Apple’s WWDC ’24: Revolutionizing Artificial Intelligence with Core ML
Introduction
As technology continues to evolve, the role of artificial intelligence (AI) in our daily lives is becoming increasingly significant. Apple’s Worldwide Developers Conference (WWDC) ’24 showcased the latest advancements in AI, particularly with the introduction of Core ML. This article will explore the best new Core ML features and how they can be utilized to run AI-enhanced features in everyday tasks.
The Power of Apple Intelligence
During the WWDC ’24 keynote and subsequent sessions, Apple unveiled Apple Intelligence, reiterating their commitment to efficient, private, and on-device AI. This innovation empowers developers to integrate AI features seamlessly into their apps and operating systems, ensuring practical uses for everyday tasks.
Replicating the Mistral 7B Example
Apple’s deployment of a state-of-the-art LLM (Large Language Model) on a Mac using Core ML sparked a flurry of interest among developers. In this blog post, we’ll explore the new Core ML features that made this achievement possible. We’ll take you through the process of running Mistral 7B on a Mac using less than 4GB of memory.
New Core ML Features
The WWDC ’24 session showcased several exciting Core ML features that simplified the development process. We’ll highlight two crucial innovations:
Swift Tensor
The new MLTensor type in Core ML provides a high-level abstraction, similar to those available in Python frameworks like NumPy and PyTorch. This simplifies working with tensor data in Swift, eliminating the need for custom code.
Stateful Buffers
Core ML’s stateful buffers allow for efficient state maintenance, reducing overhead and increasing performance. This feature is particularly beneficial for large-scale language models.
Running Mistral 7B with Swift
To run Mistral 7B with Swift, you’ll need to follow these steps:
- Clone the
previewbranch of theswift-transformersrepository. - Download the converted Core ML model.
- Run inference using Swift:
swift run transformers "Best recommendations for a place to visit in Paris in August 2024:" --max-length 200 Mistral7B-CoreML/StatefulMistralInstructInt4.mlpackage
Frequently Asked Questions
What is Core ML?
Core ML is a software framework developed by Apple that enables the integration of machine learning models into apps and operating systems.
How does Core ML improve AI in apps?
Core ML allows developers to harness the power of Apple Silicon, ensuring efficient, private, and on-device AI. This empowers developers to create AI-enhanced features that are deeply integrated with apps and the OS.
What are the benefits of Stateful Buffers?
Stateful Buffers in Core ML reduce overhead and increase performance by reserving memory for state data on the GPU. This makes them particularly beneficial for large-scale language models.
How do I get started with Core ML?
To get started with Core ML, developers can clone the preview branch of the swift-transformers repository and follow the steps outlined in this article to run Mistral 7B with Swift.
What’s next for Core ML?
Apple is committed to making AI accessible to all developers. We’re working on incorporating new features and API changes into the preview branch of swift-transformers. Stay tuned for exciting updates!
Conclusion
In conclusion, the latest advancements in Core ML and Apple Intelligence showcased at WWDC ’24 have the potential to revolutionize AI in everyday tasks. By utilizing the new features and API changes outlined in this article, developers can create AI-enhanced features that are efficient, private, and on-device. With Core ML, the possibilities are endless.
