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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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README.md
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@@ -11,28 +11,28 @@ 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
- **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
- **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
- **Lazy computation**: Computations in MLX are lazy. Arrays are only
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
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).
- **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.
Operations on MLX arrays can be performed on any of the supported
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
BibTex entry:
```
```text
@software{mlx2023,
author = {Awni Hannun and Jagrit Digani and Angelos Katharopoulos and Ronan Collobert},
title = {{MLX}: Efficient and flexible machine learning on Apple silicon},