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Normalize README bullet formatting (#2671)

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Fabrizio Milo
2025-10-13 12:13:30 -07:00
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commit 9bfc476d72
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@@ -11,28 +11,28 @@ brought to you by Apple machine learning research.
Some key features of MLX include: Some key features of MLX include:
- **Familiar APIs**: MLX has a Python API that closely follows NumPy. MLX - **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 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 [Swift](https://github.com/ml-explore/mlx-swift/) APIs, which closely mirror
the Python API. MLX has higher-level packages like `mlx.nn` and the Python API. MLX has higher-level packages like `mlx.nn` and
`mlx.optimizers` with APIs that closely follow PyTorch to simplify building `mlx.optimizers` with APIs that closely follow PyTorch to simplify building
more complex models. more complex models.
- **Composable function transformations**: MLX supports composable function - **Composable function transformations**: MLX supports composable function
transformations for automatic differentiation, automatic vectorization, transformations for automatic differentiation, automatic vectorization,
and computation graph optimization. and computation graph optimization.
- **Lazy computation**: Computations in MLX are lazy. Arrays are only - **Lazy computation**: Computations in MLX are lazy. Arrays are only
materialized when needed. materialized when needed.
- **Dynamic graph construction**: Computation graphs in MLX are constructed - **Dynamic graph construction**: Computation graphs in MLX are constructed
dynamically. Changing the shapes of function arguments does not trigger dynamically. Changing the shapes of function arguments does not trigger
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 the GPU). (currently the CPU and the GPU).
- **Unified memory**: A notable 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 transferring data. device types without transferring data.
@@ -110,7 +110,7 @@ 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 MLX useful in your research and wish to cite it, please use the following
BibTex entry: BibTex entry:
``` ```text
@software{mlx2023, @software{mlx2023,
author = {Awni Hannun and Jagrit Digani and Angelos Katharopoulos and Ronan Collobert}, author = {Awni Hannun and Jagrit Digani and Angelos Katharopoulos and Ronan Collobert},
title = {{MLX}: Efficient and flexible machine learning on Apple silicon}, title = {{MLX}: Efficient and flexible machine learning on Apple silicon},