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--- license: apache-2.0 task_categories: - text-classification language: - aa tags: - biology size_categories: - 1K<n<10K --- Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum. \--- license: apache-2.0 task\_categories:   - text-generation language:   - en tags:   - tool-calling   - function-calling   - agent   - hermes   - reasoning   - sharegpt   - sft size\_categories:   - 10K<n<100K configs:   - config\_name: kimi   data\_files:   - split: train   path: data/kimi/train.parquet   - config\_name: glm-5.1   data\_files:   - split: train   path: data/glm-5.1/train.parquet \--- \# Hermes Agent Reasoning Traces Multi-turn tool-calling trajectories for training AI agents using the \[Hermes Agent](https://github.com/nousresearch/hermes-agent) harness. Each sample is a real agent conversation with step-by-step reasoning (`<think>` blocks) and actual tool execution results. This dataset has two configs, one per source model: | Config | Model | Samples | |--------|-------|---------| | \*\*kimi\*\* | Moonshot AI Kimi-K2.5 | 7,646 | | \*\*glm-5.1\*\* | ZhipuAI GLM-5.1-FP8 | 7,055 | \## Loading ```python from datasets import load\_dataset \# Kimi-K2.5 traces ds = load\_dataset("lambda/hermes-agent-reasoning-traces", "kimi", split="train") \# GLM-5.1 traces ds = load\_dataset("lambda/hermes-agent-reasoning-traces", "glm-5.1", split="train") ``` \## Schema Both configs share the same schema: | Field | Type | Description | |-------|------|-------------| | `id` | string | UUID identifier | | `conversations` | list | Multi-turn dialogue (system, human, gpt, tool messages) | | `tools` | string | JSON tool definitions available to the agent | | `category` | string | High-level task category | | `subcategory` | string | Fine-grained task type | | `task` | string | Task description (from user prompt) | Conversation messages use ShareGPT format: ```json {"from": "system|human|gpt|tool", "value": "..."} ``` \- `<think>` blocks contain chain-of-thought reasoning \- `<tool\_call>` blocks contain function invocations \- `<tool\_response>` blocks contain real execution results \## Statistics | Metric | kimi | glm-5.1 | |--------|------|---------| | Samples | 7,646 | 7,055 | | Total turns | 185,798 | 134,918 | | Total tool calls | 106,222 | 68,328 | | Avg turns per sample | 24.3 | 19.1 | | Avg tool calls per sample | 13.9 | 9.7 | | Avg `<think>` depth (words) | 414 | 70 | \## Categories Both configs use a shared 9-category taxonomy: | Category | kimi | glm-5.1 | |----------|-----:|--------:| | Terminal \& Coding | 2,010 | 2,237 | | Agent Tools | 1,474 | 2,775 | | Repository Tasks | 1,109 | 1,022 | | Browser Automation | 1,048 | 639 | | Multi-Tool | 807 | 52 | | File Operations | 757 | 134 | | Scheduling | 204 | 104 | | Planning \& Organization | 201 | 92 | | Conversational | 36 | 0 | \## Generation Details \### Kimi-K2.5 \- \*\*Model:\*\* `moonshotai/Kimi-K2.5` (MoE) \- \*\*Inference:\*\* vLLM with `--tool-call-parser kimi\_k2 --reasoning-parser kimi\_k2 --enable-auto-tool-choice` \### GLM-5.1 \- \*\*Model:\*\* `zai-org/GLM-5.1-FP8` \- \*\*Inference:\*\* vLLM with `--tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice` \- \*\*Serving:\*\* 3x 8xH100 nodes via load-balanced gateway \- \*\*Context:\*\* 202,752 tokens max, MTP speculative decoding Both datasets were generated using the \[hermes-agent-generator](https://github.com/nousresearch/hermes-agent) pipeline with \*\*real tool execution\*\* (terminal commands, file operations, browser actions) — not synthetic outputs. \## Data Sources Both datasets include trajectories across the same task categories: \- \*\*Terminal \& Coding\*\* — script writing, debugging, environment setup, data processing, testing, documentation \- \*\*Browser Automation\*\* — Playwright-based navigation, scraping, form filling, screenshot analysis \- \*\*Agent Tools\*\* — Hermes-specific capabilities: memory persistence, task delegation, skill management, todo planning, code execution, session recall \- \*\*Repository Tasks\*\* — real codebase work across GitHub repos: bug fixes, feature implementation, test writing, code review, refactoring \## License Apache 2.0
--- license: apache-2.0 task_categories: - 文本生成 language: - 英语 tags: - 工具调用（tool-calling） - 函数调用（function-calling） - AI智能体（AI Agent） - hermes - 推理（reasoning） - sharegpt - 监督微调（SFT） size_categories: - 10K<n<100K configs: - config_name: kimi data_files: - split: train path: data/kimi/train.parquet - config_name: glm-5.1 data_files: - split: train path: data/glm-5.1/train.parquet --- # Hermes 智能体推理轨迹（Hermes Agent Reasoning Traces） 本数据集包含用于基于Hermes智能体（Hermes Agent）框架训练AI智能体的多轮工具调用轨迹。每个样本均为真实的智能体对话，包含逐步推理过程（`<think>`块）与实际工具执行结果。 本数据集包含两个配置项，分别对应两个源模型： | 配置名称 | 对应模型 | 样本数量 | |---------|---------|---------| | **kimi** | 月之暗面Kimi-K2.5 | 7,646 | | **glm-5.1** | 智谱AI GLM-5.1-FP8 | 7,055 | ## 加载方式 python from datasets import load_dataset # Kimi-K2.5 轨迹 ds = load_dataset("lambda/hermes-agent-reasoning-traces", "kimi", split="train") # GLM-5.1 轨迹 ds = load_dataset("lambda/hermes-agent-reasoning-traces", "glm-5.1", split="train") ## 数据结构（Schema） 两个配置项共享同一套数据结构： | 字段名 | 数据类型 | 字段说明 | |-------|---------|---------| | `id` | 字符串 | UUID 标识符 | | `conversations` | 列表 | 多轮对话内容，包含系统提示、用户提问、大语言模型回复、工具消息 | | `tools` | 字符串 | 智能体可用的 JSON 格式工具定义 | | `category` | 字符串 | 高阶任务类别 | | `subcategory` | 字符串 | 细粒度任务类型 | | `task` | 字符串 | 任务描述，即用户原始提示词 | 对话消息采用 ShareGPT 格式： json {"from": "system|human|gpt|tool", "value": "..."} - `<think>` 块包含思维链（Chain-of-Thought）推理内容 - `<tool_call>` 块包含函数调用指令 - `<tool_response>` 块包含真实的工具执行结果 ## 统计信息 | 评估指标 | kimi | glm-5.1 | |---------|------|---------| | 总样本数 | 7,646 | 7,055 | | 总对话轮次 | 185,798 | 134,918 | | 总工具调用次数 | 106,222 | 68,328 | | 单样本平均对话轮次 | 24.3 | 19.1 | | 单样本平均工具调用次数 | 13.9 | 9.7 | | 平均 `<think>` 块长度（单词数） | 414 | 70 | ## 任务分类体系 两个配置项均采用统一的9分类法： | 任务类别 | kimi 样本数 | glm-5.1 样本数 | |---------|----------|-------------| | 终端与编码（Terminal & Coding） | 2,010 | 2,237 | | 智能体工具（Agent Tools） | 1,474 | 2,775 | | 代码仓库任务（Repository Tasks） | 1,109 | 1,022 | | 浏览器自动化（Browser Automation） | 1,048 | 639 | | 多工具协同（Multi-Tool） | 807 | 52 | | 文件操作（File Operations） | 757 | 134 | | 任务调度（Scheduling） | 204 | 104 | | 规划与组织（Planning & Organization） | 201 | 92 | | 对话交互（Conversational） | 36 | 0 | ## 生成细节 ### Kimi-K2.5 - **模型**：`moonshotai/Kimi-K2.5`（混合专家模型 MoE） - **推理引擎**：vLLM，启用参数 `--tool-call-parser kimi_k2 --reasoning-parser kimi_k2 --enable-auto-tool-choice` ### GLM-5.1 - **模型**：`zai-org/GLM-5.1-FP8` - **推理引擎**：vLLM，启用参数 `--tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice` - **服务部署**：通过负载均衡网关部署于 3 台 8 卡 H100 节点集群 - **上下文窗口**：最大支持 202,752 个 Token，采用 MTP 推测解码（Multi-Token Prompt Speculative Decoding） 两个数据集均通过 [hermes-agent-generator](https://github.com/nousresearch/hermes-agent) 流水线生成，且采用**真实工具执行**（终端命令、文件操作、浏览器操作）而非合成输出。 ## 数据来源 两个数据集均覆盖以下同类任务类别： - **终端与编码**：脚本编写、代码调试、环境配置、数据处理、测试验证、文档撰写 - **浏览器自动化**：基于 Playwright 的网页导航、数据爬取、表单填写、截图分析 - **智能体工具**：Hermes 专属能力：内存持久化、任务委派、技能管理、待办规划、代码执行、会话回溯 - **代码仓库任务**：GitHub 代码库的实际开发工作：缺陷修复、功能实现、测试用例编写、代码审查、代码重构 ## 许可证 Apache 2.0
