the correct way is to make the model’s parameters not require gradients:
model = torch.load('mymodel.pth')
for p in model.parameters()
    p.requires_grad = False

Alternatively, if you are purely using the model for inference with no gradients needed, the input to the model can be volatile (this is better, it saves more memory as well):
myinput = torch.randn(10, 20) # example input
model = torch.load('mymodel.pth')
input = Variable(myinput, volatile=True)
output = model(input)

See these notes 66 for more hints.