Model weights are literally a compressed blob of binary data. The end result of a compilation process.

So is a zip file of source code. There is no 'compilation process'. Model training isn't a fixed end point. You start with random weights. You train. The weights get better. You do this iteratively. At some point you say 'good enough' and release. People doing further training would start with those weights and further iterate. Demanding the original training data and training process would be equivalent to demanding a completely replay-able sequence of editing steps starting with a blank text file and allowing you to replay ever edit that led the original author to the released source code.

Now there are reasons you might want to know about the training data when you wouldn't care about the authoring process used by a traditional open-source process. And these get at the reason LLM's are different than traditional software and so maybe our existing definitions of what 'open-source' means aren't a good match for LLM's. Of course there is software associated with LLM's (beyond the weight) -- defining the structure of the particular neural net those weights fit into. In every open-weights model that I'm aware of that software is open source (though trivial).

The source is actually all training data, plus the software used for training, including some scripts or instructions to run the entire thing end to end on your own computer. That's what open source has always been about.

The weights are the output of a program, it's a binary. It's not source.

> obsession with precise terms

They’re not really even being precise. The relevant software freedom, from the FSF is []

  The freedom to study how the program works, and change it so it does your computing as you wish (freedom 1). Access to the source code is a precondition for this.

Ported to the model world, this is fulfilled by sharing the weights and implementation. There’s almost nothing that having the training data gets you (other than actually training it). The weights plus a reference implementation let you see all the states to study the behavior, and let you fine tune it to do your bidding (the abliteration etc). The freedom is satisfied.

Some might argue that without the training data you couldn’t do some classes of experiments to see how it works, say leave-one-out retraining. I’d argue things like that are not really about the model but about ML research or the class of models, which while interesting is not a free software pre-requisite.

[] https://www.gnu.org/philosophy/free-sw.html#four-freedoms

Agreed, The spirit is fulfilled, as evidenced by the massive ongoing development of, effectively, new weights grown out of the sourced weights,

> Software engineering’s obsession with precise terms is actually one of the things I’m glad is dying with ai automation.

Yes. The next time you go to your doctor you should hope he's not being overly precise; or the engineer that builds the bridge; or the software engineer that implemented the embedded software in your insulin pump.

There is no precise thinking without precise terms.