Nvidia Nemotron is also an open training source model, though a portion of its dataset remains proprietary.

Quoting lambda's comment:

> Note that the Nemotron models are generally stronger than Olmo and K2 Think V2 (according to Artificial Analysis benchmarks), and there is a lot of overlap in their datasets (lots of datasets are based on the same sources with different filtering, Olmo and K2 Think V2 both have used some Nemotron datasets).

> But yeah, Nemotron is a modern and fairly capable LLM, even the 122b is more capable than Deepseek R1 (a 671b model) on most benchmarks, and there's also the recently released 550b Ultra now.

https://news.ycombinator.com/item?id=48492439

> What most people miss IMO is that this is not a team who is doing this for the fourth time like virtually any other LLM provider and who could learn from its own past experiences. I bet if the team would do another model training they could get way better results at one fourth of the costs.

Going forward would be such open source, open data and open recipe models possibly someday even with the training being crowd sourced if not inference like the BitTorrent model.

Lastly, even Chinese models (GLM, Deepseek, MiMax) work really really good and any user would testify that they do not miss OpenAI/Anthropic/Gemini at all if they're using those Chinese models which is argument enough that with such models, no one is going to miss Chinese models as well.

My last hope for soverign AI is from Chinese open models

https://chat.publicai.co

The training data and the Apertus LLM may contain or generate information that directly or indirectly refers to an identifiable individual (Personal Data). You process Personal Data as independent controller in accordance with applicable data protection law. SNAI will regularly provide a file with hash values for download which you can apply as an output filter to your use of our Apertus LLM. The file reflects data protection deletion requests which have been addressed to SNAI as the developer of the Apertus LLM. It allows you to remove Personal Data contained in the model output. We strongly advise downloading and applying this output filter from SNAI every six months following the release of the model.

Who confirms those requests are legit?

There were a number of use cases where we needed to use Gemini (audio modality), and Ultra has been a VERY cost-effective alternative once we got through the nuances.

Why the emphasis on sovereign? Open is good enough. No?

the swiss have no gpus

How many normal people do you know who use "ChatGPT"? A lot, probably.

How many even know what "Gemma" is, let alone have downloaded llama.cpp, a GGUF file from Hugginface, and run "llama-server" from a text console with all the correct command arguments? How many are thinking about this use case when speccing out their next computer? Where is the breathless marketing copy boasting x tok/s?

We are sleepwalking into slavery.

The jokes write themselves.

Not looking good so far

I think a problem with open-weight models is that while you can improve them, you are not going to create the next generation of LLMs by fine-tuning. We are at the mercy of frontier labs for access to SOTA LLMs. For example, Anthropic recently started requiring identity verification for Claude [0], same for OpenAI [1].

If one day China's distillation labs stop releasing their LLMs as open-weight, I doubt American labs will continue to release free LLM weights without that competition.

That's where fully open pipelines shine: they enable the community to create the next generation of SOTA LLMs. That is the only way LLMs truly become sovereign.

[0]: https://news.ycombinator.com/item?id=48618455

[1]: https://news.ycombinator.com/item?id=48618606