Awesome AI Math Datasets

Awesome AI Math Datasets is a community-maintained index on GitHub, one of the many "awesome" lists that catalogue a field in a single readable page. This one focuses on open datasets for building and testing maths-capable language models, grouping them by purpose: pretraining corpora, problem-and-solution collections, and evaluation benchmarks. Each entry is a short description and a link out to the primary source, so the value is in the curation and the map it draws, not in any data hosted here.

You use it the way you use any awesome-list. Open the repository, read the README, and scan the section that matches what you are building, whether that is a large pretraining corpus, a set of worked problems for retrieval, or a benchmark to measure your system against. Because it lives on GitHub, you can watch or star the repo to catch updates, and the commit history tells you how actively it is maintained, which is worth a glance before you rely on it.

For a RAG project, this is a starting point rather than an ingredient. It is most useful early on, when you are surveying what exists and want to find newer additions that a static directory cannot keep pace with. Once you have picked candidates, you move to their primary sources (many of which, like AutoMathText, AMPS, and MegaMath, are catalogued in this same directory) and evaluate them properly.

The caveats are the ones common to all community lists. Coverage is broad but uneven, links age, and inclusion is not a quality guarantee, so treat each entry as a lead to verify rather than a recommendation. Most importantly, the list itself carries no single licence: every dataset it points to sets its own terms, so check the licence at the source before you build on anything. Think of it as a companion to the maths datasets in this directory rather than a substitute for reading their individual pages.