MetaMathQA — LLM Reasoning Dataset
395K high-quality mathematical question-answer pairs created by augmenting GSM8K and MATH through answer rewriting, backward reasoning, and FOBAR methods. Powers MetaMath models that significantly outperform base models on math benchmarks.
Dataset Details
| Provider | meta-math |
| Category | Reasoning |
| Size | 395K Pairs |
| License | MIT |
| Downloads | 1.2M |
| Tags | Math, Reasoning, GSM8K, MATH, Augmented |
from datasets import load_dataset
ds = load_dataset("meta-math/MetaMathQA")
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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Frequently asked questions
Can I use MetaMathQA commercially?
Yes — MetaMathQA is released under MIT, 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 MetaMathQA contain, and do I need all of it?
MetaMathQA contains 395K Pairs. 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 MetaMathQA best used for?
Boosting GSM8K/MATH-style math skills in 7B models. It belongs to the Reasoning section of our dataset hub, where you'll find alternatives and complementary sets.
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