The state_dicts have the same lengths, my mistake. That was quite odd and confusing, though the lengths make sense now. The naming differences shouldn’t be much of a problem.
I’ll deal with that and then perform the fusion.
It could have been great if I could perform a QAT using CUDA instead of dealing with the conversion, hopefully a CUDA support will available soon.
So far the PTQ with optimized calibration works really well with my data,  I get almost no drop in AP:) , but maybe in slightly different cases it would be useful in order to avoid drop in AP.
Thanks.