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Normalize README bullet formatting (#2671)
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README.md
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README.md
@@ -11,31 +11,31 @@ brought to you by Apple machine learning research.
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Some key features of MLX include:
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Some key features of MLX include:
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- **Familiar APIs**: MLX has a Python API that closely follows NumPy. MLX
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- **Familiar APIs**: MLX has a Python API that closely follows NumPy. MLX
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also has fully featured C++, [C](https://github.com/ml-explore/mlx-c), and
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also has fully featured C++, [C](https://github.com/ml-explore/mlx-c), and
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[Swift](https://github.com/ml-explore/mlx-swift/) APIs, which closely mirror
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[Swift](https://github.com/ml-explore/mlx-swift/) APIs, which closely mirror
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the Python API. MLX has higher-level packages like `mlx.nn` and
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the Python API. MLX has higher-level packages like `mlx.nn` and
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`mlx.optimizers` with APIs that closely follow PyTorch to simplify building
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`mlx.optimizers` with APIs that closely follow PyTorch to simplify building
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more complex models.
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more complex models.
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- **Composable function transformations**: MLX supports composable function
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- **Composable function transformations**: MLX supports composable function
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transformations for automatic differentiation, automatic vectorization,
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transformations for automatic differentiation, automatic vectorization,
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and computation graph optimization.
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and computation graph optimization.
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- **Lazy computation**: Computations in MLX are lazy. Arrays are only
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- **Lazy computation**: Computations in MLX are lazy. Arrays are only
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materialized when needed.
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materialized when needed.
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- **Dynamic graph construction**: Computation graphs in MLX are constructed
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- **Dynamic graph construction**: Computation graphs in MLX are constructed
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dynamically. Changing the shapes of function arguments does not trigger
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dynamically. Changing the shapes of function arguments does not trigger
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slow compilations, and debugging is simple and intuitive.
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slow compilations, and debugging is simple and intuitive.
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- **Multi-device**: Operations can run on any of the supported devices
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- **Multi-device**: Operations can run on any of the supported devices
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(currently the CPU and the GPU).
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(currently the CPU and the GPU).
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- **Unified memory**: A notable difference from MLX and other frameworks
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- **Unified memory**: A notable difference from MLX and other frameworks
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is the *unified memory model*. Arrays in MLX live in shared memory.
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is the *unified memory model*. Arrays in MLX live in shared memory.
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Operations on MLX arrays can be performed on any of the supported
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Operations on MLX arrays can be performed on any of the supported
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device types without transferring data.
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device types without transferring data.
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MLX is designed by machine learning researchers for machine learning
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MLX is designed by machine learning researchers for machine learning
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researchers. The framework is intended to be user-friendly, but still efficient
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researchers. The framework is intended to be user-friendly, but still efficient
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@@ -110,7 +110,7 @@ Hannun, Jagrit Digani, Angelos Katharopoulos, and Ronan Collobert. If you find
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MLX useful in your research and wish to cite it, please use the following
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MLX useful in your research and wish to cite it, please use the following
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BibTex entry:
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BibTex entry:
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```
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```text
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@software{mlx2023,
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@software{mlx2023,
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author = {Awni Hannun and Jagrit Digani and Angelos Katharopoulos and Ronan Collobert},
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author = {Awni Hannun and Jagrit Digani and Angelos Katharopoulos and Ronan Collobert},
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title = {{MLX}: Efficient and flexible machine learning on Apple silicon},
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title = {{MLX}: Efficient and flexible machine learning on Apple silicon},
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