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https://github.com/mwrites/mlx

Apple MLX implementations for common AI tasks
https://github.com/mwrites/mlx

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Apple MLX implementations for common AI tasks

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README 

        
# 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 research 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/mlx/build/html/usage/quick_start.html)

in the documentation.



## Installation



MLX is available on [PyPI](https://pypi.org/project/mlx/). To install the Python API, run:



**With `pip`**:



```

pip install mlx

```



**With `conda`**:



```

conda install -c conda-forge mlx

```



Checkout the

[documentation](https://ml-explore.github.io/mlx/build/html/install.html#)

for more information on building the C++ and Python APIs from source.



## Contributing 



Check out the [contribution guidelines](https://github.com/ml-explore/mlx/tree/main/CONTRIBUTING.md) for more information

on contributing to MLX. See the

[docs](https://ml-explore.github.io/mlx/build/html/install.html) for more

information on building from source, and running tests.



We are grateful for all of [our

contributors](https://github.com/ml-explore/mlx/tree/main/ACKNOWLEDGMENTS.md#Individual-Contributors). If you contribute

to MLX and wish to be acknowledged, please add your name to the list in your

pull request.



## Citing MLX



The MLX software suite was initially developed with equal contribution by Awni

Hannun, Jagrit Digani, Angelos Katharopoulos, and Ronan Collobert. If you find

MLX useful in your research and wish to cite it, please use the following

BibTex entry:



```

@software{mlx2023,

  author = {Awni Hannun and Jagrit Digani and Angelos Katharopoulos and Ronan Collobert},

  title = {{MLX}: Efficient and flexible machine learning on Apple silicon},

  url = {https://github.com/ml-explore},

  version = {0.0},

  year = {2023},

}

```