Agent Skillsvllm-project/vime › add-eval-dataset-config

add-eval-dataset-config

GitHub

指导在vime中配置评估数据集,支持结构化YAML或旧版CLI参数。涵盖字段构建、优先级解析及验证,适用于添加周期性评估、迁移配置或自定义每数据集覆盖参数等场景。

.claude/skills/add-eval-dataset-config/SKILL.md vllm-project/vime

Trigger Scenarios

用户要求添加用于周期性评估的数据集 用户询问从--eval-prompt-data迁移到--eval-config 用户需要为特定数据集设置评估覆盖参数(如采样率、温度等)

Install

npx skills add vllm-project/vime --skill add-eval-dataset-config -g -y
More Options

Non-standard path

npx skills add https://github.com/vllm-project/vime/tree/main/.claude/skills/add-eval-dataset-config -g -y

Use without installing

npx skills use vllm-project/vime@add-eval-dataset-config

指定 Agent (Claude Code)

npx skills add vllm-project/vime --skill add-eval-dataset-config -a claude-code -g -y

安装 repo 全部 skill

npx skills add vllm-project/vime --all -g -y

预览 repo 内 skill

npx skills add vllm-project/vime --list

SKILL.md

Frontmatter
{
    "name": "add-eval-dataset-config",
    "description": "Guide for adding and validating evaluation dataset configuration in vime. Use when user wants to configure eval datasets via --eval-config or --eval-prompt-data, add per-dataset overrides, or customize evaluation rollout behavior."
}

Add Eval Dataset Config

Configure evaluation datasets in vime with explicit dataset-level overrides and predictable runtime behavior.

When to Use

Use this skill when:

  • User asks to add evaluation datasets for periodic eval
  • User asks to migrate from --eval-prompt-data to structured --eval-config
  • User asks for per-dataset eval overrides (sampling params, keys, rm_type, metadata)

Step-by-Step Guide

Step 1: Choose Config Entry Method

Supported inputs:

  • Structured config file: --eval-config <yaml>
  • Legacy CLI pairs: --eval-prompt-data <name1> <path1> <name2> <path2> ...

If --eval-interval is set, eval datasets must be configured.

Step 2: Build YAML with Required Fields

Each dataset needs at least:

  • name
  • path

Example:

eval:
  defaults:
    n_samples_per_eval_prompt: 1
    temperature: 0.7
    top_p: 1.0
  datasets:
    - name: aime
      path: /path/to/aime.jsonl
      rm_type: math
      input_key: prompt
      label_key: answer
      metadata_overrides:
        split: test

Step 3: Understand Override Priority

vime/utils/eval_config.py resolves fields in this order:

  1. Dataset-level values in eval.datasets[*]
  2. eval.defaults
  3. CLI args fallback (for example eval_* or rollout_* fields)

Common overridable fields include:

  • Runtime: n_samples_per_eval_prompt, temperature, top_p, top_k, max_response_len
  • Sample keys: input_key, label_key, tool_key, metadata_key
  • Extra: rm_type, custom_generate_function_path, metadata_overrides

Step 4: Wire Eval Function if Needed

By default, eval uses --eval-function-path (defaults to rollout function path). Use a separate eval function when inference/eval behavior must differ from training rollout.

Step 5: Validate Parsing and Runtime

  • Start with config parsing sanity by running a short launch command.
  • Confirm dataset entries are loaded into args.eval_datasets.
  • Verify output keys match eval logging/metrics expectations.

Common Mistakes

  • Missing name in dataset entries
  • Odd-length --eval-prompt-data pairs
  • Setting --eval-interval without any eval dataset
  • Mixing reward dict outputs without eval_reward_key configuration

Reference Locations

  • Eval config model: vime/utils/eval_config.py
  • Eval config resolution: vime/utils/arguments.py
  • Eval rollout path: vime/rollout/vllm_rollout.py
  • Customization docs: docs/en/get_started/customization.md

Version History

  • 8d1f4cc Current 2026-07-05 14:58

Same Skill Collection

.claude/skills/add-dynamic-filter/SKILL.md
.claude/skills/add-reward-function/SKILL.md
.claude/skills/add-rollout-function/SKILL.md
.claude/skills/add-tests-and-ci/SKILL.md
.claude/skills/vime-code-review-preferences/SKILL.md

Metadata

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Version
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Hash
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Indexed
2026-07-05 14:58

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