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. It tries to follow the Python `transformers` API and abstractions whenever possible, but it also aims to provide an idiomatic Swift interface and does not assume prior familiarity with [`transformers`](https://github.com/huggingface/transformers) or [`tokenizers`](https://github.com/huggingface/tokenizers). ## Rationale & Overview Check out [our announcement post](https://huggingface.co/blog/swift-coreml-llm). ## Modules - `Tokenizers`: Utilities to convert text to tokens and back, with support for Chat Templates and Tools. Follows the abstractions in [`tokenizers`](https://github.com/huggingface/tokenizers). Usage example: ```swift import Tokenizers func testTokenizer() async throws { 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) } ``` - `Hub`: Utilities for interacting with the Hugging Face Hub! Download models, tokenizers and other config files. Usage example: ```swift import Hub func testHub() async throws { let repo = Hub.Repo(id: "mlx-community/Qwen2.5-0.5B-Instruct-2bit-mlx") let filesToDownload = ["config.json", "*.safetensors"] let modelDirectory: URL = try await Hub.snapshot( from: repo, matching: filesToDownload, progressHandler: { progress in print("Download progress: \(progress.fractionCompleted * 100)%") } ) print("Files downloaded to: \(modelDirectory.path)") } ``` - `Generation`: Algorithms for text generation. Handles tokenization internally. Currently supported ones are: greedy search, top-k sampling, and top-p sampling. - `Models`: Language model abstraction over a Core ML package. ## 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! ## Supported Models You can run inference on Core ML models with `swift-transformers`. Note that Core ML is not required to use the `Tokenizers` or `Hub` modules. This package has been tested with autoregressive language models such as: - GPT, GPT-Neox, GPT-J. - SantaCoder. - StarCoder. - Falcon. - Llama 2. Encoder-decoder models such as T5 and Flan are currently _not supported_. ## 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). - [`transformers-to-coreml`](https://huggingface.co/spaces/coreml-projects/transformers-to-coreml), a no-code Core ML conversion tool built on `exporters`. ## 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! Please ensure your code passes the build and test suite before submitting a pull request. You can run the tests with `swift test`. ## 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"