lm-eval-ledger Makes Model Benchmark Answers Browsable
π‘Go beyond benchmark averages and inspect exactly where each model succeeds, fails, or wastes time.
β‘ 30-Second TL;DR
What Changed
The web app lets users compare model responses question by question.
Why It Matters
The tool can make benchmark debugging and model selection more transparent by exposing failure patterns hidden behind headline scores. It is especially useful for teams evaluating reasoning quality, latency, and answer reliability together.
What To Do Next
Install lm-eval-ledger, configure two models and one task in bench.yaml, then inspect per-question failures at localhost:8090 before trusting aggregate scores.
Key Points
- β’The web app lets users compare model responses question by question.
- β’Recorded fields include system prompts, generations, extracted answers, ground truth, stop reasons, and character counts.
- β’Benchmark-level views include accuracy, tokens per second, completion time, sample count, and no-answer count.
- β’Pairwise comparisons and always-right or always-wrong questions are supported.
- β’The YAML-driven workflow can run multiple models and tasks with one command.
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