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EternalBlue edited this page Jun 28, 2026 · 4 revisions

DomainPostTrain Wiki

中文

DomainPostTrain 是一个面向领域大模型后训练的可复现流水线。它把领域文档、事实 SFT 样本、偏好数据和 GRPO 奖励提示组织成 LoRA/QLoRA 训练链路，并产出 adapter、合并模型、质量评估报告、本地推理服务和导出产物。

本 Wiki 默认先给出中文说明，再提供英文说明，方便不同读者查阅。

主仓库里的 .wiki/ 是 GitHub Wiki 的源目录。页面对读者生效前，需要把这些 Markdown 文件复制并推送到独立的 OWNER/REPO.wiki.git 仓库。操作见 发布 Wiki。

从这里开始

常见任务入口

我想要... 阅读
用最短路径验证本地链路 快速开始
检查 CUDA、PyTorch、TRL 和 GPU 状态 操作手册
下载或指定基础模型 操作手册
安装 CUDA、PyTorch 或 ONNX 依赖 安装
把 mock 数据替换成自己的领域数据 数据契约
调整 batch size、LoRA rank、验证集或输出目录 配置
启用 DPO 或 GRPO 训练流水线
接入本地、DeepSeek 或 GLM 奖励 judge GRPO 与 Reward Judge
查看失败报告和恢复运行 操作手册
导出 GGUF 或 ONNX 推理与导出
把 .wiki 发布到 GitHub Wiki 发布 Wiki

默认训练链路

CPT -> Fact-SFT -> optional DPO -> optional GRPO -> merge -> quality eval -> inference/export

仓库自带的 AsterHelp 是虚构领域的静态 mock 数据。训练真实模型或发布衍生项目之前，请替换 CPT 文档、SFT 样本、DPO 偏好样本、GRPO 奖励样本、质量评估问题和 system prompt，并确认数据来源、授权和安全边界。

主仓库文档边界

Wiki 是任务型说明。主仓库仍保留更紧凑的项目级文档：

  • README.md：项目概览和核心流程。
  • configs/README.md：完整双语配置参考。
  • data/README.md：打包 mock 数据说明和发布提醒。
  • scripts/README.md：脚本分组。
  • CONTRIBUTING.md：贡献检查。
  • SECURITY.md：安全报告和数据安全边界。

运行假设

  • 默认依赖面向 CUDA 12.6 GPU 训练。
  • 训练前建议运行 python scripts/diagnostics/check_training_environment.py。
  • ONNX 导出是可选能力，依赖在 requirements-onnx.txt。
  • GRPO 模型评分通过 grpo.reward_judge 调用 OpenAI-compatible chat completions API。
  • 本 Wiki 的源文件维护在 .wiki/，需要同步到独立的 GitHub Wiki 仓库后才会对读者生效。

English

DomainPostTrain is a reproducible LLM post-training pipeline for turning domain documents, factual SFT examples, preference data, and GRPO reward prompts into LoRA/QLoRA adapters, a merged model, quality evaluation reports, local inference services, and export artifacts.

This Wiki is Chinese-first by default. Each page also includes an English section for readers who prefer English.

.wiki/ in the main repository is the source directory for GitHub Wiki pages. To make changes visible to readers, copy these Markdown files to the separate OWNER/REPO.wiki.git repository and push them. See Publishing.

Start Here

Common Tasks

I want to... Read this
Run the shortest local verification Quick Start
Check CUDA, PyTorch, TRL, and GPU state Operations Runbook
Download or point to a base model Operations Runbook
Install CUDA, PyTorch, or ONNX dependencies Installation
Replace the mock domain with my own data Data Contracts
Tune batch size, LoRA rank, validation, or output paths Configuration
Enable DPO or GRPO Training Pipeline
Use a local, DeepSeek, or GLM reward judge GRPO And Reward Judge
Inspect failure reports and resume work Operations Runbook
Export GGUF or ONNX Inference And Export
Publish .wiki to GitHub Wiki Publishing

Default Pipeline

CPT -> Fact-SFT -> optional DPO -> optional GRPO -> merge -> quality eval -> inference/export

The repository ships static mock data for the fictional AsterHelp domain. Before training or publishing a derivative project, replace the corpus, SFT rows, preference rows, reward prompts, quality evaluation questions, and system prompts with data you are licensed to use.

Primary Repository Docs

The Wiki is task-oriented. The main repository keeps compact project-level references:

  • README.md: project overview and core workflow.
  • configs/README.md: full bilingual configuration reference.
  • data/README.md: packaged mock data summary and publication warning.
  • scripts/README.md: script grouping.
  • CONTRIBUTING.md: contribution checks.
  • SECURITY.md: security reporting and data safety boundary.

Runtime Assumptions

  • Default dependencies target CUDA 12.6 GPU training.
  • Before training, run python scripts/diagnostics/check_training_environment.py.
  • ONNX export is optional and uses requirements-onnx.txt.
  • GRPO reward-model scoring uses grpo.reward_judge to call an OpenAI-compatible chat completions API.
  • GitHub Wiki content is maintained in .wiki/ and must be copied to the separate OWNER/REPO.wiki.git repository to go live.

开始 Start

使用 Use

支持 Support

项目 Project

Clone this wiki locally