The thing to note here, besides the transparency and the fact that it’s actually a good model that also works well on coding and agentic tasks, is that it’s the first release by a team formed less than a year ago, with a strong focus on iteration velocity. There’s more to come.
disclaimer: I‘m part of the training team, happy to answer any questions
- Is it possible to train only on math and logic materials and expect the model's response in math questions to be superior to general models with the same training/inference compute hardware?
- Are there non-LLM approaches to the above task with the goal of achieving a non-hallucinatory agent?
Any future plans you can share to further evolve / train models in this size class?
It seems like a very suitable size for local AI models on reasonably high end consumer devices, given it's low active parameter count and a mixed 8bit/4bit quant would fit easily inside 64GB of memory.
I can't comment on concrete sizes of future models, partly because it's not decided yet. But: Pre-training for Kolibri only started in August so there's a high chance continued post-training - which we plan to do - will yield some nice checkpoints. We believe this size and sparsity allows achieving great inference throughput at reasonable levels of intelligence.
peterBlue75 · · focus · HN ↗
disclaimer: I‘m part of the training team, happy to answer any questions
ducktective · · focus · HN ↗
- Are there non-LLM approaches to the above task with the goal of achieving a non-hallucinatory agent?
sspiff · · focus · HN ↗
It seems like a very suitable size for local AI models on reasonably high end consumer devices, given it's low active parameter count and a mixed 8bit/4bit quant would fit easily inside 64GB of memory.
peterBlue75 · · focus · HN ↗