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desideratum

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Yes my findings and thoughts were pretty much identical. I actually think you can get something reasonable at 1.3B params with the correct training recipe, but definitely not at this compute/token budget.

One thing I found was that the model would pretty much always emit solutions from its training data when asked to solve problems, but it was much better at using Bash commands to explore a codebase, for example.

The Hugging Face folks have a great post on also using CAI for more vibes/character post-training than harmlessness https://huggingface.co/blog/constitutional_ai#oh-honey-lets-...

This is a gross simplification of the process - you would typically use order(s) of magnitude more data and compute, and a substantial amount of online reinforcement learning to elicit emergent tool use capabilities.

Many recent OSS models have great tech reports where you can learn more about these kind of things: Kimi 2.5 https://github.com/MoonshotAI/Kimi-K2.5/blob/master/tech_rep... GLM 5 https://arxiv.org/abs/2602.15763 DeepSeek R1 https://arxiv.org/pdf/2501.12948

This is a great question. You definitely aren't training this to use it, you're training it to understand how things work. It's an educational project, if you're interested in experimenting with things like distributed training techniques in JAX, or preference optimisation, this gives you a minimal and hackable library to build on.

Oh, and if you want to utilize 120Hz on the XDR display, you're going to have to replace your perfectly functioning Mac.

Mac models with M1, M1 Pro, M1 Max, M1 Ultra, M2, and M3 support Studio Display XDR at up to 60Hz. All other Studio Display XDR features are supported.

Hi Stefano. I'm an ML Engineer/Researcher looking for a new role, and very interested in learning more about Epistemic. Am I correctly visualizing something similar to https://www.connectedpapers.com/ as part of your product? I can see incredible potential using ML/NLP to build knowledge graphs from a diverse set of sources of biomedical knowledge, from derisking future research to exposing connections and ideas people haven't even considered! Thanks, Salman

Some truly impressive results. I'll pick my usual point here when a fancy new (generative) model comes out, and I'm sure some of the other commenters have alluded to this. The examples shown are likely from a set of well-defined (read: lots of data, high bias) input classes for the model. What would be really interesting is how the model generalizes to /object concepts/ that have yet to be seen, and which have abstract relationships to the examples it has seen. Another commenter here mentioned "red square on green square" working, but "large cube on small cube", not working. Humans are able to infer and understand such abstract concepts with very few examples, and this is something AI isn't as close to as it might seem.