Coreml llm

Core ML is a key framework for deploying and running machine learning models on Apple devices. At WWDC 2024, several sessions covered new features and optimizations for Core ML, particularly in the context of large language models (LLMs).

Key Points on Core ML and LLMs:

1. Model Integration and Optimization:

   • Core ML tools allow you to convert models from frameworks like PyTorch into the Core ML format, optimizing them for Apple hardware. This includes techniques like quantization and efficient key-value caching, which are particularly useful for LLMs (Platforms State of the Union).

2. New Features in Core ML:

   • ML Tensor: Simplifies the computational glue code for stitching models together.
   • State Management: Improves inference efficiency for large language models.
   • Multifunction Models: Allows a single model to perform multiple tasks, which can be useful for LLMs that need to handle various types of queries (Deploy machine learning and AI models on-device with Core ML).

3. Performance Tools:

   • Updated performance reports provide insights into the cost of each operation, helping you optimize your models further (Explore machine learning on Apple platforms).

4. Hardware Utilization:

   • Core ML automatically segments models across CPU, GPU, and the neural engine to maximize hardware utilization, which is crucial for running complex models like LLMs efficiently (Explore machine learning on Apple platforms).

5. Advanced Control:

   • For apps with demanding graphics workloads, Metal Performance Shaders (MPS) and Accelerate framework's BNNs graph provide ways to sequence ML tasks with other workloads, optimizing GPU and CPU performance (Explore machine learning on Apple platforms).

Relevant Sessions:

1. Explore machine learning on Apple platforms
2. Deploy machine learning and AI models on-device with Core ML
3. Platforms State of the Union
4. Bring your machine learning and AI models to Apple silicon

These sessions provide a comprehensive overview of the new capabilities and optimizations in Core ML, particularly for deploying and running large language models on Apple devices.