Poor move IMO. Training data should be required to be released to be considered an open source model. Without it all I can do is set weights, etc. Without training data I can't truly reproduce the model, inspect the data for biases/audit the model for fairness, make improvements & redistribute (a core open source ethos).
Keeping the training data closed means it's not truly open.
Obviously the biggest example here is all of that training data which was scraped from the public web (or worse) and cannot be relicensed because the model producers do not have permission to relicense it.
There are other factors too though. A big one is things like health data - if you train a model that can e.g. visually detect cancer cells you want to be able to release that model without having to release the private health scans that it was trained on.
See their FAQ item: Why do you allow the exclusion of some training data? https://opensource.org/ai/faq#why-do-you-allow-the-exclusion...
The actual poor move is trying to fit the term "open source" onto AI models at all, rather than new terms with names that actually match how models are developed.
I think it greatly diminishes the value of the concept and label of open-source. And it's honestly a bit tragic.
For a (somewhat extreme) example, what if I use the model to write children's stories, and suddenly it regurgitates Mein Kampf? That would certainly ruin the day.
We need better tools to examine the weights (what gets activated to which extent for which topics, for example). Getting full training corpus, while nice, cannot be our only choice.
I can think of a few ways. Perhaps I'd use an LLM to find objectionable content. But anyway, it is the same argument as you can have against e.g. the Linux kernel. Are you going to read every line of code to see if it is secure? Maybe, or maybe not, but that is not the point.
The point is now a model is a black box. It might as well be a Trojan horse.
How would you download it?
Where would you store it?
Cheaper to train your own.
Big tech has been abusing open source to cheaply capture most of the internet and e-commerce anyway, so perhaps it's time we walked away from the term altogether.
The OSI has abdicated the future of open machine learning. And that's fine. We don't need them.
"Free software" is still a thing and it means a very specific and narrow set of criteria. [1, 2]
There's also "Fair software" [3], which walks the line between CC BY-NC-SA and shareware, but also sticks it to big tech by preventing Redis/Elasticsearch capture by the hyperscalers. There's an open game engine [4] that has a pretty nice "Apache + NC" type license.
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Back on the main topic of "open machine learning": since the OSI fucked up, I came up with a ten point scale here [5] defining open AI models. It's just a draft, but if other people agree with the idea, I'll publish a website about it (so I'd appreciate your feedback!)
There are ten measures by which a model can/should be open:
1. The model code (pytorch, whatever)
2. The pre-training code
3. The fine-tuning code (which might be very different from the pre-training code)
4. The inference code
5. The raw training data (pre-training + fine-tuning)
6. The processed training data (which might vary across various stages of pre-training and fine-tuning: different sizes, features, batches, etc.)
7. The resultant weights blob(s)
8. The inference inputs and outputs (which also need a license; see also usage limits like O-RAIL)
9. The research paper(s) (hopefully the model is also described and characterized in the literature!)
10. The patents (or lack thereof)
A good open model will have nearly all of these made available. A fake "open" model might only give you two of ten.
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[2] https://en.wikipedia.org/wiki/Free_software
[3] https://fair.io/
They do continue to require the core freedoms, most importantly "Use the system for any purpose and without having to ask for permission". That's why a lot of the custom licenses (Llama etc) don't fit the OSI definition.