A Blog post by Ai2 on Hugging Face
olmo-eval automates evaluation for iterative LLM development with modular components and per-prompt analysis to separate signal from noise. For teams tuning data/architecture/hyperparameters, it reduces iteration latency and natively supports multi-turn agent evaluation.
AI2 releases olmo-eval, a modular workbench automating benchmark evaluation during LLM development with noise-aware statistical analysis. Teams accelerate iteration by reconfiguring benchmarks and reliably detecting real improvements from random variation.
olmo-eval is an open evaluation workbench that helps model developers add, run, and analyze benchmarks across changing LLM checkpoints, extending OLMES from final-score…