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@ -9,9 +9,9 @@ by Apple machine learning research.
Some key features of MLX include: Some key features of MLX include:
- **Familiar APIs**: MLX has a Python API which closely follows NumPy. - **Familiar APIs**: MLX has a Python API that closely follows NumPy.
MLX also has a fully featured C++ API which closely mirrors the Python API. MLX also has a fully featured C++ API, which closely mirrors the Python API.
MLX has higher level packages like `mlx.nn` and `mlx.optimizers` with APIs MLX has higher-level packages like `mlx.nn` and `mlx.optimizers` with APIs
that closely follow PyTorch to simplify building more complex models. that closely follow PyTorch to simplify building more complex models.
- **Composable function transformations**: MLX has composable function - **Composable function transformations**: MLX has composable function
@ -26,15 +26,15 @@ Some key features of MLX include:
slow compilations, and debugging is simple and intuitive. slow compilations, and debugging is simple and intuitive.
- **Multi-device**: Operations can run on any of the supported devices - **Multi-device**: Operations can run on any of the supported devices
(currently the CPU and GPU). (currently, the CPU and GPU).
- **Unified memory**: A noteable difference from MLX and other frameworks - **Unified memory**: A notable difference from MLX and other frameworks
is the *unified memory model*. Arrays in MLX live in shared memory. is the *unified memory model*. Arrays in MLX live in shared memory.
Operations on MLX arrays can be performed on any of the supported Operations on MLX arrays can be performed on any of the supported
device types without moving data. device types without moving data.
MLX is designed by machine learning researchers for machine learning MLX is designed by machine learning researchers for machine learning
researchers. The framework is intended to be user friendly, but still efficient 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 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 conceptually simple. We intend to make it easy for researchers to extend and
improve MLX with the goal of quickly exploring new ideas. improve MLX with the goal of quickly exploring new ideas.
@ -47,10 +47,10 @@ The design of MLX is inspired by frameworks like
## Examples ## Examples
The [MLX examples repo](https://github.com/ml-explore/mlx-examples) has a The [MLX examples repo](https://github.com/ml-explore/mlx-examples) has a
variety of examples including: variety of examples, including:
- [Transformer language model](https://github.com/ml-explore/mlx-examples/tree/main/transformer_lm) training. - [Transformer language model](https://github.com/ml-explore/mlx-examples/tree/main/transformer_lm) training.
- Large scale text generation with - Large-scale text generation with
[LLaMA](https://github.com/ml-explore/mlx-examples/tree/main/llama) and [LLaMA](https://github.com/ml-explore/mlx-examples/tree/main/llama) and
finetuning with [LoRA](https://github.com/ml-explore/mlx-examples/tree/main/lora). 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). - Generating images with [Stable Diffusion](https://github.com/ml-explore/mlx-examples/tree/main/stable_diffusion).
@ -64,7 +64,7 @@ in the documentation.
## Installation ## Installation
MLX is available on [PyPi](https://pypi.org/project/mlx/). To install the Python API run: MLX is available on [PyPi](https://pypi.org/project/mlx/). To install the Python API, run:
``` ```
pip install mlx pip install mlx