If I understand the issue correctly, you are using a model, call model.eval(), feed static input into it, and are seeing different results.
This is unexpected and we would need to have an executable code snippet in order to debug it.
To do so, I don’t believe that loading a specific state_dict or the real dataset is necessary and would claim that the issue should also be visible when just initializing the model and feeding it with a static (random) input.
Write a script, which creates a model instance, create a single random input tensor, call model.eval(), and run multiple forward passes. If the output differs by more than the known limited floating point precision, post the code here.