From 49cda449b1711203516a2c5efc7a9818a21262e9 Mon Sep 17 00:00:00 2001 From: Awni Hannun Date: Tue, 5 Dec 2023 14:10:59 -0800 Subject: [PATCH] apple mlr (#7) --- README.md | 3 ++- docs/src/index.rst | 6 +++--- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index acbaf0cca..9b82b653e 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,8 @@ [**Documentation**](https://ml-explore.github.io/mlx/build/html/index.html) | [**Examples**](#examples) -MLX is an array framework for machine learning on Apple silicon. +MLX is an array framework for machine learning on Apple silicon, brought to you +by Apple machine learning research. Some key features of MLX include: diff --git a/docs/src/index.rst b/docs/src/index.rst index 445970370..d9e89b48b 100644 --- a/docs/src/index.rst +++ b/docs/src/index.rst @@ -1,8 +1,8 @@ MLX === -MLX is a NumPy-like array framework designed for efficient and flexible -machine learning on Apple silicon. +MLX is a NumPy-like array framework designed for efficient and flexible machine +learning on Apple silicon, brought to you by Apple machine learning research. The Python API closely follows NumPy with a few exceptions. MLX also has a fully featured C++ API which closely follows the Python API. @@ -17,7 +17,7 @@ The main differences between MLX and NumPy are: - **Multi-device**: Operations can run on any of the supported devices (CPU, GPU, ...) -The design of MLX is strongly inspired by frameworks like `PyTorch +The design of MLX is inspired by frameworks like `PyTorch `_, `Jax `_, and `ArrayFire `_. A noteable difference from these frameworks and MLX is the *unified memory model*. Arrays in MLX live in shared