How can I use Mistral on-device

To use Mistral on-device, you can follow these steps as outlined in the WWDC sessions:

Model Training and Preparation:
- Define and Train the Model: Start by defining the model architecture and training it using libraries like PyTorch or TensorFlow. Utilize Apple silicon and the unified memory architecture on Mac for high-performance training.
- Convert to Core ML Format: Convert the trained model into Core ML format using Core ML tools. This step includes optimizing the model representation and parameters to achieve great performance while maintaining accuracy.
Model Integration:
- Integrate with Apple Frameworks: Use Core ML to integrate the model into your app. Core ML provides a unified API for performing on-device inference across various machine learning and AI model types. It optimizes hardware-accelerated execution across the CPU, GPU, and neural engine.
Optimization Techniques:
- Quantization and KV Cache: Apply techniques like quantization and efficient key-value caching to optimize the model further. For example, the Mistral 7B model can be converted to a Core ML model with post-training per-block quantization to run smoothly on Apple silicon.
Running the Model:
- Execution on Device: Once integrated, you can run the model on your device. Core ML optimizes the execution, ensuring efficient performance.

For a detailed walkthrough, you can refer to the following sessions and chapters:

These sessions provide comprehensive guidance on preparing, optimizing, and deploying machine learning models, including Mistral, on Apple devices.