Ok. I think I’ve found something.  When I try to load the model and the parameters, I have to load the model with the training categorical embedding sizes.  When I don’t load the model via model.Load_state_dict(), and just run the validation data through a instantiated model with the validation embedding sizes, it works (via CPU, I haven’t tried GPU).  But I can’t load a state_dict with validation data embedding sizes and can’t run validation data through with training embedding training sizes.
Does that make sense?
If it makes sense, I guess this brings up a question as to how to train a model that will run with new categories inside the categorical variables and thus, produce different categorical embedding sizes that could be in new data brought in.