datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
glaive_toolcall_enBorrowed from: https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2
You can use it in LLaMA Factory by specifying dataset: glaive_toolcall_en.
hermes_reasoning_tool_use
TL;DR
51 004 ShareGPT conversations that teach LLMs when, how and whether to call tools.Built with the Nous Research Atropos RL stack in Atropos using a custom MultiTurnToolCallingEnv, and aligned with BFCL v3 evaluation scenarios.Released by @interstellarninja under Apache-2.0.
1 Dataset Highlights
Count
Split
Scenarios covered
Size
51 004
train
single-turn · multi-turn · multi-step · relevance
392 MB
Each row: OpenAI-style conversations… See the full description on the dataset page: https://huggingface.co/datasets/interstellarninja/hermes_reasoning_tool_use.SWE-Bench-Verified-O1-native-tool-calling-reasoning-high-results
SWE-Bench Verified O1 Dataset
Executive Summary
This repository contains verified reasoning traces from the O1 model evaluating software engineering tasks. Using OpenHands + CodeAct v2.2, we tested O1's bug-fixing capabilities using their native tool calling capabilities on the SWE-Bench Verified dataset, achieving a 45.8% success rate across 500 test instances.
Overview
This dataset was generated using the CodeAct framework, which aims to improve code… See the full description on the dataset page: https://huggingface.co/datasets/AlexCuadron/SWE-Bench-Verified-O1-native-tool-calling-reasoning-high-results.reason-tool-use-demo-1500
Dataset info
The dataset is a selection of reasoning toolcalls data from https://huggingface.co/datasets/interstellarninja/hermes_reasoning_tool_use, which contains data from Hermes-Tools、Glaive-FC、ToolAce、Nvidia-When2Call.
The format has been transformed to adapt llama-factory v1 training pipeline.
agent-think-tool_use
Agent Think Tool Use
Датасет многошаговых агентных сессий для дообучения моделей работе с кодом, инструментами и инженерными задачами. Записи содержат пользовательские требования, комментарии агента во время работы, decision summaries, вызовы инструментов, результаты запусков, обработку ошибок и финальную проверку.
Каждый shard представляет отдельную связанную сессию, а не отдельный вопрос и ответ. Данные охватывают исследование задачи, работу с документацией, проектирование… See the full description on the dataset page: https://huggingface.co/datasets/ru-dataset/agent-think-tool_use.toolbench-v1
ToolBench Dataset
Dataset Description
ToolBench is an open-source, large-scale, high-quality instruction tuning SFT dataset designed to facilitate the construction of powerful LLMs with general tool-use capability. It was constructed automatically using ChatGPT (gpt-3.5-turbo-16k) upgraded with enhanced function call capabilities.
This dataset corresponds to the training data used for ToolLLaMA.
Repository: OpenBMB/ToolBench
Paper: ToolLLM: Facilitating Large Language… See the full description on the dataset page: https://huggingface.co/datasets/tuandunghcmut/toolbench-v1.tool-use-multiturn-reasoningglaive_toolcall_enBorrowed from: https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2
You can use it in LLaMA Factory by specifying dataset: glaive_toolcall_en.
to-tool-call-datasets
🛠️ To-Tool-Call Datasets
A unified Qwen3-style tool-call corpus for SFT, GRPO, and agent training
To-Tool-Call Datasets is a curated mirror of public tool-call and function-calling corpora, re-serialized into one training-ready messages JSONL convention.
Quick Start ·
At a Glance ·
Format ·
Sources ·
Training Notes
[!IMPORTANT]
This repository is a format-harmonization layer, not a new claim of ownership over the… See the full description on the dataset page: https://huggingface.co/datasets/zhangdw/to-tool-call-datasets.glaive_toolcall_zhBorrowed from: https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2
Translated by GPT-3.5.
You can use it in LLaMA Factory by specifying dataset: glaive_toolcall_zh.
n8n-Toolkit
🤖 n8n-Toolkit Dataset
A comprehensive fine-tuning dataset for n8n workflow automation, AI agents, SEO/marketing strategist, and business automation
📊 Quick Stats
📈 Metric
Count
Percentage
Total Examples
55,026
100%
Estimated Pages
550
-
With Images/Screenshots
54,03898.2%
With System Messages
~55,000
~100%
With Thinking Supervision
~30,000+
~55%
With Task Labels
55,026
100%
🎯 What Is This Dataset?
This is a… See the full description on the dataset page: https://huggingface.co/datasets/DavidrPatton/n8n-Toolkit.tool-math
Tool Math
Tool Math is a chat-style math reasoning dataset designed for small language models that need to learn when to call a calculator tool and how to continue from the returned value.
Each example is a complete conversation with:
a user math word problem,
short assistant reasoning turns,
native OpenAI-style calc tool calls,
separate tool-result messages,
a final answer in \boxed{...},
a structured tool_trace column for programmatic training and evaluation,
a text_messages… See the full description on the dataset page: https://huggingface.co/datasets/User01110/tool-math.Tool-Star-SFT-54KThe cold start dataset of Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement Learning
hf paper link: https://huggingface.co/papers/2505.16410
arxiv link: https://arxiv.org/abs/2505.16410
Github Repo: https://github.com/dongguanting/Tool-Star
tools-benchmark
Cynaptics Tools Benchmark
Public split: 54 multi-hop QA examples (18 each of 2/3/4-hop).
Fields: id, question, context, answer, category, subcategory, metadata.
sft-tool-calling-structured-output-v1
vericava/sft-tool-calling-structured-output-v1
Dataset to train (SFT) 3-20B LLMs for tool calling and structured outputs/classifications.
Includes contents in English as well as some Japanese.
bam-scientific-tool-use
BAM Scientific Tool Use
Inspect real welding measurement files, calculate a precisely specified result, and cite the evidence used. BAM Scientific Tool Use is a small, auditable dataset for evaluating language models with Python tools. It contains 160 tasks over 30 welding passes from five physical workpieces: 130 answerable calculations and 30 constructed cases where a required file is withheld.
The measurements come from Chaurasia et al., Tandem-GMAW: advanced pulsed and… See the full description on the dataset page: https://huggingface.co/datasets/harrrshall/bam-scientific-tool-use.tool-reasoning-sft-TOOLS-hermes_reasoning_tool_use-data-cleaned-rectified
Hermes Reasoning Tool Use — Cleaned & Rectified
👥 Follow the Author
Aman Priyanshu
Overview
This dataset is a cleaned and restructured version of interstellarninja/hermes_reasoning_tool_use. The original dataset uses the Hermes/NousResearch multi-turn format with from/value fields and embedded <think> + <tool_call> tags inside single gpt turns. This version converts it into a strict multi-turn conversation structure with validated role transitions.… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/tool-reasoning-sft-TOOLS-hermes_reasoning_tool_use-data-cleaned-rectified.tool-reasoning-sft-TOOLS-toolace-sft-tool-use-agent-data-cleaned-rectified
ToolACE - Tool-Use Agent Data Cleaned & Rectified
👥 Follow the Author
Aman Priyanshu
Overview
This dataset is a cleaned and restructured version of the Team-ACE/ToolACE dataset. ToolACE is a high-quality conversational tool-use dataset containing 11,300+ examples of natural language interactions requiring function calling across diverse domains. This version converts the original OpenAI function-call format into a standardized multi-turn tool-use… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/tool-reasoning-sft-TOOLS-toolace-sft-tool-use-agent-data-cleaned-rectified.verified-tool-use-dataset
Verified tool-use trajectories for LLM agents
This was a time-boxed experiment by an autonomous agent (Protogonos), now concluded. Nothing here is offered for sale or for hire, and no payment is accepted.
Multi-turn function-calling conversations for training and evaluating
tool-using agents — 48 trajectories across 16 domains, with every tool call
checked against its tool's JSON-Schema. The free sample in this repo is a real
slice of the full set: the viewer above renders it… See the full description on the dataset page: https://huggingface.co/datasets/protogonos/verified-tool-use-dataset.tool-reasoning-sft-RESEARCH-grill-lab-browsecomp-plus-runs-data-cleaned-rectified
Tool-Reasoning SFT — BrowseComp-Plus Runs (Cleaned & Rectified)
Multi-turn tool-use reasoning trajectories derived from grill-lab/browsecomp-plus-runs, converted to a structured SFT format following the interstellarninja/hermes_reasoning_tool_use convention.
Source
Based on the execution trajectories from "Revisiting Text Ranking in Deep Research" (arXiv:2602.21456):
Original data: grill-lab/browsecomp-plus-runs (MIT)
Format
Each row contains a messages… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/tool-reasoning-sft-RESEARCH-grill-lab-browsecomp-plus-runs-data-cleaned-rectified.long-horizon-eval
long-horizon-eval
Evaluation results for long-horizon agent performance
Dataset Description
This dataset contains evaluation results for agent trajectories, including quality assessments and performance metrics.
Dataset Structure
The dataset is organized by model name, with each model having separate JSONL files for different experimental passes.
long-horizon-eval/
├── model-1/
│ ├── pass@1.jsonl
│ ├── pass@2.jsonl
│ └── pass@3.jsonl
├── model-2/
│ └──… See the full description on the dataset page: https://huggingface.co/datasets/ToolGym/long-horizon-eval.toolace_sequential_tool_use_reasoningtool-calls-single-reasoningcomparag-tool-votes
CompaRAG — Tool Votes Dataset
CompaRAG is a blind comparison platform for MCP (Model Context Protocol) tools, built by The Borges Graph as a spinoff of comparIA, a French government initiative (Ministère de la Culture / Beta.gouv.fr).
What is this dataset?
This dataset contains human preference votes collected on the CompaRAG platform. Users submit a task and a goal, two MCP tools respond anonymously, and the user votes for the best result — without knowing which… See the full description on the dataset page: https://huggingface.co/datasets/ArthurSrz/comparag-tool-votes.ToolForge-data
ToolForge — Source QA
The multi-hop question answering corpora that ToolForge
takes as input, in the exact slices used in the paper.
📄 Paper — arXiv:2512.16149
💻 Code — github.com/Buycar-arb/ToolForge
git clone https://github.com/Buycar-arb/ToolForge.git && cd ToolForge
pip install -e ".[all]"
python download_data.py
Contents
257,901 questions across six corpora, drawn from HotpotQA and 2WikiMultihopQA.
corpus
questions
size
question shape… See the full description on the dataset page: https://huggingface.co/datasets/buycar/ToolForge-data.hr-ops-tools
HR-Ops: 8,621 rows of tool calling and cited policy for HR assistants
A training set for HR-operations assistants, built around one idea: make the HR task
objectively checkable. The headline shard is tool calling against authored HR-ops
function schemas, where a correct answer is exact JSON and a wrong one cannot hide behind
fluent prose. Built for the Adaption AutoScientist Challenge, Part 2 (HR).
What this dataset proves, and how you check it
rows
8… See the full description on the dataset page: https://huggingface.co/datasets/Jainamshahhh/hr-ops-tools.tool-use-relevance-reasoninggrpo-4k-reasoning-tools
🧠 GRPO 4K Reasoning Tools
A compact, high-quality dataset designed to train and evaluate reasoning-capable LLMs with tool usage and function calling. This dataset focuses on structured thinking, multi-step reasoning, and practical tool integration across diverse NLP tasks.
📌 Overview
GRPO 4K Reasoning Tools is a curated dataset of ~4K examples that combines:
🧩 Step-by-step reasoning (“thinking” traces)
🛠️ Tool usage / function calling patterns
🧠 Multi-task learning… See the full description on the dataset page: https://huggingface.co/datasets/nphearum/grpo-4k-reasoning-tools.tool-n1-sft-unique-train-eval
Tool-N1 SFT Unique Train-Eval Split
This dataset contains supervised fine-tuning (SFT) data for training models on multi-hop tool usage and reasoning, with proper train/evaluation splits and guaranteed unique queries.
Key Features
✅ Perfect Alternating Format: <think>reasoning</think> <tool_call>tool_call</tool_call> pattern✅ Unique Queries: Complete deduplication based on query content✅ Train/Eval Split: Proper 80/20 split for training and evaluation✅ Multi-hop… See the full description on the dataset page: https://huggingface.co/datasets/Anna4242/tool-n1-sft-unique-train-eval.dino-data-vision-tooling-preview
Dino Data Vision Tooling Preview
What This Dataset Is
This dataset is a focused vision-tooling preview built from two Dino Data capability slices:
image context understanding
image tooling
The goal is to train or inspect assistant behavior for image-related tasks where visual context, multimodal interpretation, or tool-aware image handling is relevant.
Included Capability Slices
Source lane
Public task name
What it teaches… See the full description on the dataset page: https://huggingface.co/datasets/DinoDS/dino-data-vision-tooling-preview.