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At WWDC 2024, Apple discussed various techniques for compressing machine learning models to make them suitable for deployment on devices like iPhones and iPads. In the session titled "Bring your machine learning and AI models to Apple silicon," several methods were highlighted:

1. Palletization: This technique allows for flexibility in choosing the number of bits to achieve different compression ratios. For instance, using eight-bit palletization can reduce a model's size significantly, making it more feasible for mobile devices.

2. Quantization: This involves mapping model weights to integer values, which are stored with quantization parameters to convert them back to float values when needed. This helps in reducing the model size while maintaining performance.

3. Pruning: By setting the smallest values in a weight matrix to zero, pruning helps in efficiently packing model weights with sparse representation, further reducing the model size.

4. Compression Workflows: The session also discussed workflows that use calibration data to achieve better compression results, allowing models to maintain accuracy even after significant size reduction.

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