Simon Willison’s Weblog


Introducing Apple’s On-Device and Server Foundation Models. Apple Intelligence uses both on-device and in-the-cloud models that were trained from scratch by Apple.

Their on-device model is a 3B model that "outperforms larger models including Phi-3-mini, Mistral-7B, and Gemma-7B", while the larger cloud model is comparable to GPT-3.5.

The language models were trained on unlicensed scraped data - I was hoping they might have managed to avoid that, but sadly not:

We train our foundation models on licensed data, including data selected to enhance specific features, as well as publicly available data collected by our web-crawler, AppleBot.

The most interesting thing here is the way they apply fine-tuning to the local model to specialize it for different tasks. Apple call these "adapters", and they use LoRA for this - a technique first published in 2021. This lets them run multiple on-device models based on a shared foundation, specializing in tasks such as summarization and proof-reading.

Here's the section of the Platforms State of the Union talk that talks about the foundation models and their fine-tuned variants.

As Hamel Husain says:

This talk from Apple is the best ad for fine tuning that probably exists.

The video also describes their approach to quantization:

The next step we took is compressing the model. We leveraged state-of-the-art quantization techniques to take a 16-bit per parameter model down to an average of less than 4 bits per parameter to fit on Apple Intelligence-supported devices, all while maintaining model quality.

Still no news on how their on-device image model was trained. I'd love to find out it was trained exclusively using licensed imagery - Apple struck a deal with Shutterstock a few months ago.