# Maths for the anneal

The last part of training, when the learning rate comes down (the anneal, or decay phase), is where a model gains the
most from the best data. This folder holds the maths and reasoning text for that phase: 9.8B tokens.

Everything here is kept as plain documents (a problem followed by its worked solution, or a page of maths), not as
chat turns. Chat data stays in tuning, after pretraining.

## Sources

| source | tokens | what it is | licence |
|---|---|---|---|
| MegaMath Q&A (LLM360/MegaMath), `megamath_qa/` | 6.53B | maths questions with step-by-step answers, about 300 tokens each | ODC-By |
| MathCoder2 (Dolmino mix), `mathcoder2/` | 2.97B | maths with reasoning and code | Apache 2.0 |
| TuluMath (Dolmino mix), `tulu_math/` | 0.25B | persona word problems with solutions | ODC-By |
| DolminoSynthMath (Dolmino mix), `dolmino_math_synth/` | 0.03B | basic arithmetic and GSM-style problems | ODC-By |
| CodeSearchNet maths filter (Dolmino mix), `codesearchnet_owmfilter/` | 0.002B | maths-related code | ODC-By |
| GSM8K training set (Dolmino mix), `gsm8k/` | 0.001B | grade-school problems with worked answers | MIT |

Left out: Metamath-owmfilter (CC BY-SA); MegaMath Web-Pro (maths web pages that Nemotron-CC-Math already covers, with no
shared URLs to remove the repeats); TinyGSM and TinyGSM-MIND (problems solved as Python programs: a single
format that costs model capacity and does not carry over to plain-answer benchmarks); and long reasoning traces from
large models (thousands of tokens per answer, too long for a model of this size).

## Format

The same as the text shards: Gemma 4 tokens, 3 bytes each, every document `<bos> text <eos>`, with a `.json` and a
`.counts.npz` next to each shard. One folder per source.