模型能够在MLX上运行。

我们对未来充满期待。

欢迎Jun！🙌

We 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! 🙌