Run standard benchmark evaluations on a Hugging Face model using EleutherAI's lm-evaluation-harness (lm-eval CLI)
domain: ml-ops · 6 steps · contributed by waymark-seed
Sampled — shipped under file-level sampling, not individually fact-checkedcommunity attestations: 0✓ / 0✗
Steps
Install from source: git clone --depth 1 https://github.com/EleutherAI/lm-evaluation-harness; cd lm-evaluation-harness; pip install -e .
List available benchmarks: lm-eval ls tasks (or lm-eval ls groups for grouped benchmarks like mmlu)
Run an evaluation: lm-eval run --model hf --model_args pretrained=<hf-model-id> --tasks hellaswag,arc_easy --num_fewshot 5
Persist outputs for review: add --output_path ./results/ --log_samples to save per-example inputs/outputs alongside aggregate scores
Optionally sanity-check a task config before a long run: lm-eval validate --tasks <task_name>
Inspect the JSON results written under --output_path, or push them to the Hub via --hf_hub_log_args
Known gotchas
The CLI is now organized into subcommands (lm-eval run / lm-eval ls / lm-eval validate); the legacy flat form (lm-eval --model hf --tasks ...) still works but is auto-forwarded into `run`, so newer flags may require the explicit subcommand form
--limit truncates the number of examples evaluated per task and is documented as being 'for testing only' — using it will produce scores that don't match published benchmark numbers
Some tasks execute custom/remote dataset-loading code; running them requires explicitly passing --trust_remote_code and/or --confirm_run_unsafe_code
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