smevals is small on the surface, but it points at a practical model-ops problem: small models need their own evaluation harness.
The repository describes a framework for running evals against small and large models. That framing matters because small-model adoption is usually blocked less by enthusiasm and more by proof: which tasks are good enough, which fail, and where a cheap model can replace a default frontier model without quietly degrading the workflow.
For agent builders, the useful artifact is the harness. If a team can capture representative tasks, replay them across model classes, and compare results over time, model routing becomes an engineering decision instead of a taste argument.