base on Examples using MLX Swift # MLX Swift Examples Example [MLX Swift](https://github.com/ml-explore/mlx-swift) programs. The language model examples use models implemented in [MLX Swift LM](https://github.com/ml-explore/mlx-swift-lm). - [MNISTTrainer](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). - [LLMEval](Applications/LLMEval/README.md): An example that runs on both iOS and macOS that downloads an LLM and tokenizer from Hugging Face and generates text from a given prompt. - [VLMEval](Applications/VLMEval/README.md): An example that runs on iOS, macOS and visionOS to download a VLM and tokenizer from Hugging Face and analyzes the given image and describe it in text. - [MLXChatExample](Applications/MLXChatExample/README.md): An example chat app that runs on both iOS and macOS that supports LLMs and VLMs. - [LoRATrainingExample](Applications/LoRATrainingExample/README.md): An example that runs on macOS that downloads an LLM and fine-tunes it using LoRA (Low-Rank Adaptation) with training data. - [LinearModelTraining](Tools/LinearModelTraining/README.md): An example that trains a simple linear model. - [StableDiffusionExample](Applications/StableDiffusionExample/README.md): An example that runs on both iOS and macOS that downloads a stable diffusion model from Hugging Face and and generates an image from a given prompt. - [llm-tool](Tools/llm-tool/README.md): A command line tool for generating text using a variety of LLMs available on the Hugging Face hub. - [ExampleLLM](Tools/ExampleLLM/README.md): A command line tool using the simplified API to interact with LLMs. - [image-tool](Tools/image-tool/README.md): A command line tool for generating images using a stable diffusion model from Hugging Face. - [mnist-tool](Tools/mnist-tool/README.md): A command line tool for training a a LeNet on MNIST. > [!IMPORTANT] > `MLXLMCommon`, `MLXLLM`, `MLXVLM` and `MLXEmbedders` have moved to a new repository > containing _only_ reusable libraries: [mlx-swift-lm](https://github.com/ml-explore/mlx-swift-lm). Previous URLs and tags will continue to work, but going forward all updates to these libraries will be done in the other repository. Previous tags _are_ supported in the new repository. > [!TIP] > Contributors that wish to edit both `mlx-swift-examples` and `mlx-swift-lm` can > use [this technique in Xcode](https://developer.apple.com/documentation/xcode/editing-a-package-dependency-as-a-local-package). # Reusable Libraries LLM and VLM implementations are available in [MLX Swift LM](https://github.com/ml-explore/mlx-swift-lm): - [MLXLLMCommon](https://swiftpackageindex.com/ml-explore/mlx-swift-lm/main/documentation/mlxlmcommon) -- common API for LLM and VLM - [MLXLLM](https://swiftpackageindex.com/ml-explore/mlx-swift-lm/main/documentation/mlxllm) -- large language model example implementations - [MLXVLM](https://swiftpackageindex.com/ml-explore/mlx-swift-lm/main/documentation/mlxvlm) -- vision language model example implementations - [MLXEmbedders](https://swiftpackageindex.com/ml-explore/mlx-swift-lm/main/documentation/mlxembedders) -- popular Encoders / Embedding models example implementations MLX Swift Examples also contains a few reusable libraries that can be imported with this code in your `Package.swift` or by referencing the URL in Xcode: ```swift .package(url: "https://github.com/ml-explore/mlx-swift-examples/", branch: "main"), ``` Then add one or more libraries to the target as a dependency: ```swift .target( name: "YourTargetName", dependencies: [ .product(name: "StableDiffusion", package: "mlx-libraries") ]), ``` - [StableDiffusion](https://swiftpackageindex.com/ml-explore/mlx-swift-examples/main/documentation/stablediffusion) -- SDXL Turbo and Stable Diffusion model example implementations - [MLXMNIST](https://swiftpackageindex.com/ml-explore/mlx-swift-examples/main/documentation/mlxmnist) -- MNIST implementation for all your digit recognition needs ## Running The application and command line tool examples can be run from Xcode or from the command line: ``` ./mlx-run llm-tool --prompt "swift programming language" ``` Note: `mlx-run` is a shell script that uses `xcode` command line tools to locate the built binaries. It is equivalent to running from Xcode itself. See also: - [MLX troubleshooting](https://swiftpackageindex.com/ml-explore/mlx-swift/main/documentation/mlx/troubleshooting) ", Assign "at most 3 tags" to the expected json: {"id":"9646","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"