ml-explore/mlx-swift

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# MLX Swift [**Installation**](#installation) | [**Documentation**](https://swiftpackageindex.com/ml-explore/mlx-swift/main/documentation/mlx) | [**Examples**](#examples) MLX Swift is a Swift API for [MLX](https://ml-explore.github.io/mlx/build/html/index.html). MLX is an array framework for machine learning on Apple silicon. MLX Swift expands MLX to the Swift language, making research and experimentation easier on Apple silicon. [View Current Release Planning](https://github.com/ml-explore/mlx-swift/issues?q=is%3Aissue+is%3Aopen+label%3Arelease-planning) and read about [Contributing](#contributing). ## Language Models LLM and VLM implementations are available in [mlx-swift-lm](https://github.com/ml-explore/mlx-swift-lm). ## Examples MLX Swift has [many examples](https://swiftpackageindex.com/ml-explore/mlx-swift/main/documentation/mlx/examples), including: - [MNISTTrainer](https://github.com/ml-explore/mlx-swift-examples/blob/main/Applications/MNISTTrainer/README.md): An example that runs on both iOS and macOS that downloads MNIST training data and trains a [LeNet](https://en.wikipedia.org/wiki/LeNet). - [MLXChatExample](https://github.com/ml-explore/mlx-swift-examples/blob/main/Applications/MLXChatExample/README.md): An example chat app that runs on both iOS and macOS that supports LLMs and VLMs. - [LLMEval](https://github.com/ml-explore/mlx-swift-examples/blob/main/Applications/LLMEval/README.md): A simple example that runs on both iOS and macOS that downloads an LLM and tokenizer from Hugging Face and generates text from a given prompt. - [StableDiffusionExample](https://github.com/ml-explore/mlx-swift-examples/blob/main/Applications/StableDiffusionExample/README.md): An example that runs on both iOS and macOS that downloads a stable diffusion model from Hugging Face and generates an image from a given prompt. - [llm-tool](https://github.com/ml-explore/mlx-swift-examples/blob/main/Tools/llm-tool/README.md): A command line tool for generating text using a variety of LLMs available on the Hugging Face hub. The [MLX Swift Examples repo](https://github.com/ml-explore/mlx-swift-examples) contains the complete code and documentation for these examples, including [guidelines on porting models](https://swiftpackageindex.com/ml-explore/mlx-swift-examples/main/documentation/mlxlmcommon/porting) from MLX Python. ## Installation The ``MLX`` Swift package can be built and run from Xcode or SwiftPM. A CMake installation is also provided, featuring a native Linux build option. More details are in the [documentation](https://swiftpackageindex.com/ml-explore/mlx-swift/main/documentation/mlx/install). ### Xcode (1) In Xcode you can add `https://github.com/ml-explore/mlx-swift.git` as a package dependency and link `MLX`, `MLXNN`, `MLXOptimizers` and `MLXRandom` as needed. ### XCode (2) Note that the SwiftPM and XCode (1) methods build `MLX` as a Library, not as a framework. It is possible to construct a situation where YourApp -> MLX, YourApp -> YourFramework and YourFramework -> MLX. This would give two copies of MLX in the resulting process and it may not work as expected. If this cannot be avoided, either by making YourFramework a Library or having YourApp _not_ link MLX, you can use the `xcode/MLX.xcodeproj` to build MLX as a _Framework_. This will require `mlx-swift` to be checked out adjacent or inside your project, possibly using git submodules, and dragging the `mlx-swift/xcode/MLX.xcodeproj` into your project. Once that is done your application can build and link MLX and related as Frameworks. ### SwiftPM To use ``MLX`` with SwiftPM you can add this to your `Package.swift`: ```swift dependencies: [ .package(url: "https://github.com/ml-explore/mlx-swift", from: "0.10.0") ] ``` and add the libraries as dependencies: ```swift dependencies: [.product(name: "MLX", package: "mlx-swift"), .product(name: "MLXNN", package: "mlx-swift"), .product(name: "MLXOptimizers", package: "mlx-swift")] ``` > [!Note] > SwiftPM (command line) cannot build the Metal shaders so the ultimate build has to be done > via Xcode. ### xcodebuild **Update the submodules** The directories `Source/Cmlx/mlx` and `Source/Cmlx/mlx-c` are sourced as submodules. Before you attempt to build the project locally, pull down the updates for those submodules: ```shell git submodule update --init --recursive ``` **Use Xcode to build the binaries and metal shaders** Although `SwiftPM` (command line) cannot build the Metal shaders, `xcodebuild` can and it can be used to do command line builds: ```shell # build and run tests xcodebuild test -scheme mlx-swift-Package -destination 'platform=OS X' # build Tutorial xcodebuild build -scheme Tutorial -destination 'platform=OS X' ``` ### CMake #### (1) macOS **Install Dependencies** Building with CMake requires both CMake and Ninja to be installed. You can do this with [Homebrew](https://brew.sh/): ```shell brew install cmake brew install ninja ``` **Build + Run Examples** - The examples use the Metal GPU backend by default on macOS. - Note that the CUDA GPU backend is exclusive to Linux. ```shell mkdir -p build cd build cmake .. -G Ninja ninja ./example1 ./tutorial ``` #### (2) Linux **Install Dependencies** - To build the example binaries, install all dependencies listed in the CI [scripts](.github/scripts/). - Note: The CUDA GPU backend requires the CUDA toolkit and additional dependencies. - For Swift installation on Linux, visit [swift.org](https://www.swift.org/install/linux/). **Build + Run Examples (CPU backend)** On Linux, the examples use the CPU backend by default. ```shell mkdir -p build pushd build cmake -DMLX_BUILD_METAL=OFF .. -G Ninja ninja ./example1 ./tutorial popd ``` **Build + Run Examples (GPU CUDA backend)** ```shell mkdir -p build pushd build cmake -DMLX_BUILD_METAL=OFF -DMLX_BUILD_CUDA=ON -DMLX_C_BUILD_EXAMPLES=OFF .. -G Ninja ninja ./example1 --device gpu ./tutorial --device gpu popd ``` ## Contributing Check out the [contribution guidelines](CONTRIBUTING.md) for more information on contributing to MLX. See the [docs](https://swiftpackageindex.com/ml-explore/mlx-swift/main/documentation/mlx/install) for more information on building from source, and running tests. We are grateful for all of [our contributors](ACKNOWLEDGMENTS.md#Individual-Contributors). If you contribute to MLX Swift and wish to be acknowledged, please add your name to the list in your pull request. Smaller, focused PRs are easier and quicker to review. Sometimes larger PRs can't be helped. If you want to discuss, feel free to open an issue or ask questions in a PR. MLX Swift was initially developed by David Koski and Ronan Collobert, and is now maintained by David Koski. MLX Swift is built on top of [MLX](https://github.com/ml-explore/mlx), which was initially developed with equal contribution by Awni Hannun, Jagrit Digani, Angelos Katharopoulos, and Ronan Collobert. ## Versions See [Releases](https://github.com/ml-explore/mlx-swift/releases). Generally the MLX Swift version number corresponds to the same version number in [MLX](https://github.com/ml-explore/mlx). Release notes indicate specifics. All capabilities in MLX (Python) should be available in MLX Swift. If you encounter any that are missing please file an issue or feel free to submit a PR.

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MLX Swift - High-performance ML framework for Apple Silicon with lazy evaluation, automatic differentiation, and unified memory

Quellsprache: Englisch

2026-10-20

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