The output values of your model are logits, so they do not have a bound on their values.
nn.CrossEntropyLoss will apply a F.log_softmax on the outputs, so that they will be normalized to log probabilities before feeding them to nn.NLLLoss.
Your example output value just indicates that class0 is more likely for the sample than class1.
If your training still doesn’t work, I would still suggest to try to overfit a small sample and check, if it’s working.