- Just realized that there are basically no American open models right now ever since the Llama series was abandoned. Basically Gemma and GPT-OSS I guess?
Ah but Mira Murati's new Inkling is Apache 2.0
But it makes sense that if you're a university researcher you are thinking about what's a model that will be open weight and developed over the long term and doesn't raise 'Chyna' concerns in Washington DC
- There's a bunch of American open models. Inkling, Nemotron, Trinity come to mind, but I'm sure there's others.
- Laguna S 2.1 is really great too, in the "preview" release they've done so far at least. Still pending some reasoning-looping, but besides that, it's a really strong model to run within 96GB VRAM with the NVFP4 variants, and it's really good at coding (specifically).
- No it doesn't follow instructions and is substantially slower than ds4.
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- Just looked into some Nemotron stats
Looks like on <https://arena.ai> agent arena (grouped by lab) Nvidia is 15/15 (much worse than Thinky and Mistral) and on text arena it's 18/27
On <https://openrouter.ai/models?order=most-popular> I definitely see usage though (probably mostly cause Nemotron 3 Ultra is free) the grouped order is DeepSeek, Tencent, Xiaomi, OpenAI, Z.ai, Nvidia
- I think glancing at a random snapshot from today misses all the context. Nemotron 3 is far more significant than you're giving it credit for.
At this point, Nemotron 3 is really an 8 month old model series. That's when Nemotron 3 Nano was released, and the Nemotron 3 Super/Ultra models this year are obviously based on that recipe, mostly just bigger with a few tweaks here and there. Against today's models, no, not that interesting. Each of the Nemotron 3 models were briefly competitive when they launched, but never exceptional, and less competitive with each scale up. The fact that it took so long for Nemotron 3 Ultra to launch really hampered its competitiveness.
The Nemotron 3 series is extremely open about training recipes and training data, far more open than most open weight models, and that is valuable.
Before Nemotron 3, Nvidia had never released a single LLM that I would consider interesting at all, so Nemotron 3 was a big step up. The closest thing was Mistral NeMo, but a significant part of the credit there goes to the Mistral team, not Nvidia.
Given how much Nemotron 3 improved, I'm curious to see if Nemotron 4 will take them to a leading edge level instead of just briefly competitive.
(Nvidia released a Nemotron 3 and a Nemotron 4 like 3 years ago... this year's Nemotron 3 is entirely unrelated. Nvidia's naming schemes leave a little bit to be desired.)
- LiquidAI (LFM) and Poolside (Laguna) as well. All of them are like 3-6 months behind Chinese models, though only Nvidia and Thinking Machines seem to have the compute to train large models.
- Don't forget IBM
- Also Nemotron and Arcee.
- Laguna S 2.1 is another fairly impressive-for-the-size American open model
- review of AllenAI Olmo research team and commitment to OSS -- AI2 complete transparency including training data, code, intermediate checkpoints, and detailed logs for reproducibility and scientific rigor.
- Do all these models have any significant architectural differences or training data sources? What are the factors going into the diversity of their performance?
- This is refreshing considering all the FUD (mostly from 1 frontier lab) happening around Open weight models.
- What would the selected participants get from this? Looks like there is no offer of funding?
- I wonder why it took so long.
- Mostly because it's generally a bad idea for government to try to compete with a brand new tech industry with hundreds of billions in private capital developing commercial models. If the American private industry does actually wash out vs Chinese open models there might be talent available for them to put money into, so maybe they are just preparing for that scenario in the meantime.
- stewards of the nuclear weapons biz. they'll do great here.
- “Gomi” is the Japanese word for garbage. Gotta wonder if someone has a sense of humor…
- The Australian Liberal Party (basically our version of conservative republicans) proposed the National Energy Guarantee policy in 2017, which inevitably failed due to the media and public’s relative literacy and tendency to turn policy names into acronyms.
- I couldn't find any details about size or training data for the model.
- It looks like they're taking applications for training data (due August 14th), so I think it's safe to say this is just an announcement of intent and a call for involvement vs. something that is readily available. Seems almost quaint in comparison to the strategy of sucking up every piece of data you can find anywhere on the Internet and feeding it to your LLM but I suspect their intent is to be more careful in what they train their model on.
- Pretty cool I’ll take it. Thanks!
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- what a valuable contribution
- You and me both.
- Sounds like a job for the U.S. Department of Shitposting
- It is. Depending on who Trump has fired or put in charge of a department it can be another shell that pumps out low quality crap. It might be the most valuable contribution on this thread.
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- Genesis is skynet
- Modeling with my life as data.
76 points by moelf 2 hours ago | 32 comments