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Run LiveBench evaluations against every model configured in the DeepSeek Harness — pick provider/model, category, task, release and question range from dropdowns, watch progress, and","maintainers":[{"name":"vithrive","email":"cszr2018@gmail.com"}],"readme":"# dsh-livebench-panel\n\n[![npm version](https://img.shields.io/npm/v/dsh-livebench-panel.svg)](https://www.npmjs.com/package/dsh-livebench-panel)\n[![npm downloads](https://img.shields.io/npm/dm/dsh-livebench-panel.svg)](https://www.npmjs.com/package/dsh-livebench-panel)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)\n\nDSH web 插件：在 **Trajectory 视图（轨迹视图）** 的「对话 / 轨迹」标签右侧新增一个 **LiveBench** 标签页。点进去即可用下拉框选择参数，直接对本机 LiveBench 发起评测并查看成绩。\n\n> 💬 使用问题、配置分享、成绩对比 → [Discussions](https://github.com/Vithrive/dsh-livebench-panel/discussions)；\n> 缺陷与功能建议 → [Issues](https://github.com/Vithrive/dsh-livebench-panel/issues)。\n> 遇到报错请先看 [LiveBench 本地补丁说明](docs/livebench-patches.md)，多数问题源于漏打补丁。\n\n## ⚠️ 依赖要求（必读）\n\n本插件只是**控制台**，真正的评测由 [LiveBench](https://github.com/LiveBench/LiveBench) 项目在本机完成。仅安装插件时，面板底部会显示同样的安装指引。完整安装步骤（Windows，约 10–20 分钟）：\n\n```bat\n:: 1) 克隆 LiveBench（放到默认路径可免配置；装在其它目录请设环境变量 DSH_LIVEBENCH_HOME 指向它）\ngit clone https://github.com/LiveBench/LiveBench V:\\PythonProject\\C_UtilizeSpace\\LiveBench\n\n:: 2) 用 Python 3.11 建虚拟环境并安装（3.10 会因 litellm 报 NotRequired 错误）\ncd /d V:\\PythonProject\\C_UtilizeSpace\\LiveBench\npy -3.11 -m venv .venv\n.venv\\Scripts\\python -m pip install -e .\n\n:: 3) 仅评测 coding 类需要：安装评分依赖（较大，含 TensorFlow）\n.venv\\Scripts\\python -m pip install -r livebench\\code_runner\\requirements_eval.txt\n\n:: 4) 下载题目数据（约 250MB）\ncd livebench\n..\\.venv\\Scripts\\python download_questions.py\n\n:: 5) 重启 dsh web，回到面板点「刷新」\n```\n\n装在其它目录时：设置**系统环境变量** `DSH_LIVEBENCH_HOME=<你的LiveBench目录>`，重启 dsh web 生效。\n\n## 面板选项说明\n\n| 选项 | 含义 | 选择建议 |\n|---|---|---|\n| 模型 | harness 全部 provider 的全部模型（含内置 deepseek-official）；分组展示 | 首次验证选小任务 + 便宜模型 |\n| 推理强度 | 该模型的 reasoning effort（无配置则禁用）；编码进条目名，不同强度独立出分 | 日常 `medium`/`high`；对比测试固定同一档 |\n| 题集 release | LiveBench 题目发布批次 | 公开题目最全的是 `2024-11-25`（推荐） |\n| **Baseline 题库** | 参考模型的实测对错题集，**可脱离分类/任务单独使用**（见下节） | 快速探针：参考模型「做错」的题 |\n| 分类 | 六大类：coding / math / reasoning / language / data_analysis / instruction_following | 首次验证选 `language` |\n| 任务 | 分类下的具体任务（如 language/typos 拼写纠错） | 首次验证选 `typos`（短平快） |\n| 题目序号范围 | 起止下标（0 起，**含首尾**）；选 Baseline 时序号相对该题库 | 冒烟测试填 0–1（只跑 2 题） |\n| max-tokens | 单次回答的 token 上限（默认 65536，上限 200000） | 推理模型别低于 32768，否则思考吃满预算、正文为空，这题会被记成「没做出来」 |\n\n## Baseline 题库（探针）\n\n用**一个参考模型的实测对错**当尺子，快速判断别的模型处在哪个档位。位于「分类 / 任务」之上，\n三层可选：\n\n1. **参考模型**：`Baseline(glm-5.3-flash@max)`\n2. **任务**：该参考模型已经跑过全量的任务（如 `olympiad（36）`）\n3. **做错 / 做对**：各自标注题目数量（如 `做错（8）` / `做对（28）`），可多选\n\n选中后**不需要也不应该再选分类/任务**——\"不选 = 全部分类\"这条规则对 Baseline 不适用。\n「题目序号范围」仍然生效，序号相对 Baseline 题库（0 起、含首尾）；超出上界不会报错，\n会按实际题数截断。\n\n**为什么默认勾「做错」**：探针的价值在于\"参考模型做不出来的题\"。参考模型能轻松做对的题，\n对更强的模型没有分辨力，混进来只会稀释信号。「做对」也保留，用于反向验证\n（例如确认某模型确实强于参考模型）。\n\n**当前内置的数据**（2026-09-12 实测，release `2024-11-25`）：\n\n| 参考模型 | 任务 | 全量 | 做对 | 做错 |\n|---|---|---|---|---|\n| `zai-coding-cn__glm-5.3-flash@max` | `math/olympiad` | 36 | 28 | 8 |\n\n实测效果（跑「做错」那 8 题）：`aiportx-gg/grok-4.6@xhigh` 明显强于参考模型；\n`deepseek-official/deepseek-flash@max` 明显更弱。失败的 8 题全部属于 olympiad 的\n**公式还原**题型——把解答里的公式挖空让模型补全，是纯符号推理，很吃\"智力\"。\n\n后续任务按同样方式补进 `lib/index.js` 的 `BASELINE_SETS.tasks` 即可：\n只需填 `passed` / `failed` 两组 question id（取自 LiveBench 的 `question.jsonl`），\n面板用 `--question-id` 直接指定，题目在题库里是否连续都无所谓。\n\n## 功能\n\n- **标签页位置**：`conversation.view` 槽位 `id: \"livebench\"`、`order: 20`（对话 = 0，轨迹 = 10，LiveBench 排最右）。\n- **模型下拉框**：与 harness 会话窗口的模型选择**同源**，展示**全部 provider / 全部模型**。清单取自\n  ①运行中的 `llm` 服务注册的路由与模型（权威来源），叠加 ②生效的 provider 配置（`llm-pi-ai.providers`\n  与内置 `llm-deepseek` 节），配置来源按 `settings` 服务（运行时生效值）→ `<profile>/cordis.patch.yml`\n  → `$DSH_HOME/cordis.patch.yml` → 旧版 `$DSH_HOME/settings.yaml`（升级后改名 `settings.yaml.imported`）\n  依次回退 —— dsh 0.4 起 `settings.yaml` 已被拆成分层 patch，只认那一个文件会让下拉框只剩内置\n  `deepseek-official` 兜底模型。内置 `deepseek-official` 路由（`@deepseek-ai/dsh-llm-deepseek`）始终合并进来；\n  `openai-completions`/`openai-responses`/`anthropic-messages` 且能解析出 `baseURL` 的 provider 标记为可直连\n  LiveBench（`--api-base` 路由），其余标「·未接LiveBench」。\n  `/config` 响应里的 `providerSource` 会写明本次清单读自哪些来源（诊断用）。\n- **推理强度下拉框**（模型右侧）：来自模型配置的 `reasoningEfforts` 映射（如 gpt-5.6 系 off/low/medium/high/xhigh/max，DeepSeek off/low/high/max）。选定后：\n  - 插件向 `livebench/model/model_configs/dsh_panel_generated__<display-name>.yaml` 写入一条模型配置（每个模型一个文件，避免并发写同名文件互相覆盖），经 LiveBench 的 `api_kwargs.default.reasoning_effort` 透传给 API（`off` 表示不透传、由后端走默认）；\n  - 强度编码进 display-name（如 `code-gpt__gpt-5.6-sol@high`），**不同强度在成绩表中是独立条目**，可直接对比。\n- **参数下拉框**：题集 release（LiveBench 全部 releases）、分类（coding/math/reasoning/language/data_analysis/instruction_following）、任务（随分类联动）、题目序号范围、max-tokens（**默认 32000**，自动夹到模型声明的上限；LiveBench 自带默认只有 4096，推理模型会思考吃满、正文为空，难题可手动调到 65536）、**并发请求数**（`--parallel-requests`，1/2/3/4/6/8，默认 1=串行；模型单题思考慢时并发多题是唯一能等比缩短总时长的开关）。\n- **协议路由**：**Anthropic/Claude 系模型一律走 `anthropic-messages`**（POST `<baseURL>/v1/messages`；\n  `baseURL` 会自动剥掉 `/v1`、`/v1/messages` 后缀，因为 anthropic SDK 自己会拼）；\n  其余模型按生效配置的 `api` 走 `openai-completions` / `openai-responses` / 内置端点。\n  运行日志首行打印 `[route] …`，写明协议、目标地址与 `max_tokens`。\n- **不替上游做形状决定**（实测结论）：面板**不注入 `thinking`**、**不注入长度上限字段**。\n  中转对这两样都不当真 —— 注入 `thinking` 后 aiportx 只会回空的 thinking 片段（一题 11~22 分钟、\n  正文 0~75 字符、判 0）；`max_tokens` / `max_completion_tokens` 写 512 实测吐 4062 / 4737 token。\n  保持 LiveBench 的原生形状最稳；`--max-tokens` 仍会夹到模型在生效配置里声明的上限。\n- **流式看门狗三层判死，且不会误杀健康请求**：① 连接上任何字节都算活（含每 3 秒的 `ping`），\n  `LIVEBENCH_STREAM_IDLE_TIMEOUT` 默认 600s（可用渠道声明的 `streamIdleTimeoutMs` 覆写）；\n  ② **内容**维度：`LIVEBENCH_CONTENT_IDLE_SECONDS` 默认 480s 没有任何正文/思考片段就断开\n  —— 中转会连发 ping 十几分钟不给一个字，只按字节判活对它无效；\n  ③ 总时长上限 `LIVEBENCH_STREAM_MAX_SECONDS` 默认 900s；首个字节等待上限\n  `LIVEBENCH_FIRST_BYTE_TIMEOUT` 默认 600s。\n- **进度心跳**：模型长时间思考时网关只发 `ping`、正文迟迟不来，日志里会每 30 秒出现一行\n  `仍在接收: 已 120s，连接累计 12345 字节（模型思考中，正文未到）`，长思考不会\"看起来卡死\"\n  （`LIVEBENCH_PROGRESS_SECONDS`，0 = 关闭）。\n- **运行控制**：开始 / 停止 / 刷新；最多 9 个模型并发评测（每个模型同时只跑 1 次）；实时滚动日志（每 2.5s 轮询）；「刷新」会清掉已结束的运行日志。\n- **自动补跑（API 抖动兜底）**：一次评测要连续打几百次 API，中转站 502/限流/超时是常态。\n  跑完后若答案文件里还有 `$ERROR$`，面板会自动用 `--resume --retry-failures`\n  **只重跑失败的那几题**（最多 3 轮，间隔 15s / 60s / 3min），并复用同一个条目名 ——\n  已成功题目的答案与判分都保留，成绩表里仍然只有一行。Baseline 评测同样适用。\n  日志里会打印 `[自动补跑] …`。\n  注意：思考吃满 max-tokens 的空答案（不是 `$ERROR$`）不会自动补跑——那不是抖动，\n  把 max-tokens 调大后手动重跑才有意义。\n- **题目数据集离线读取**：huggingface.co 在国内常常连不通，而已缓存的分类根本不需要联网。\n  面板检测到本次要跑的分类都在 `.hf_cache` 里时，直接以 `HF_HUB_OFFLINE=1` 启动，\n  省掉 5 轮 HEAD 超时重试（每次白等 20~60s）；缺缓存的分类保持联网，不影响新 release。\n- **成绩表**：直接读取 `data/live_bench/**/model_judgment/ground_truth_judgment.jsonl` 计算 模型 × 任务 平均分（分数 = 正确率 ×100，单元格下方括号标注「**本次没做或没做完的题数 / 用户选择的题数**」，即题目序号范围的题数；不参与正确率计算的总题数不展示），无需等 LiveBench 自己出榜。**判分与答案按 `answer_id` 配对**：补跑换过答案的题只认新判分，旧的 0 分不会把成绩拉低；答案文件里同一题出现多行时按最后一行取值（与 LiveBench 自身口径一致）。支持按模型名/时间排序、拖 ⠿ 自定义行序、勾选或单行按钮删除成绩（删除会同时清掉该模型的答案与判分行；只有空壳文件的“幽灵行”不会再被扫描出来，删除结果稳定持久）。\n- **题目序号范围按闭区间处理**：LiveBench 的 `--question-end` 是开区间（`questions[begin:end]`），面板按用户直觉采用闭区间（传参时 `止 + 1`），因此界面上填的题数与实际跑的题数一致。\n\n## 依赖\n\n- 本机 LiveBench 检出：默认 `V:\\PythonProject\\C_UtilizeSpace\\LiveBench`，可用环境变量 `DSH_LIVEBENCH_HOME` 覆盖。\n- 已配置好的评测 venv：`<root>\\.venv\\Scripts\\python.exe`（Python 3.11，`pip install -e .` 完成）。\n- API Key：从 harness provider 的 `apiKeyEnv` 环境变量解析，经 `LIVEBENCH_API_KEY` 传给 LiveBench，**不会**出现在命令行或浏览器响应里。\n\n## API（同源）\n\n| 路由 | 方法 | 说明 |\n|---|---|---|\n| `/dsh-livebench-panel/api/config` | GET | 可用性、releases、分类→任务表、providers+models |\n| `/dsh-livebench-panel/api/start` | POST | 启动一次 `run_livebench.py` |\n| `/dsh-livebench-panel/api/status` | GET | 运行状态 + 日志尾部 |\n| `/dsh-livebench-panel/api/stop` | POST | 终止当前评测 |\n| `/dsh-livebench-panel/api/results` | GET | 汇总成绩行 |\n| `/dsh-livebench-panel/api/delete` | POST | 删除指定 模型×分类×任务 的答案与判分记录（正文 `{rows:[{model,category,task}]}`；正在评测的模型会被跳过并在 `skipped` 中返回） |\n| `/dsh-livebench-panel/api/clear` | POST | 清掉已结束的运行日志/记录 |\n| `/dsh-livebench-panel/api/home` | POST | 保存 LiveBench 检出路径 |\n\n## 安装\n\n### 方式 A：从 npm 安装（推荐）\n\n```bash\ndsh plugin --profile web add dsh-livebench-panel\n```\n\n### 方式 B：从本仓库安装\n\n```bash\ngit clone https://github.com/Vithrive/dsh-livebench-panel.git ~/.dsh/plugins/dsh-livebench-panel\n```\n\n然后在 `~/.dsh/profiles/web/package.json` 中：\n\n1. `dependencies` 加入 `\"dsh-livebench-panel\": \"link:../../plugins/dsh-livebench-panel\"`；\n2. `dsh.profile.bundles` 数组加入 `\"dsh-livebench-panel\"`。\n\n最后在 profile 目录执行 `pnpm install`，重启 `dsh web`，打开任一会话的轨迹视图即可看到 **LiveBench** 标签。\n\n## 开发\n\n提交前跑一遍自检（语法 + 插件声明 + npm 打包内容白名单，与 CI 口径一致）：\n\n```bash\nnode scripts/check.mjs\n```\n\n仓库结构：\n\n```\nlib/index.js            node 半：/dsh-livebench-panel/api/* 路由，spawn run_livebench.py\nlib/client.js           浏览器半：注册 conversation.view 槽位的 LiveBench 标签\ncordis.patch.yml        把插件行插入 profile 的 bundle 层\nscripts/check.mjs       本地自检（语法 / 插件声明 / 打包白名单）\ndocs/livebench-patches.md  依赖的 LiveBench 本地补丁（Windows + 中转站兼容）\nsmoke.mjs               开发期冒烟脚本（不进 npm 包）\n```\n\n## 交流与反馈\n\n- **Bug / 功能建议**：[Issues](https://github.com/Vithrive/dsh-livebench-panel/issues)（有对应模板）\n- **使用问题 / 配置分享 / 成绩对比**：[Discussions](https://github.com/Vithrive/dsh-livebench-panel/discussions)\n- 提交 Issue 时请附：插件版本、系统与 Node/Python 版本、复现用的 release + 任务 + 题号范围 + provider/模型、以及完整日志（记得打码 API Key 和私有域名）。\n\n## License\n\n[MIT](LICENSE) © 2026 cszr (Vithrive)\n","readmeFilename":"README.md"}