It's good that there is a movement for open LLMs, but it's not where the battleground is right now. The battleground is local vs service LLMs, and we are losing that battle badly despite all the software being here now and viable, entirely because UX sucks.
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?
Better UX does not buy you a datacenter farm to train state of the art cutting edge models. Right now the only people who can do that are the technobility class.
Other fully open LLMs include Allen AI's OLMo 3.1 and MBZUAI's K2 Think V2, both of which have released their full training pipelines and datasets.
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.
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.
> We are at the mercy of frontier labs for access to SOTA LLMs
I disagree with this use of SOTA, and this topic is why.
Anthropic and OpenAI have “cutting-edge” models. These are beyond the state of the art but they are closed, secretive, hard to quantify.
The “state of the art” is open source, open weights models that can be inspected, studied, shared and critiqued, because that is what is meant by “the art” —- it is the knowledge and principles and evidence and materials available to all. The “state of the art” is the highest point of that.
I wish we could make this distinction and stop blessing two secretive, unverifiable loss-making companies with so much power.
(Putting that aside, I suspect — without evidence, mind you - that the endless march to solving models by making them bigger is not the solution anyway.)
Sorry but I think you’re requirement that something only be “the art” if any arbitrary person can critique it is off.
The frontier labs are working on the state of the art but it’s just art that you aren’t allowed to see.
Unfortunately.
This notion that Chinese labs are merely distilling frontier models is quite an unwarranted slur. Those labs have published WAY more useful research than US labs on RL techniques, novel model architectures, training pipelines, etc. They have also hit intelligence-per-parameter densities that US labs have yet to attain.
Apart from that, merely training a model on outputs from another model, off policy and without the logits, doesn’t really work that well.
The Chinese labs know how to build frontier level models. GLM-5.2 shows that they no longer even need Nvidia chips to do it.
I like the idea, and it has become more pressing that everyone outside the US think about tech sovereignty because the US has become an unsafe place to keep your data, but the impression I get from Apertus is that it moves at the speed of a committee. I have no expectation they'll deliver a competitive model. At least, not competitive with current models. Maybe competitive with models a year ago (though they haven't even done that yet, right?).
"the US has become an unsafe place to keep your data"
I empathize with this but curious what would make any other country a better safehaven for your data? I personally like the EU's approach to data safeguards, but are there other locales/data protections you have in mind that would keep your data "safe".
For a model that claims to focus on many languages, it's quite unreliable when it comes to simple questions like "how to say X in language Y" or "how to conjugate verb X in language Y". It keeps hallucinating words that do not exist, and when corrected, it only hallucinates a new lie.
The previous version of this model has been pretty bad, but claimed to adhere to copyright laws. However, based on my testing, that's not true either. So in my view this is completely useless.
As long as the following remains true, this release ends up a bigger contribution to science at large than most other models trained "behind closed doors":
> Fully open model: open weights + open data + full training details including all data and training recipes
Sovereign AI is not about using just one model. It's about using the right model for the right job, and getting them to talk through the solution TOGETHER before presenting the answer.
I use it extensively. It is not ready for agentic use, but as a generic driving model for RAG use cases, it is pretty competent. You can build useful software with it.
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.
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
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
I disagree with this use of SOTA, and this topic is why.
Anthropic and OpenAI have “cutting-edge” models. These are beyond the state of the art but they are closed, secretive, hard to quantify.
The “state of the art” is open source, open weights models that can be inspected, studied, shared and critiqued, because that is what is meant by “the art” —- it is the knowledge and principles and evidence and materials available to all. The “state of the art” is the highest point of that.
I wish we could make this distinction and stop blessing two secretive, unverifiable loss-making companies with so much power.
(Putting that aside, I suspect — without evidence, mind you - that the endless march to solving models by making them bigger is not the solution anyway.)
This notion that Chinese labs are merely distilling frontier models is quite an unwarranted slur. Those labs have published WAY more useful research than US labs on RL techniques, novel model architectures, training pipelines, etc. They have also hit intelligence-per-parameter densities that US labs have yet to attain.
Apart from that, merely training a model on outputs from another model, off policy and without the logits, doesn’t really work that well.
The Chinese labs know how to build frontier level models. GLM-5.2 shows that they no longer even need Nvidia chips to do it.
I empathize with this but curious what would make any other country a better safehaven for your data? I personally like the EU's approach to data safeguards, but are there other locales/data protections you have in mind that would keep your data "safe".
> Fully open model: open weights + open data + full training details including all data and training recipes
There are equally open, much more useful models out there: https://artificialanalysis.ai/?models=nvidia-nemotron-3-ultr...
My last hope for soverign AI is from Chinese open models
If you want to mix models like this, check out https://github.com/deepbluedynamics/nemesis8