You could use this sample code to add gaussian noise to all parameters:
with torch.no_grad():
    for param in model.parameters():
        param.add_(torch.randn(param.size()) * 0.1)

If you want to skip certain parameters, you could use model.named_parameters() and filter out all unnecessary parameters.