base on Swift Package to implement a transformers-like API in Swift # `swift-transformers` [](https://github.com/huggingface/swift-transformers/actions/workflows/unit-tests.yml) [](https://swiftpackageindex.com/huggingface/swift-transformers) [](https://swiftpackageindex.com/huggingface/swift-transformers) This 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 and Overview Please, check [our post](https://huggingface.co/blog/swift-coreml-llm). ## Modules - `Tokenizers`. Utilities to convert text to tokens and back. Follows the abstractions in [`tokenizers`](https://github.com/huggingface/tokenizers) and [`transformers.js`](https://github.com/xenova/transformers.js). Usage example: ```swift import Tokenizers func testTokenizer() async throws { let tokenizer = try await AutoTokenizer.from(pretrained: "pcuenq/Llama-2-7b-chat-coreml") let inputIds = tokenizer("Today she took a train to the West") assert(inputIds == [1, 20628, 1183, 3614, 263, 7945, 304, 278, 3122]) } ``` However, you don't usually need to tokenize the input text yourself - the [`Generation` code](https://github.com/huggingface/swift-transformers/blob/17d4bfae3598482fc7ecf1a621aa77ab586d379a/Sources/Generation/Generation.swift#L82) will take care of it. - `Hub`. Utilities to download configuration files from the Hub, used to instantiate tokenizers and learn about language model characteristics. - `Generation`. Algorithms for text generation. Currently supported ones are greedy search and top-k sampling. - `Models`. Language model abstraction over a Core ML package. ## Supported Models 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_. They are high up in our [priority list](#roadmap). ## 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`. ## 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: "0.1.5") ] ``` And then, add the Transformers library as a dependency to your target: ``` targets: [ .target( name: "YourTargetName", dependencies: [ .product(name: "Transformers", package: "swift-transformers") ] ) ] ``` ## <a name="roadmap"></a> Roadmap / To Do - [ ] Tokenizers: download from the Hub, port from [`tokenizers`](https://github.com/huggingface/tokenizers) - [x] BPE family - [x] Fix Falcon, broken while porting BPE - [x] Improve tests, add edge cases, see https://github.com/xenova/transformers.js/blob/27920d84831e323275b38f0b5186644b7936e1a2/tests/generate_tests.py#L24 - [x] Include fallback `tokenizer_config.json` for known architectures whose models don't have a configuration in the Hub (GPT2) - [ ] Port other tokenizer types: Unigram, WordPiece - [ ] [`exporters`](https://github.com/huggingface/exporters) – Core ML conversion tool. - [x] Allow max sequence length to be specified. - [ ] Allow discrete shapes - [x] Return `logits` from converted Core ML model - [x] Use `coremltools` @ `main` for latest fixes. In particular, [this merged PR](https://github.com/apple/coremltools/pull/1915) makes it easier to use recent versions of transformers. - [ ] Generation - [ ] Nucleus sampling (we currently have greedy and top-k sampling) - [ ] Use [new `top-k` implementation in `Accelerate`](https://developer.apple.com/documentation/accelerate/bnns#4164142). - [ ] Support discrete shapes in the underlying Core ML model by selecting the smallest sequence length larger than the input. - [ ] Optimization: cache past key-values. - [ ] Encoder-decoder models (T5) - [ ] [Demo app](https://github.com/huggingface/swift-chat) - [ ] Allow system prompt to be specified. - [ ] How to define a system prompt template? - [ ] Test a code model (to stretch system prompt definition) ## 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"