what is mixtral model

Generated on 2/17/2025

1 search

The "Mistral model" mentioned in the context refers to the Mistral 7 billion parameter model from Mistral's Hugging Face space. It is used in demonstrations at WWDC to showcase optimizations in model execution, such as quantization and stateful KV cache techniques in Core ML. These optimizations help in running the model more efficiently on Apple devices, particularly on macOS. The Mistral model is an example of how Apple is integrating advanced machine learning models into its ecosystem, allowing developers to leverage these capabilities in their applications.

For more details, you can refer to the Platforms State of the Union session.

Deploy machine learning and AI models on-device with Core ML

Deploy machine learning and AI models on-device with Core ML

Learn new ways to optimize speed and memory performance when you convert and run machine learning and AI models through Core ML. We’ll cover new options for model representations, performance insights, execution, and model stitching which can be used together to create compelling and private on-device experiences.

Explore machine learning on Apple platforms

Explore machine learning on Apple platforms

Get started with an overview of machine learning frameworks on Apple platforms. Whether you’re implementing your first ML model, or an ML expert, we’ll offer guidance to help you select the right framework for your app’s needs.

Platforms State of the Union

Platforms State of the Union

Discover the newest advancements on Apple platforms.

Bring your machine learning and AI models to Apple silicon

Bring your machine learning and AI models to Apple silicon

Learn how to optimize your machine learning and AI models to leverage the power of Apple silicon. Review model conversion workflows to prepare your models for on-device deployment. Understand model compression techniques that are compatible with Apple silicon, and at what stages in your model deployment workflow you can apply them. We’ll also explore the tradeoffs between storage size, latency, power usage and accuracy.

Accelerate machine learning with Metal

Accelerate machine learning with Metal

Learn how to accelerate your machine learning transformer models with new features in Metal Performance Shaders Graph. We’ll also cover how to improve your model’s compute bandwidth and quality, and visualize it in the all new MPSGraph viewer.

Support real-time ML inference on the CPU

Support real-time ML inference on the CPU

Discover how you can use BNNSGraph to accelerate the execution of your machine learning model on the CPU. We will show you how to use BNNSGraph to compile and execute a machine learning model on the CPU and share how it provides real-time guarantees such as no runtime memory allocation and single-threaded running for audio or signal processing models.