Swift Transformers Reaches 1.0 – and Looks to the Future

We released swift-transformers two years ago (!) with the goal to support Apple developers and help them integrate local LLMs in their apps. A lot has changed since then (MLX and chat templates did not exist!), and we’ve learned how the community is actually using the library.

We want to double down on the use cases that provide most benefits to the community, and lay out the foundations for the future. Spoiler alert: after this release, we’ll focus a lot on MLX and agentic use cases 🚀

What is swift-transformers

swift-transformers is a Swift library that aims to reduce the friction for developers that want to work with local models on Apple Silicon platforms, including iPhones. It includes the missing pieces that are not provided by Core ML or MLX alone, but that are required to work with local inference. Namely, it provides the following components:

• Tokenizers. Preparing inputs for a language model is surprisingly complex. We've built a lot of experience with our tokenizers Python and Rust libraries, which are foundational to the AI ecosystem. We wanted to bring the same performant, ergonomic experience to Swift. The Swift version of Tokenizers should handle everything for you, including chat templates and agentic use!
• Hub. This is an interface to the Hugging Face Hub, where all open models are available. It allows you to download models from the Hub and cache them locally, and supports background resumable downloads, model updates, offline mode. It contains a subset of the functionality provided by the Python and JavaScript libraries, focused on the tasks that Apple developers need the most (i.e., uploads are not supported).
• Models and Generation. These are wrappers for LLMs converted to the Core ML format. Converting them is out of the scope of the library (but we have some guides). Once they are converted, these modules make it easy to run inference with them.