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evaluation-reproduction

evaluation-reproduction

Route Qwen benchmark reproduction and evaluation-script selection for C-Eval, MMLU, CMMLU, GSM8K, HumanEval, and plugin workflows.

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Documentation de la Skill

GitHub

name: evaluation-reproduction description: "Route Qwen benchmark reproduction and evaluation-script selection for C-Eval, MMLU, CMMLU, GSM8K, HumanEval, and plugin workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Qwen Evaluation and Reproduction

Use this sub-skill when the user wants to reproduce Qwen benchmarks, select an evaluation script, prepare data directories, or understand the differences between base and chat evaluation paths.

Safe start

  • Use scripts/qwen_eval_command_builder.py to print a benchmark command plan without downloading datasets or loading a model.
  • Treat HumanEval execution as sandbox-sensitive. Do not run untrusted generated code without explicit approval.
  • Do not assume the chat evaluation scripts reproduce the same protocol as external benchmark harnesses; the repository documentation explicitly notes 0-shot chat results in some places where 5-shot results came from another system.

Routes

User requestRead
C-Eval, MMLU, CMMLU, GSM8K, HumanEval, or plugin benchmark overview and file layoutreferences/benchmark-reproduction.md
Script flags, model/checkpoint selection, dataset arguments, and base-vs-chat command differencesreferences/evaluation-script-reference.md
Missing dataset, wrong result path, sandbox concern, or benchmark dependency issuereferences/troubleshooting.md

Boundaries

  • For inference of the underlying checkpoint, use ../inference-model-loading/SKILL.md.
  • For prompt/tool-use behavior, use ../prompting-tool-use-tokenization/SKILL.md.
  • For serving or deployment, use ../serving-deployment/SKILL.md.
  • For finetuning or quantization, use ../finetuning-quantization/SKILL.md.

Operating rules

  • Benchmark commands are only useful when the dataset layout, checkpoint family, and seed are explicit.
  • Never present a score without the benchmark dataset and result file locations the repo expects.
  • Keep the repository's warning about HumanEval untrusted code execution visible.
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