Of course both things are useful in practice, and unless you’re a free-as-in-freedom software purist, free-as-in-beer software is also very useful!
But the point is exactly that the distinction matters, and conflating the terms doesn’t do either thing a favor (it also doesn’t really work well with “free software”, since the beer trope is needed to explain what you mean. “Libre” is at least non ambiguous).
Having the training data is not useful just for retraining, but also to know what the model can reasonably answer in 0-shot, to make sure it is evaluated on things it hasn’t seen before during pretraining (e.g. winograd schemas), to estimate biases or problems in the training data… if all you have is the weights, these tasks are much harder!
But the point is exactly that the distinction matters, and conflating the terms doesn’t do either thing a favor (it also doesn’t really work well with “free software”, since the beer trope is needed to explain what you mean. “Libre” is at least non ambiguous).
Having the training data is not useful just for retraining, but also to know what the model can reasonably answer in 0-shot, to make sure it is evaluated on things it hasn’t seen before during pretraining (e.g. winograd schemas), to estimate biases or problems in the training data… if all you have is the weights, these tasks are much harder!