Data Contracts

数据契约 / Data Contracts

中文

当你要用自己的领域数据替换仓库自带 mock 数据时，使用本页。

发布衍生仓库前，请确认 data/ 中不包含私有文档、客户数据、凭据、私有 system prompt、源代码或许可证受限语料。

打包数据结构

data/cpt/source_documents/*.md      # CPT source documents
data/sft/*.jsonl                    # Fact-SFT examples
data/dpo/preference_examples.jsonl  # DPO preference pairs
data/grpo/reward_examples.jsonl     # GRPO reward examples
data/eval/quality_questions.jsonl   # post-training quality evaluation questions

样例领域是虚构的 AsterHelp 内部支持知识库助手，数据刻意保持很小且静态。

CPT 文档

输入：

data/cpt/source_documents/*.md

用途：

• 教给模型领域概念、政策、流程、安全边界和排障知识。
• 用于 corpus discovery、安全预检查和覆盖度感知的数据集构建。

关键配置：

corpus:
  input_paths:
    - "../data/cpt/source_documents"

Fact-SFT 行

输入：

data/sft/*.jsonl

推荐字段：

字段	含义
instruction	用户任务或问题。
output	期望助手回答。
input 或 context	可选上下文，会拼到用户问题后。
category 或 type	可选分类，用于报告分组。

加载器也接受这些等价格式：

• question + answer
• prompt + response
• messages，其中第一个 assistant 消息作为答案，之前的 system/user 消息作为提示。

Fact-SFT 使用 assistant-only loss，因此 prompt token 会被 mask，只训练回答 token。

DPO 行

输入：

data/dpo/preference_examples.jsonl

推荐字段：

字段	含义
prompt	用户提示或任务。
chosen	偏好的回答。
rejected	不偏好的回答。
category 或 type	可选分类，用于报告分组。

加载器也接受这些别名：

标准字段	可接受别名
prompt	instruction, question
chosen	preferred, accept
rejected	bad, reject

规则：

• prompt、chosen、rejected 都必须非空。
• chosen 必须不同于 rejected。

GRPO 行

输入：

data/grpo/reward_examples.jsonl

基础要求：

• 必须能构造出 prompt。
• 至少存在一个奖励信号字段。

Prompt 可以来自：

• messages
• prompt
• instruction
• question

可选 context 或 input 会被拼入 prompt。include_system_prompt: false 可以让样本不附加配置中的 system prompt。

奖励信号字段：

字段	含义
reference_answer	参考答案，用于 overlap 类内置奖励，也会作为 judge 上下文。
answer, solution, ground_truth, expected	reference_answer 的可接受别名。
required_terms	生成结果应包含的术语。
must_include, keywords	required_terms 的可接受别名。
forbidden_terms	生成结果应避免的术语。
must_not_include, banned_terms	forbidden_terms 的可接受别名。
must_refuse	正确行为是否应该拒答。
requires_refusal	must_refuse 的可接受别名。
min_completion_chars	可选的最短回答字符数。
max_completion_chars	可选的最长回答字符数。
category, type, task	可选分组标签，用于报告和 judge 上下文。

这些字段用于内置规则奖励，也会作为外部 reward_judge 的评判上下文。模型型评分不读取本地模型路径或 Hub ID；需要通过 GRPO 与 Reward Judge 配置 HTTP judge。

质量评估行

输入：

data/eval/quality_questions.jsonl

字段：

字段	含义
category	必须是 domain_knowledge、safety_boundary 或 base_regression。
question	训练后质量评估使用的提示，必须非空。

质量评估不是训练验证集。它在训练或合并后运行，用来检查输出行为。

发布前检查

发布衍生仓库前：

1. 确认所有数据都可公开、授权明确且适合分发。
2. 移除私有路径、客户名称、内部 URL、密钥和专有 prompt。
3. 检查 configs/domain_post_training.yaml 中是否残留私有默认值。
4. 不要提交 outputs/、models/、.venv/、__pycache__/ 和训练产物。

---

English

Use this page when replacing the packaged mock data with your own domain data.

Before publishing a derivative repository, confirm that data/ does not contain private documents, customer data, credentials, private system prompts, source code, or license-restricted corpora.

Packaged Data Layout

data/cpt/source_documents/*.md      # CPT source documents
data/sft/*.jsonl                    # Fact-SFT examples
data/dpo/preference_examples.jsonl  # DPO preference pairs
data/grpo/reward_examples.jsonl     # GRPO reward examples
data/eval/quality_questions.jsonl   # post-training quality evaluation questions

The sample domain is AsterHelp, a fictional internal support knowledge-base assistant. It is deliberately small and static.

CPT Documents

Input:

data/cpt/source_documents/*.md

Purpose:

• Teach domain concepts, policies, procedures, safety boundaries, and troubleshooting knowledge.
• Feed corpus discovery, safety preflight, and coverage-aware dataset construction.

Important config:

corpus:
  input_paths:
    - "../data/cpt/source_documents"

Fact-SFT Rows

Input:

data/sft/*.jsonl

Recommended fields:

Field	Meaning
instruction	User task or question.
output	Expected assistant answer.
input or context	Optional context appended to the user prompt.
category or type	Optional category for reports.

The loader also accepts:

• question + answer
• prompt + response
• messages, where the first assistant message becomes the answer and earlier system/user messages become the prompt.

Fact-SFT uses assistant-only loss, so prompt tokens are masked and only answer tokens train the model.

DPO Rows

Input:

data/dpo/preference_examples.jsonl

Recommended fields:

Field	Meaning
prompt	User prompt or task.
chosen	Preferred answer.
rejected	Less preferred answer.
category or type	Optional category for reports.

Accepted aliases:

Standard field	Accepted aliases
prompt	instruction, question
chosen	preferred, accept
rejected	bad, reject

Rules:

• prompt, chosen, and rejected must be non-empty.
• chosen must differ from rejected.

GRPO Rows

Input:

data/grpo/reward_examples.jsonl

Required baseline:

• A prompt must be constructible.
• At least one reward signal must be present.

Prompt can come from:

• messages
• prompt
• instruction
• question

Optional context or input is appended to the prompt. include_system_prompt: false prevents the configured system prompt from being added to that example.

Reward signal fields:

Field	Meaning
reference_answer	Reference text for overlap-style scoring and judge context.
answer, solution, ground_truth, expected	Accepted aliases for reference_answer.
required_terms	Terms the completion should include.
must_include, keywords	Accepted aliases for required_terms.
forbidden_terms	Terms the completion should avoid.
must_not_include, banned_terms	Accepted aliases for forbidden_terms.
must_refuse	Whether the correct behavior is refusal.
requires_refusal	Accepted alias for must_refuse.
min_completion_chars	Optional lower length bound.
max_completion_chars	Optional upper length bound.
category, type, task	Optional grouping label for reports and judge context.

These fields drive built-in rule rewards and provide context for the external reward_judge. Model-based scoring does not load local model paths or Hub IDs; configure an HTTP judge through GRPO And Reward Judge.

Quality Evaluation Rows

Input:

data/eval/quality_questions.jsonl

Fields:

Field	Meaning
category	Must be domain_knowledge, safety_boundary, or base_regression.
question	Non-empty prompt used for post-training quality evaluation.

Quality evaluation is not a training validation set. It runs after training or merge to inspect output behavior.

Publication Checklist

Before publishing a derivative repository:

1. Confirm all data is public, licensed, and safe to distribute.
2. Remove private paths, customer names, internal URLs, secrets, and proprietary prompts.
3. Check configs/domain_post_training.yaml for private defaults.
4. Keep outputs/, models/, .venv/, __pycache__/, and training artifacts out of commits.