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Deploying LLMs locally with Apple’s MLX framework
A technical deep dive into the new deep learning library MLX
What is this about?
In December 2023, Apple released their new MLX deep learning framework, an array framework for machine learning on Apple silicon, developed by their machine learning research team. This tutorial will explore the framework and demonstrate deploying the Mistral-7B model locally on a MacBook Pro (MBP). We’ll set up a local chat interface to interact with the deployed model and test its inference performance in terms of tokens generated per second. Additionally, we’ll delve into the MLX API to understand the available levers for altering the model’s behaviour and influencing the generated text.
As usual, the code is available in a public GitHub repository: https://github.com/marshmellow77/mlx-deep-dive
Why is this important?
Apple’s new machine learning framework, MLX, offers notable advantages over other deep learning frameworks with its unified memory architecture for machine learning on Apple silicon. Unlike traditional frameworks such as PyTorch and Jax, which require costly data copying between CPU and GPU, MLX maintains data in shared memory accessible to both. This design eliminates the overhead of data…
