Apple Launches MLX Machine-Learning Framework For Apple Silicon

Apple has released MLX, a free and open-source machine learning framework for Apple Silicon. Computerworld reports: The idea is that it streamlines training and deployment of ML models for researchers who use Apple hardware. MLX is a NumPy-like array framework designed for efficient and flexible machine learning on Apple's processors. This isn't a consumer-facing tool; it equips developers with what appears to be a powerful environment within which to build ML models. The company also seems to have worked to embrace the languages developers want to use, rather than force a language on them -- and it apparently invented powerful LLM tools in the process.

MLX design is inspired by existing frameworks such as PyTorch, Jax, and ArrayFire. However, MLX adds support for a unified memory model, which means arrays live in shared memory and operations can be performed on any of the supported device types without performing data copies. The team explains: 'The Python API closely follows NumPy with a few exceptions. MLX also has a fully featured C++ API which closely follows the Python API.'

Apple has provided a collection of examples of what MLX can do. These appear to confirm the company now has a highly-efficient language model, powerful tools for image generation using Stable Diffusion, and highly accurate speech recognition. This tallies with claims earlier this year, and some speculation concerning infinite virtual world creation for future Vision Pro experiences. Ultimately, Apple seems to want to democratize machine learning. 'MLX is designed by machine learning researchers for machine learning researchers,' the team explains.

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