The framing here is undersold in the broader discourse: "open weights" is a ruse for reproducibility. What you have is closer to a compiled binary than source code. You can run it, you can diff it against other binaries, but you cannot, in any meaningful sense, reproduce or extend it from first principles.

This matters because OSS truly depends on the reproducibility claim. "Open weights" borrows the legitimacy of open source (the assumption that scrutiny is possible, that no single actor has a moat, that iteration is democratised). Truly democratised iteration would crack open the training stack and let you generate intelligence from scratch.

Huge kudos to Addie and the team for this :)

Somewhat orthogonal but: when do we expect "volunteer" groups to provide training data for LLMs for [edit: free] for (like) hobbyist kinds of things? (Or do we?)

Like wikipedia probably provides a significant amount of training for LLMs. And that is volunteer and free. (And I love the idea of it.)

But I can imagine (for example) board game enthusiasts to maybe want to have training data for games they love. Not just rules but strategies.

Or, really, any other kind of hobby.

That stuff (I guess) gets in training data by virtue of being on chat groups, etc. But I feel like an organized system (like wikipedia) would be much better.

And if these sets were available, I would expect the foundation model trainers would love to include it. And the results would be better models for those very enthusiasts.

"open training" is something that won't ever happen for large scale models. For one, probably everyone's training datasets include large amount of questionable material: copyrighted media first and foremost (court cases have shown that AI models can regurgitate entire books almost verbatim), but also AI slop contaminating the dataset, or on the extreme end CSAM - for Grok to know how the intimate bits of children look like (which is what was shown during the time anyone could prompt it with "show her in a bikini") it obviously has to have ingested CSAM during training.

And then, a ton of training still depends on human labor - even at $2/h in exploitative bodyshops in Kenya [1], that still adds up to a significant financial investment in training datasets. And image training datasets are expensive to train as well - Google's reCAPTCHA used millions of hours of humans classifying which squares contained objects like cars or motorcycles.

[1] https://time.com/6247678/openai-chatgpt-kenya-workers/