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。
Trigger Scenarios
Install
npx skills add vllm-project/vime --skill add-eval-dataset-config -g -y
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-datato 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:
namepath
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:
- Dataset-level values in
eval.datasets[*] eval.defaults- 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
namein dataset entries - Odd-length
--eval-prompt-datapairs - Setting
--eval-intervalwithout any eval dataset - Mixing reward dict outputs without
eval_reward_keyconfiguration
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


