界面已本地化;技能介绍与说明文档按所选语言由 AI 自动翻译,代码、命令、路径和专业术语保留原文,以原作者文档为准。
探索技能
数据与研究 · SKILL.md

configuration-and-data

configuration-and-data

【数据与研究】Build, inspect, validate, and round-trip experiment configs and datasets for all supported H2O LLM Studio problem types.

独立 GitHub 快照 · 自动分类 · 以原作者文档为准

技能说明

GitHub

name: "configuration-and-data" description: "Build, inspect, validate, and round-trip experiment configs and datasets for all supported H2O LLM Studio problem types." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

configuration-and-data

Use this sub-skill when you need to load, inspect, validate, or round-trip H2O LLM Studio experiment configs and dataset files.

Owns

  • YAML experiment configs and nested dataclass sections.
  • Problem type selection and per-problem defaults.
  • Dataset connectors, local CSV / Parquet file rules, and import-time column mapping.
  • Prompt, answer, system, parent-id, and DPO rejection columns.
  • Conversation chains and default seeded datasets.
  • Config checks that happen before training.

Does not own

  • Training execution, CLI launches, or GPU setup. Use training-and-experiments.
  • Model, loss, metric, and plot internals. Use modeling-and-evaluation.
  • GUI navigation or app startup. Use app-and-ui.

Supported problem types

  • Causal language modeling
  • Causal classification modeling
  • Causal regression modeling
  • Sequence-to-sequence modeling
  • DPO modeling

Read first

  • references/configuration-reference.md
  • references/data-formats.md
  • references/problem-types.md
  • references/troubleshooting.md

Skill-owned scripts

  • scripts/inspect_config.py — load a YAML config, report the resolved problem type, and verify a canonical round-trip.
  • scripts/validate_dataset.py — load the config, validate the referenced dataset files, and inspect chain and label rules.

Typical workflow

  1. Confirm the intended problem type.
  2. Load the YAML config and compare it with the expected schema.
  3. Validate the train and validation datasets, including required columns.
  4. Check the config round-trip before handing the experiment off to training.

Cross-links

  • If the task needs training or checkpoint debugging, switch to training-and-experiments.
  • If the task is about loss curves, metrics, or predictions, switch to modeling-and-evaluation.
  • If the task is about import dialogs, dataset pages, or app screens, switch to app-and-ui.
我的收藏