lmms-eval

Why we included this project

Teams that build or fine-tune multimodal models usually hit the same wall: the hardest part is trusting the eval numbers, and this project is built around that problem. Instead of wiring up each benchmark by hand, you point it at a model and it runs vision, language, audio, and video tasks through one pipeline backed by a library of 100+ standard benchmarks, so your checkpoint gets compared against established results in the same format. Reproducibility gets real attention here, with confidence intervals, paired comparisons, and consistent post-processing aimed at the familiar failure where two groups report different scores for the same model. Adaptive batching and video I/O optimizations also keep expensive GPUs busy during large runs, which matters for small teams without dedicated eval infrastructure. For anyone deciding whether a change is a genuine improvement or just noise, having text, image, video, and audio covered by a single tool saves real time.