import coremltools as ct from coremltools.models.neural_network import quantization_utils # load full precision model model_fp32 = ct.models.MLModel(modelPath) model_fp16 = quantization_utils.quantize_weights(model_fp32, nbits=16) model_fp16.save("reduced-model.mlmodel") I'm testing it with the model from one of Apple's source codes(GameBoardDetector), and it works fine, reduces the model size by half. But there are several problems with my model(trained on CreateML app using Full Network): Quantizing to float 16 does not work(new file gets created with reduced only 0.1mb). Quantizing to below 16 values cause errors, and no file gets created. Here are additional metadata and precisions of models. Working model's additional metadata and precision: Mine's additional metadata and precision:

I'm using the new SpeechAnalyzer framework to detect certain commands and want to improve accuracy by giving context. Seems like AnalysisContext is the solution for this, but couldn't find any usage example. So I want to make sure that I'm doing it right or not. let context = AnalysisContext() context.contextualStrings = [ AnalysisContext.ContextualStringsTag("commands"): [ "set speed level", "set jump level", "increase speed", "decrease speed", ... ], AnalysisContext.ContextualStringsTag("vocabulary"): [ "speed", "jump", ... ] ] try await analyzer.setContext(context) With this implementation, it still gives outputs like "Set some speed level", "It's speed level", etc. Also, is it possible to make it expect number after those commands, in order to eliminate results like "set some speed level to" (instead of two).