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Evaluate a language model with lm-evaluation-harness: validate, smoke test, run and keep samples
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What this guide covers
For people who want to score a language model on a named benchmark from the command line and be able to audit the result. You end with a scored run and saved per-example inputs and outputs. Based on the project's interface documentation and not run.
More about this guide
Guide ID: art_01M6TX698DX3HMKM77XQP8WJA6
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Guide title
Evaluate a language model with lm-evaluation-harness: validate, smoke test, run and keep samples
Coverage
For people who want to score a language model on a named benchmark from the command line and be able to audit the result. You end with a scored run and saved per-example inputs and outputs. Based on the project's interface documentation and not run.
Example
Hypothetical, not run: a 20-example smoke test and then a full hellaswag evaluation of gpt2, with samples saved for inspection.
Useful starting points
- Never report a score from a run that used the example limit; the documentation marks it as for testing only.
- Open a few saved samples and read the prompt and model output yourself; a wrongly formatted prompt makes the score meaningless.
- Only enable the unsafe-code or remote-code switches for task sources you trust.
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