> We're releasing SmolLM3 with our engineering blueprint. It includes architecture details, exact data mixtures showing how we progressively boost performance across domains in a three-stage pretraining approach, and the methodology for building a hybrid reasoning model. Usually, achieving these results would require months of reverse engineering. Instead, we're providing the full methodology.

Looks like ballpark a million dollars of GPU time if you want to train up one for yourself (4000 gpus/24 days).

Very nice write up that’s generous in sharing their learnings.

This is a solid and positive contribution.

"So it's a small large language model?"

"Oh yes, very small."

"How can it be small and large at the same time?"

"Well, it's small by the standards of a large language model."

"So it's large."

"Oh yes, very large."

"Large compared to what?"

"Small language models."

"And so something like ChatGPT, what would that be exactly? A large large language model?"

"Yes, precisely. An LLLM."

I've been using the smollm base models for my own finetunes just because they're so high quality, it looks like I might be using them to drive local agents/code completion in the near future too.

Their RL algorithm looks interesting. I'm still using OpenAI's algorithm for my stuff, I've been meaning to check on the SoTA since I know my code is pretty outdated (It's crazy how fast that happens with this stuff.)

I hope you continue the 50-100M parameter models.

I think there is a case for models that finish fast on CPUs in solve by llm test cases.