axiom-ios-ml

Convert PyTorch or TensorFlow models to CoreML format to enable on-device machine learning inference on iOS devices. This process ensures your trained models can run natively within iOS applications for offline functionality and privacy.

Optimize on-device iOS inference by compressing machine learning models using quantization and palettization techniques. These methods reduce the overall model size and improve execution speed without significantly degrading prediction accuracy on iPhones and iPads.

Stateful LLM inference with KV-cache is supported for iOS devices to facilitate efficient on-device text generation. This mechanism maintains context across interactions while minimizing memory overhead during model execution.

Implement speech-to-text transcription in iOS applications using the SpeechAnalyzer API to process real-time audio or file-based inputs. This integration provides accurate on-device transcription, ensuring user privacy and offline functionality.

Converting models to CoreML fully supports custom image recognition workflows for iOS deployment. You can convert your trained model, compress it for faster loading, and execute smooth on-device inference directly within your application.

Quantization and palettization reduce machine learning model size and accelerate on-device inference speeds on iOS. These compression techniques are essential constraints to overcome strict mobile memory and processing limitations.