OpenThoughts3-1.2M — LLM Reasoning Dataset
1.2M reasoning traces — 850k math, 250k code, and 100k science questions — with chains of thought generated by QwQ-32B. The result of 1,000+ controlled experiments on reasoning-data curation, and the training set behind OpenThinker3-7B, the state-of-the-art open-data 7B reasoning model (53% AIME 2025). The reference open recipe for distilling reasoning ability.
Dataset Details
| Provider | Open Thoughts |
| Category | Reasoning |
| Size | 1.2M Rows |
| License | Apache 2.0 |
| Downloads | n/a |
| Tags | Reasoning-Traces, Math, Code, Science, Distillation, 2025 |
from datasets import load_dataset
ds = load_dataset("open-thoughts/OpenThoughts3-1.2M")
Fine-tune with this dataset
Estimated VRAM to fine-tune with QLoRA (4-bit base model + LoRA adapters), using conservative defaults:
| 7B QLoRA | ~6GB VRAM |
| 13B QLoRA | ~10GB VRAM |
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- Orca Math Word Problems — Grade-school math word problems for small models
Frequently asked questions
Can I use OpenThoughts3-1.2M commercially?
Yes — OpenThoughts3-1.2M 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 OpenThoughts3-1.2M contain, and do I need all of it?
OpenThoughts3-1.2M contains 1.2M 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 OpenThoughts3-1.2M best used for?
Distilling strong math/code/science reasoning into 7B–32B models. It belongs to the Reasoning section of our dataset hub, where you'll find alternatives and complementary sets.
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