base on Swift Package to implement a transformers-like API in Swift <p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="media/swift-t-banner.png"> <source media="(prefers-color-scheme: light)" srcset="media/swift-t-banner.png"> <img alt="Swift + Transformers" src="media/swift-t-banner.png" style="max-width: 100%;"> </picture> <br/> <br/> </p> [](https://github.com/huggingface/swift-transformers/actions/workflows/unit-tests.yml) [](https://swiftpackageindex.com/huggingface/swift-transformers) [](https://swiftpackageindex.com/huggingface/swift-transformers) `swift-transformers` is a collection of utilities to help adopt language models in Swift apps. Those familiar with the [`transformers`](https://github.com/huggingface/transformers) Python library will find a familiar yet idiomatic Swift API. ## Rationale & Overview Check out [our v1.0 release post](https://huggingfce.co/blog/swift-transformers) and our [original announcement](https://huggingface.co/blog/swift-coreml-llm) for more context on why we built this library. ## Examples The most commonly used modules from `swift-transformers` are `Tokenizers` and `Hub`, which allow fast tokenization and model downloads from the Hugging Face Hub. ### Tokenizing text + chat templating Tokenizing text should feel very familiar to those who have used the Python `transformers` library: ```swift let tokenizer = try await AutoTokenizer.from(pretrained: "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B") let messages = [["role": "user", "content": "Describe the Swift programming language."]] let encoded = try tokenizer.applyChatTemplate(messages: messages) let decoded = tokenizer.decode(tokens: encoded) ``` ### Tool calling `swift-transformers` natively supports formatting inputs for tool calling, allowing for complex interactions with language models: ```swift let tokenizer = try await AutoTokenizer.from(pretrained: "mlx-community/Qwen2.5-7B-Instruct-4bit") let weatherTool = [ "type": "function", "function": [ "name": "get_current_weather", "description": "Get the current weather in a given location", "parameters": [ "type": "object", "properties": ["location": ["type": "string", "description": "City and state"]], "required": ["location"] ] ] ] let tokens = try tokenizer.applyChatTemplate( messages: [["role": "user", "content": "What's the weather in Paris?"]], tools: [weatherTool] ) ``` ### Hub downloads Downloading models to a user device _fast_ and _reliably_ is a core requirement of on-device ML. `swift-transformers` provides a simple API to download models from the Hugging Face Hub, with progress reporting, flaky connection handling, and more: ```swift let repo = Hub.Repo(id: "mlx-community/Qwen2.5-0.5B-Instruct-2bit-mlx") let modelDirectory: URL = try await Hub.snapshot( from: repo, matching: ["config.json", "*.safetensors"], progressHandler: { progress in print("Download progress: \(progress.fractionCompleted * 100)%") } ) print("Files downloaded to: \(modelDirectory.path)") ``` ### CoreML Integration The `Models` and `Generation` modules provide handy utilities when working with language models in CoreML. Check out our example converting and running Mistral 7B using CoreML [here](https://github.com/huggingface/swift-transformers/tree/main/Examples). The [modernization of Core ML](https://github.com/huggingface/swift-transformers/pull/257) and corresponding examples were primarily contributed by @joshnewnham, @1duo, @alejandro-isaza, @aseemw. Thank you 🙏 ## Usage via SwiftPM To use `swift-transformers` with SwiftPM, you can add this to your `Package.swift`: ```swift dependencies: [ .package(url: "https://github.com/huggingface/swift-transformers", from: "0.1.17") ] ``` And then, add the Transformers library as a dependency to your target: ```swift targets: [ .target( name: "YourTargetName", dependencies: [ .product(name: "Transformers", package: "swift-transformers") ] ) ] ``` ## Projects that use swift-transformers ❤️ - [WhisperKit](https://github.com/argmaxinc/WhisperKit): A Swift Package for state-of-the-art speech-to-text systems from [Argmax](https://github.com/argmaxinc) - [MLX Swift Examples](https://github.com/ml-explore/mlx-swift-examples): A Swift Package for integrating MLX models in Swift apps. Using `swift-transformers` in your project? Let us know and we'll add you to the list! ## Other Tools - [`swift-chat`](https://github.com/huggingface/swift-chat), a simple app demonstrating how to use this package. - [`exporters`](https://github.com/huggingface/exporters), a Core ML conversion package for transformers models, based on Apple's [`coremltools`](https://github.com/apple/coremltools). ## Contributing Swift Transformers is a community project and we welcome contributions. Please check out [Issues](https://github.com/huggingface/swift-transformers/issues) tagged with `good first issue` if you are looking for a place to start! Before submitting a pull request, please ensure your code: - Passes the test suite (`swift test`) - Passes linting checks (`swift format lint --recursive .`) To format your code, run `swift format -i --recursive .`. ## License [Apache 2](LICENSE). ", Assign "at most 3 tags" to the expected json: {"id":"11871","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"