Imho the weights are the real meat for most typical models, you can run with them and continue training them with your own code. It's not even guaranteed that the original code would be very useful for that.
But if you are going to make that distinction, for which you can make a case I think, shouldn't you include a third dimension, 'data'? The code alone is hardly useful if you want to rebuild the weights, but all it tells you is that they're loading their proprietary data and then using PyTorch to set up and train the model. You can't reproduce anything using just that. So the real equivalent of open source would be imho either open weights, or open data plus code plus weights (the latter are arguably redundant, but still practical to include). Given that the size of that repo will typically be gigantic, I think open weights is the case we should really be focusing on. I'd rather have a paper explaining the model together with the weights, rather than code that I can't run anyway, if I'm designing an algorithm to continue training the model.
But if you are going to make that distinction, for which you can make a case I think, shouldn't you include a third dimension, 'data'? The code alone is hardly useful if you want to rebuild the weights, but all it tells you is that they're loading their proprietary data and then using PyTorch to set up and train the model. You can't reproduce anything using just that. So the real equivalent of open source would be imho either open weights, or open data plus code plus weights (the latter are arguably redundant, but still practical to include). Given that the size of that repo will typically be gigantic, I think open weights is the case we should really be focusing on. I'd rather have a paper explaining the model together with the weights, rather than code that I can't run anyway, if I'm designing an algorithm to continue training the model.