From 0cfbfc9904588ac7f3d7f9df172c885e6d5ee062 Mon Sep 17 00:00:00 2001 From: "Yiyang(Steven) Yu" <2322740290@qq.com> Date: Sun, 10 Dec 2023 17:47:37 -0500 Subject: [PATCH] Update README.md (#121) --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 4f0f282a4..1fe603552 100644 --- a/README.md +++ b/README.md @@ -16,24 +16,24 @@ Some key features of MLX include: 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 has 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 materialized when needed. - - **Dynamic graph construction**: Computation graphs in MLX are built + - **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 GPU). + (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 moving data. + 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 @@ -66,7 +66,7 @@ in the documentation. ## 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