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When I published tweets showing Falcon or Llama 2 running on my Mac, I got many questions from other developers asking how to convert those models to Core ML, because they want to use them in their apps as well.

Some converted models, such as Llama 2 7B or Falcon 7B, ready for use with these text generation tools.An updated version of transformers-to-coreml, a no-code Core ML conversion tool built on exporters.An updated version of exporters, a Core ML conversion package for transformers models.swift-chat, a simple app demonstrating how to use the package.It is an evolution of swift-coreml-transformers with broader goals: Hub integration, arbitrary tokenizer support, and pluggable models. swift-transformers, an in-development Swift package to implement a transformers-like API in Swift focused on text generation.Video: Llama 2 (7B) chat model running on an M1 MacBook Pro with Core ML.

We are calling all Swift developers interested in ML – is that all Swift developers? – to contribute with PRs, bug reports, or opinions to improve this together. We are also releasing alpha libraries and tools to support developers in the journey. Today, we are publishing this guide to go through the steps required to run a model such as Llama 2 on your Mac using Core ML. We aim to create a set of tools and building blocks that help developers build faster. We have been exploring this space and would love to continue working on it with the community. However, using an LLM model such as Llama in an app involves several tasks which many people face and solve alone. We are witnessing a defining moment in computing history, where LLMs are going out of research labs and becoming computing tools for everybody. Moreover, LLMs have recently emerged as almost general-purpose tools – they can be adapted to new domains as long as we can model our task to work on text or text-like data. The ML ecosystem has matured a lot, with thousands of models that solve a wide variety of problems. I believe that ML is a new way to build software, and I know that many Swift developers want to incorporate AI features in their apps. Yet, this community soon managed to create top-notch applications that felt at home with the new paradigm.

The new devices adopted some unfamiliar decisions in the constraint space, with a combination of power, screen real estate, UI idioms, network access, persistence, and latency that was different to what we were used to before. I started writing apps for iPhones in 2007, when not even APIs or documentation existed.

I have a lot of respect for iOS/Mac developers.

Free for apple download Transformers: Revenge of the Fallen