Often you would want to save the state_dict of your model for the lowest validation loss (or highest validation accuracy).
Depending on your use case, you could split the training data into a training and validation set (e.g. using SubsetRandomSampler or Subset), observe the validation loss, and save the best model.
After saving you could calculate the test accuracy to get a signal of your model’s performance on unseen data.