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lm-evaluation-harness: unified framework for testing generative language models, README overview

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# lm-evaluation-harness: unified framework for testing generative language models, README overview

- id: src_01M6V3Z1ABJQPBYGCF0FRVRMHB
- type: source · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T20 · tags: none
- updated: 2026-11-07T13:42:58.599Z
- url: https://veyrul.com/sources/src_01M6V3Z1ABJQPBYGCF0FRVRMHB

The README says the project provides a unified framework to test generative language models on a large number of different evaluation tasks.

Source: https://github.com/eleutherai/lm-evaluation-harness

What it supplies: The README says the project provides a unified framework to test generative language models on a large number of different evaluation tasks.

Useful for: - Run over 60 standard academic benchmarks against a model loaded through transformers, vLLM or a commercial API.
- Compare results between papers using the same publicly available prompts.
- Add your own prompts and evaluation metrics.

First action: - Clone the repository with a depth of 1 and install it with pip install -e .
- Install a model backend with an extra such as lm_eval[hf], lm_eval[vllm] or lm_eval[api].
- Use lm-eval ls tasks to list tasks and lm-eval run -h for evaluation options; for API models, check a few outputs with --limit 10 first.

Prerequisites and limits: The README says the base package does not include transformers or torch and that model backends must be installed separately. It gives no Python version, and its citation entry names version v0.4.3 while its news items refer to later changes.

- The README is long and mostly an index to the documentation; task details are in linked pages that were not read.
- For closed chat model APIs the README warns that answer extraction and scoring on generative tasks should be checked on sample outputs.
- Results depend on the prompts, backend and settings you choose.

The README states no licence in its text, and no licence file was read here, so check the repository before use. Benchmarks, models and APIs you evaluate have their own terms. The README asks readers to cite the harness.

Written from the README text only. Statements are as the README gives them; nothing was installed or run.
Based on README text only; the documentation pages, the task lists, the licence file and the code were not read. No install or run was made.

Source inspected: https://raw.githubusercontent.com/EleutherAI/lm-evaluation-harness/d6de81643928d653435c431bae19945d41d32520/README.md ; 60912 bytes ; sha256 57c5cf1db6edb20393ca70851de820898bd8d10424a5a7574f9f566a6f8d70bd ; 9 exact pinned excerpts in the draft pin map

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    "The README says the base package does not include transformers or torch and that model backends must be installed separately. It gives no Python version, and its citation entry names version v0.4.3 while its news items refer to later changes.\n\n- The README is long and mostly an index to the documentation; task details are in linked pages that were not read.\n- For closed chat model APIs the README warns that answer extraction and scoring on generative tasks should be checked on sample outputs.\n- Results depend on the prompts, backend and settings you choose.\n\nThe README states no licence in its text, and no licence file was read here, so check the repository before use. Benchmarks, models and APIs you evaluate have their own terms. The README asks readers to cite the harness.\n\nWritten from the README text only. Statements are as the README gives them; nothing was installed or run.\nBased on README text only; the documentation pages, the task lists, the licence file and the code were not read. No install or run was made.",
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Rights:
- documentation: license=unknown hosting=permitted redistribution=permitted modification=permitted training=unknown · Only the team-authored original attributed summary and links are covered. Upstream papers, tutorials, code, models, weights and media retain their separate terms; no source-text/media redistribution right is implied.

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