We (developers and tech managers) really need to hold the line on this terminology. This is a full actual open source LLM. The usual “open inference” model is not.
Usually “open source” for an LLM means you get the weights and the inference code, which I’ve started calling “open inference”. It’s certainly good and useful, but it’s not the actual source of the model.
I find people get into silly arguments about the terminology because they’re focused on whether the “source” is “open” and not on what the “source” is actually the source of.
“Weights available” indicates even the weights aren’t “open” in the usual software meaning of the term, as they typically come with restrictive licenses (more restrictive than copyleft or attribution).
In a space where copyrighted material and personal data is an issue, actually academic research can actually use many exceptions (at least under legislation in Europe) . This unfortunately also means that one cannot pass on the data sources under truly licencing beyond research use/ reproducibility.
My perspective mostly comes from the fact that universities will claim parents from research. For something like an AI model, I guess it could go either way.
Personally, I am all in favor of increasing commercialisation of academic research, if and only if the money earned goes first and foremost to reducing tuition for students.
Alas, tuition seems to ever inflate upwards. I remember when my university built a football stadium- all sorts of talking points were floated about, like how the sports programs pay for themselves, it's good for recruiting, we wouldn't get increased tuition, blah blah blah. Well, once construction finished, guess what? Students got a $400 stadium "fee" added onto enrollment each semester. Sure, it wasn't technically part of tuition, but it also wasn't optional.
That alone pretty much guaranteed that I ignored every attempt from the alumni association that came begging for donations after I graduated. They run a well oiled machine, and yet somehow no matter how much money the school had, students kept paying more each year.
So, yeah, I do know many (most?) research schools have commercializing offices dedicated to making money off of their research, and they have every incentive to keep doing so, whether via parents or keeping things secret to commercial partners. They just don't do anything publicly visible with the money.
Edit: sorry for the stream of consciousnesses, I'm running on an hour of sleep and just realized I don't have the energy for a coherent, concise response
I have worked part-time (hardware technician) at two US-based companies, entirely open-source (including firmware), that are still profitable (one for decade+).
My limited understanding (finance side) is that most customers prefer to buy from (even open-source) companies, for several reasons:
1) They don't have time/desire to assemble hundreds of components, prefering drop-in solution
2) Our manufacturing facility has experience / protools, produces products within tighter tolerances
3) Many people understand their own manufacturing limitations and would prefer warranted solutions, without their understood dangers of DIY
I personally dropped out of US grad school because it was the antithesis of open-source licensing.
Disclosure: I am an AMD shareholder, excited about this recent announcement
There's a huge difference between software and AI models. We can debate why that happens but it's a fact. Companies are willing to release open weights but virtually no one is willing to create open source models. Shaming and well actuallying has achieved nothing so far.
And I'm not arguing that they should release open source models. There's no shame in releasing an open-inference model. But I think I'm fair in saying they should use an accurate term for what they do release.
There is nothing "source" about the "open source models" that companies typically release. The use of the term "open source" is deliberate marketing BS. If you want to argue there's a difference between software and a model, then don't use software terms that are already well-defined to refer to some property of the model.