The fully open post-training corpus behind SmolLM3-3B, organized into three subsets matching the model's training phases: mid-training (4.8M rows), SFT (a decontaminated mixture of ~24 datasets including OpenThoughts, Tulu 3, OpenHermes, and multilingual data), and preference (447k rows for APO). The successor to SmolTalk — a complete, reproducible recipe for modern small-model post-training including dual reasoning/no-reasoning modes.
Smoltalk — General SFT for small models (the SmolLM2 recipe)
Tulu 3 SFT Mix — Reproducing a state-of-the-art fully open post-training recipe
Python-Edu — Continued pretraining for Python code understanding
OpenHermes 2.5 — The default general-purpose SFT mix for 7B-13B fine-tunes
Frequently asked questions
Can I use SmolTalk 2 commercially?
Yes — SmolTalk 2 is released under Apache 2.0, a permissive license that allows commercial use, including training models you ship in a product. Check the dataset card for attribution requirements before release.
How much data does SmolTalk 2 contain, and do I need all of it?
SmolTalk 2 contains 3 Subsets (Mid 4.8M Rows). You rarely need all of it: for style and format fine-tuning, a few hundred to a few thousand examples are enough — load a slice (e.g. split="train[:1000]") and scale up only if quality plateaus.
What is SmolTalk 2 best used for?
Reproducing a complete modern post-training pipeline (mid-training → SFT → preference) for small models. It belongs to the Instruction / SFT section of our dataset hub, where you'll find alternatives and complementary sets.