Changing pre-trained model's parameters
I need to change parameters of a pre-trained model, not in any particular way. I'm trying this:
ids = [int(p.sum().item()) for p in model.parameters()]
print(ids[0])

for i in model.parameters():
    x = i.data
    x = x/100

    break;

ids = [int(p.sum().item()) for p in model.parameters()]
print(ids[0])

but it outputs two exactly the same numbers.

This is simple: you need to perform an in-place operation, otherwise you'll operate on a new object:
for i in model.parameters():
    x = i.data
    x /= 100
    break

Here's a minimal reproducible example:
import torch

torch.manual_seed(2021)

m = torch.nn.Linear(1, 1)

# show current value of the weight
print(next(m.parameters()))
# &gt; tensor([[-0.7391]], requires_grad=True)

for i in m.parameters():
    x = i.data
    x = x/100
    break

# same value :(
print(next(m.parameters()))
# &gt; tensor([[-0.7391]], requires_grad=True)

for i in m.parameters():
    x = i.data
    x /= 100
    break

# now, we changed it
print(next(m.parameters()))
# &gt; tensor([[-0.0074]], requires_grad=True)

P.S.: break is unnecessary in my example, but I kept it just because you used it in your example.
