Maybe my answer here is too basic but this is how I generally do this:
Load the model:
from model_location import model
Instantiate the model:
m = model()
Load the state dictionary required:
m.load_state_dict(torch.load(state_dictionary_saved_at_epoch_7.pt'))
(as you have saved it as saved.pt you might have to preload this dictionary and then replace state_dictionary_saved_at_epoch_7.pt' with whatever is in 'state_dict': model.state_dict() at epoch 7. (perhaps this is the subtelty of your question that most of this answer will be not addressing properly?); you will also have to push the optimizer state dictionary and loss value too but I don’t know how to do that.)
Set the training state of the model m to true (perhaps this is done by default):
m.train(True)
…and then continue training using the model now called m in this example.
Sorry if that’s too basic an answer!