mlx

# MLX

[**Quickstart**](#quickstart) | [**Installation**](#installation) |
[**Documentation**](https://ml-explore.github.io/mlx/build/html/index.html) |
[**Examples**](#examples)

[![CircleCI](https://circleci.com/gh/ml-explore/mlx.svg?style=svg)](https://circleci.com/gh/ml-explore/mlx)

MLX is an array framework for machine learning on Apple silicon,
brought to you by Apple machine learning research.

Some key features of MLX include:

- **Familiar APIs**: MLX has a Python API that closely follows NumPy. MLX
   also has fully featured C++, [C](https://github.com/ml-explore/mlx-c), and
   [Swift](https://github.com/ml-explore/mlx-swift/) APIs, which closely mirror
   the Python API. MLX has higher-level packages like `mlx.nn` and
   `mlx.optimizers` with APIs that closely follow PyTorch to simplify building
   more complex models.

- **Composable function transformations**: MLX supports composable function
  transformations for automatic differentiation, automatic vectorization,
  and computation graph optimization.

- **Lazy computation**: Computations in MLX are lazy. Arrays are only
  materialized when needed.

- **Dynamic graph construction**: Computation graphs in MLX are constructed
  dynamically. Changing the shapes of function arguments does not trigger
  slow compilations, and debugging is simple and intuitive.

- **Multi-device**: Operations can run on any of the supported devices
  (currently the CPU and the GPU).

- **Unified memory**: A notable difference from MLX and other frameworks
  is the *unified memory model*. Arrays in MLX live in shared memory.
  Operations on MLX arrays can be performed on any of the supported
  device types without transferring data.

MLX is designed by machine learning researchers for machine learning
researchers. The framework is intended to be user-friendly, but still efficient
to train and deploy models. The design of the framework itself is also
conceptually simple. We intend to make it easy for researchers to extend and
improve MLX with the goal of quickly exploring new ideas.

The design of MLX is inspired by frameworks like
[NumPy](https://numpy.org/doc/stable/index.html),
[PyTorch](https://pytorch.org/), [Jax](https://github.com/google/jax), and
[ArrayFire](https://arrayfire.org/).

## Examples

The [MLX examples repo](https://github.com/ml-explore/mlx-examples) has a
variety of examples, including:

- [Transformer language model](https://github.com/ml-explore/mlx-examples/tree/main/transformer_lm) training.
- Large-scale text generation with
  [LLaMA](https://github.com/ml-explore/mlx-examples/tree/main/llms/llama) and
  finetuning with [LoRA](https://github.com/ml-explore/mlx-examples/tree/main/lora).
- Generating images with [Stable Diffusion](https://github.com/ml-explore/mlx-examples/tree/main/stable_diffusion).
- Speech recognition with [OpenAI's Whisper](https://github.com/ml-explore/mlx-examples/tree/main/whisper).

## Quickstart

See the [quick start
guide](https://ml-explore.github.io/ml