# swift-transformers **Repository Path**: ExodusCat/swift-transformers ## Basic Information - **Project Name**: swift-transformers - **Description**: No description available - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2026-02-14 - **Last Updated**: 2026-02-14 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

Swift + Transformers

[![Unit Tests](https://github.com/huggingface/swift-transformers/actions/workflows/ci.yml/badge.svg)](https://github.com/huggingface/swift-transformers/actions/workflows/unit-tests.yml) [![](https://img.shields.io/endpoint?url=https%3A%2F%2Fswiftpackageindex.com%2Fapi%2Fpackages%2Fhuggingface%2Fswift-transformers%2Fbadge%3Ftype%3Dswift-versions)](https://swiftpackageindex.com/huggingface/swift-transformers) [![](https://img.shields.io/endpoint?url=https%3A%2F%2Fswiftpackageindex.com%2Fapi%2Fpackages%2Fhuggingface%2Fswift-transformers%2Fbadge%3Ftype%3Dplatforms)](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://huggingface.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 🙏 ### Offline CoreML tokenizers When you bundle a compiled CoreML model and tokenizer files with your app, you can skip any network requests by injecting the tokenizer when constructing `LanguageModel`: ```swift let compiledURL: URL = ... // path to .mlmodelc let tokenizerFolder: URL = ... // folder containing tokenizer_config.json and tokenizer.json // Construct the tokenizer from local files (inside an async context) let tokenizer = try await AutoTokenizer.from(modelFolder: tokenizerFolder) let model = try LanguageModel.loadCompiled( url: compiledURL, tokenizer: tokenizer ) ``` Make sure the tokenizer assets come from the same Hugging Face repo as the original checkpoint or are compatible with the model you use. For the Mistral example in `Examples/Mistral7B/`, you can fetch the tokenizer like this: ```bash huggingface-cli download \ mistralai/Mistral-7B-Instruct-v0.3 \ tokenizer.json tokenizer_config.json \ --local-dir Examples/Mistral7B/local-tokenizer ``` If the repo is gated, authenticate with `huggingface-cli login` first. Both initializers reuse the tokenizer you pass in and never reach out to the Hugging Face Hub. ## 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).