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Apple’s machine learning research team MLX introduced a machine learning framework called. In this way, developers can create efficient models and deep learning model libraries for Apple Silicon.
Apple’s machine learning research team MLX introduced a machine learning framework called. In this way, developers can create efficient models and deep learning model libraries for Apple Silicon. Both frame and MLX data based deep learning models are available on GitHub and PyPI.
This is evident from Apple’s GitHub page From PyTorch, Jax and ArrayFire Inspired by MLX, it will have shared memory. This means that any job running on MLX can run on any supported hardware without having to move any data. Currently, CPUs and GPUs have MLX support. According to reports, MLX is simple enough for developers to use, but has enough power to develop models like Meta’s Llama and Stable Diffusion generative AIs. Frameworks and model libraries can be used for many AI applications on the market.
Yet MLX Data focuses on deep learning in general. Apple, does not focus on the field of productive artificial intelligence like its major competitors Microsoft and Google. In fact, the company avoids using the word artificial intelligence as much as possible.
Apple has done that before working on basic models It was explained. The underlying models contain a lot of unlabeled data. It is therefore possible to use the same dataset for different purposes.
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