These issues are often created either by a failure in the model saving or a change in the data loading.
To check the first aspect, you could compare the outputs of the model (in .eval()) for a constant input (e.g. torch.ones) before saving and after loading. If the outputs have a larger error than the expected floating point precision, then the saving of the parameters and buffers fails.
On the other hand, if the outputs are equal, you could check the data loading pipeline and make sure the data is processed in the same way.