I often wonder why OLMo and Nemotron aren't more popular -- they are gold-standard / "frontier" of a year ago. If we had more support behind these, seeing a true open-source AI system that legitimately challenges OpenAI & Anthropic might not be far away!
I'm not really familiar with either, but I'm more familiar with Olmo. My impression is Nemotron is newer -- why is it less applicable? Is it not totally open like Olmo?
Open-source data coverage: The released datasets cover an estimated 8–10T tokens
(~40–50% of the internal 25T blend). Missing categories include code (~14% of blend),
nemotron-cc-code (~2%), crawl++ (~2%), and academic text (~2%). Users should
supplement with their own data for these categories and adjust train_iters
accordingly.
K2 Think V2 is another fully open model like Olmo, with full datasets released.
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.
It does have a fully open training recipe, just some data missing from its datasets, but if you want a fully open pipeline it's going to be a good place to start, you just need to find some more data to fill in the datasets to get up to the token count with reasonably high quality data.
I often wonder why OLMo and Nemotron aren't more popular -- they are gold-standard / "frontier" of a year ago. If we had more support behind these, seeing a true open-source AI system that legitimately challenges OpenAI & Anthropic might not be far away!