https://www.marble.onl/posts/considerations_for_copyrighting...
The equivalent would be someone which gives you only the binary to Libreoffice. That's perfectly fine for editing documents and spreadsheets, but suppose you want to fix a bug in Libreoffice? Just having the binary is going to make it quite difficult to fix things.
Simiarly, suppose you find that the model has a bias in terms of labeling African Americans as criminals; or women as lousy computer programmers. If all you have is the model weights of the trained model, how easily can you fix the model? And how does that compare with running emacs on the Libreoffice binary?
That's textbook fine-tuning and is basically trivial. Adding another layer and training that is many orders of magnitude more efficient than retraining the whole model and works ~exactly as well.
Models are data, not instructions. Analogies to software are actively harmful. We do not fix bugs in models any more than we fix bugs in a JPEG.
In other words there should be a reasonable line when model is called open source. In extreme view it's when the model, the training framework, and the data are available for free. This would mean open source model can be trained only on public domain data. Which makes class of open source models very, very limited.
More realistic is to make the code and the weights available. So that with some common knowledge new model can be trained, or old fine tuned, on available data. Important note: weights cannot be reproduced even if original training data is available. It will be always a new model with (slightly) different responses.
PS: If I ever make my models open I can't open the data anyway. License on images directly prohibits publishing them.
What you can't easily do is retrain from scratch using a heavily modified architecture or different training data preconditioning. So yes, it is valuable to have dataset access and compute to do this and this is the primary type of value for LLM providers. It would be great if this were more open — it would also be great if everybody had a million dollars.
I think it's pretty misguided to put down the first type of value and openness when honestly they're pretty independent, and the second type of value and openness is hard for anybody without millions of dollars to access.
And yet, the people who insist on having source code so they can edit the program and recompile it have said that for programs, having just the binary isn't good enough.
When they get bought by Oracle and progress slows to a crawl because it's not profitable enough to interest them, you can't exactly do a LibreOffice. Or they can turn around and say "license change, future versions may not be used for <market that controlling company would like to dominate>" and now you're stuck with whatever old version of the model while they steamroll your project with newer updates.
Open weights are worth nothing in terms of long term security of development, they're a toy that you can play with but you have no assurances of anything for the future.
You may complain that subsequent models are not iterative on the past and so having that old version doesn’t help; but then the data probably changes too so having the old data would largely leave you with the same old model.
As opposed to not being able to fork it at all because an "open source" model actually just means "you are allowed to use this particular release of our mystery box."
Maybe I misunderstood your original question. To be clear, the process of modifying a trained model does not require the presence of the original data. You said “deleted” which perhaps I misinterpreted. You’re not “instantiating a new model from scratch” when you modify it. You’re continuing to train it where it left off.
The general idea is to get as good of a mastery of language as possible, generally, and then fine tune to specialize on tasks