That gives the same problem sadly.

I have found the solution though. The trainer/optimizer is not exported along 
with the other data unless specifically called through trainer.save_states() 
which means the rise in loss after is because the trainer doesn't know what it 
was doing.

A bit weird that the official docs/examples do not mention having to save your 
trainer states explicitly when talking about saving and loading models to 
resume training.





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