how do I reduce the size of an ml model to run on it on my mac

To reduce the size of a machine learning model for running on your Mac, you can use several model compression techniques available in Apple's Core ML tools. Here are some steps and techniques you can consider:

Model Conversion and Optimization: Convert your model into the Core ML format using Core ML tools. This conversion is optimized for execution on Apple silicon, leveraging the unified memory, CPU, GPU, and neural engine for efficient compute.
Quantization: Use quantization techniques to reduce the model size. For example, you can apply 4-bit quantization, which is especially optimized for GPUs on Macs. This can significantly reduce the model size while maintaining performance. You can use the CT optimize module to specify the compression configuration, such as using the linear quantizer with a specific data type and granularity.
Compression Techniques: Explore other compression techniques like sparsity, which can be combined with quantization to further reduce model size. These techniques are designed to work well with Apple's neural engine.
Testing and Tuning: After applying compression techniques, it's important to test and tune the model to ensure that the output quality remains acceptable. Quantization, for instance, can affect output quality, so additional testing is recommended.

For more detailed guidance, you can refer to the session Bring your machine learning and AI models to Apple silicon (02:47) which covers model compression techniques in depth.