I’m implementing a de-noiser using deep learning.
Here’s the steps I followed:

I have a pytorch model with 467k parameters (in float) that is 467*4 = 1.9MB big
I converted it into ONNX format: size still 1.9MB
I followed the AOT instructions and got a bundle for my arm-m33 architecture from the ONNX model.
I checked the results of the inference on arm and they match with those of pytorch.
I had a look at the model.weights.bin created in the bundle and it is 20MB, this is a problem for me

Unfortunately the client design is not going to have that kind of memory.
Is there any way to shrink the weights down? (ideally to a size close to the ONNX model)