# Count total and trainable parameters
total_params = sum(p.numel() for p in model.parameters())
trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)

print(f"Total parameters: {total_params:,}")
print(f"Trainable parameters: {trainable_params:,}")
print(f"% of trainable parameters: {100*trainable_params/total_params:.2f}%")

# >> Total parameters: 427,616,513
# >> Trainable parameters: 1,376,256
# >> % of trainable parameters: 0.32%