coreml

You convert PyTorch or TensorFlow models to CoreML by transforming them into an mlmodel file, applying optional quantization or stateful optimizations, and integrating the result into Swift apps for on-device inference.

Optimizing machine learning models for on-device deployment involves applying quantization and pruning techniques to compress the model, resulting in smaller file sizes and faster execution on Apple devices.

Yes, CoreML supports running stateful transformer models with KV-cache in iOS apps by maintaining persistent states across inference calls, enabling efficient transformer-like generation directly on Apple devices.

Yes, CoreML supports multi-function models that combine LoRA adapter workflows with a shared base model, enabling flexible and efficient on-device execution for customized machine learning tasks.

MLTensor pipelines improve model performance on macOS by stitching multiple models together and executing asynchronous operations, optimizing on-device throughput and reducing latency for complex workflows.

On-device machine learning provides private, efficient, and low-latency execution by processing data locally on Apple hardware, ensuring user privacy without requiring network connectivity or external API calls.