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Notes -
I mean it's certainly possible to release your training code as well as the resulting weights for an LLM -- now I'm curious as to whether this company is actually doing that or not?
If not, agreed that "OS" is a big misnomer here -- there are certainly lots of individuals floating around who might like to train their own version of this and could afford to do so (FIRE startup retirees spring to mind) and "you can use our weights" is quite different from "you can try to make improvements on our process". More like free beer than free speech.
There are tiers to this, from just weights release to full data+code+weights. Chinese labs mostly release weights and tech report with a reproducible (given some effort) recipe, sometimes code, rarely some or all of the data (more often parts of post-training data, though in these cases it's typically just links to datasets that have already been open).
I think nitpicking about open source is uninteresting when the recipe is available. This is a very dynamic field of applied science, rather than labor-intensive programming exercise. The volume of novel code in a given LLM project is comparable to a modest Emacs package, what matters is ideas (derisked at scale). Specific implementations are usually not that valuable – DeepSeek's GRPO, as described in their papers, has been improved upon in the open multiple times by this point. Data composition is dependent on your own needs and interests, there are vast open datasets, just filter them as you see fit.
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