mistral
Generated on 8/3/2024
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The Mistral model is mentioned in several sessions at WWDC 2024, particularly in the context of machine learning and AI. Here are the relevant sessions and some details:
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Accelerate machine learning with Metal
- Chapter: MPS Graph viewer
- Discusses the use of the Mistral model with 7 billion parameters, converted to Core ML, and how to visualize and understand the structure of the graph using the new MPS Graph viewer in Xcode 16.
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Deploy machine learning and AI models on-device with Core ML
- Chapter: Models with state
- Demonstrates the performance improvements when using stateful models, specifically comparing the KV cache implementations using the Mistral 7 billion model on a MacBook Pro with an M3 Max chip.
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Bring your machine learning and AI models to Apple silicon
- Chapter: Stateful model
- Provides a detailed walkthrough on preparing the Mistral 7 billion model for Apple silicon, including making the model stateful and handling KV cache efficiently.
These sessions provide a comprehensive overview of how the Mistral model is utilized and optimized for Apple's hardware and software ecosystem.
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Build a spatial drawing app with RealityKit
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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.
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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.
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.