how do i run an llm on iphone
Generated on 7/30/2024
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To run a large language model (LLM) on an iPhone, you can follow these steps:
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Model Training and Preparation:
- Start by defining the model architecture and training the model using libraries such as PyTorch or TensorFlow. You can take full advantage of Apple silicon and the unified memory architecture on Mac for this purpose.
- Once trained, convert the model into the Core ML format using Core ML tools. This step involves optimizing the model representation and parameters to achieve great performance while maintaining good accuracy.
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Model Conversion:
- Use Core ML tools, an open-source Python package, to convert your PyTorch model into the Core ML format. Core ML tools offer various optimization techniques, such as quantization and efficient key-value caching in LLMs, to enhance performance.
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Model Integration:
- Integrate the converted model into your app using the Core ML framework. Core ML optimizes hardware-accelerated execution across the CPU, GPU, and neural engine, ensuring efficient performance on Apple devices.
- Core ML provides a unified API for performing on-device inference across a wide range of machine learning and AI model types.
For more detailed guidance, you can refer to the following sessions from WWDC 2024:
- Explore machine learning on Apple platforms (07:17)
- Deploy machine learning and AI models on-device with Core ML (01:07)
- Bring your machine learning and AI models to Apple silicon (02:47)
These sessions cover the entire workflow from training and converting models to integrating and running them on Apple devices.
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.
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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.
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
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