The model will change, but since you’ve manipulated the internal parameters manually, the in_features and other attributes won’t be changed as seen here:
# default setup
model = nn.Linear(10, 10, bias=False)
print(model)
> Linear(in_features=10, out_features=10, bias=False)
x = torch.randn(1, 10)
out = model(x)
print(out.shape)
> torch.Size([1, 10])

# manual manipulation
with torch.no_grad():
    model.weight = nn.Parameter(torch.randn(1, 10))
print(model)
> Linear(in_features=10, out_features=10, bias=False) # wrong, as you've manually manipulated the parameter

out = model(x)
print(out.shape)
> torch.Size([1, 1]) # new, expected output shape

I would generally not recommend to manipulate internals manually unless you are sure that’s the right approach.