hi @eleflea, check out https://github.com/pytorch/vision/blob/master/references/classification/train_quantization.py 20 for one example.  One approach which has proven to work well is:

start QAT training from a floating point pre-trained model and with observers and fake_quant enabled
after a couple of epochs, freeze the BN stats if your network has any BNs (epoch == 3 in the example)
after a couple of epochs, disable observers (epoch == 4 in the example)