datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ToolACE
ToolACE
ToolACE is an automatic agentic pipeline designed to generate Accurate, Complex, and divErse tool-learning data.
ToolACE leverages a novel self-evolution synthesis process to curate a comprehensive API pool of 26,507 diverse APIs.
Dialogs are further generated through the interplay among multiple agents, guided by a formalized thinking process.
To ensure data accuracy, we implement a dual-layer verification system combining rule-based and model-based checks.
More details… See the full description on the dataset page: https://huggingface.co/datasets/lockon/ToolACE.ToolACE
ToolACE
ToolACE is an automatic agentic pipeline designed to generate Accurate, Complex, and divErse tool-learning data.
ToolACE leverages a novel self-evolution synthesis process to curate a comprehensive API pool of 26,507 diverse APIs.
Dialogs are further generated through the interplay among multiple agents, guided by a formalized thinking process.
To ensure data accuracy, we implement a dual-layer verification system combining rule-based and model-based checks.
More details… See the full description on the dataset page: https://huggingface.co/datasets/Team-ACE/ToolACE.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.
Nexus-Agents-ToolCalling
Nexus Agents — Tool-Calling Conversations
Synthetic, schema-verified tool-calling conversations for training the Nexus Projects
agents. This is the exact data behind
Nemotron-3-Nano-30B-A3B — Nexus Agents (GGUF),
including the verification transcripts that scored it (27/27 on the behavioral
interview eval, vs 13/27 for the base model).
Links: the fine-tuned model →
Nemotron-3-Nano-30B-A3B — Nexus Agents (GGUF) ·
the generator + seed data + eval harness →
Nexus Training Studio ·… See the full description on the dataset page: https://huggingface.co/datasets/NexusProjectsAI/Nexus-Agents-ToolCalling.ToolScale
ToolScale Dataset
The ToolScale dataset is a key component of the ToolOrchestra: Elevating Intelligence via Efficient Model and Tool Orchestrationproject. It provides synthetic environment and tool-call tasks specifically generated to aid the reinforcement learning (RL) training of small orchestrator models. These orchestrators are designed to effectively manage and coordinate diverse intelligent tools and other models for solving complex, multi-turn agentic tasks.… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/ToolScale.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.tool-output-extraction-swebench
Tool Output Extraction Dataset
Paper | Code
Training data for squeez — a small model that prunes verbose coding agent tool output to only the evidence the agent needs next.
Task
Task-conditioned context pruning of a single tool observation for coding agents.
Given a focused extraction query and one verbose tool output, return the smallest verbatim evidence block(s) the agent should read next.
The model copies lines from the tool output — it never rewrites, summarizes, or… See the full description on the dataset page: https://huggingface.co/datasets/KRLabsOrg/tool-output-extraction-swebench.toolace-parsed
[PARSED] ToolACE
The data in this dataset is a subset of the original Team-ACE/ToolACE
Subset name
multi-turn
parallel
multiple definition
Last turn type
number of dataset
toolace
yes
yes
yes
complex
11k
This is a re-parsing formatting dataset for the ToolACE official dataset.
Load the dataset
from datasets import load_dataset
ds = load_dataset("minpeter/toolace-parsed")
print(ds)
# DatasetDict({
# train: Dataset({
# features:… See the full description on the dataset page: https://huggingface.co/datasets/minpeter/toolace-parsed.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-llama-format
Open Paws Tool Use Llama Format
This dataset is part of the Open Paws initiative to develop AI training data aligned with animal liberation and advocacy principles. Created to train AI systems that understand and promote animal welfare, rights, and liberation.
Dataset Details
Dataset Type: Tool Use Data
Format: JSONL (JSON Lines)
Languages: Multilingual (primarily English)
Focus: Animal advocacy and ethical reasoning
Organization: Open Paws
License: Apache 2.0… See the full description on the dataset page: https://huggingface.co/datasets/open-paws/tool-use-llama-format.Seal-Tools
Seal-Tools
This Huggingface repository contains the dataset generated in Seal-Tools: Self-Instruct Tool Learning Dataset for Agent Tuning and Detailed Benchmark.
Abstract
Seal-Tools contains self-instruct API-like tools. Seal-Tools not only offers a large
number of tools, but also includes instances
which demonstrate the practical application
of tools. Seeking to generate data on a large
scale while ensuring reliability, we propose a
self-instruct method to generate… See the full description on the dataset page: https://huggingface.co/datasets/casey-martin/Seal-Tools.OpenGrad-ToolPolicy-Canonical-v1
This is a provenance-preserving canonical candidate corpus. It is a pre-training canonical release, not an empirically selected or recommended training mixture.
What this release is
OpenGrad ToolPolicy Canonical v1 is a provenance-preserving, model-independent normalization of several public tool-use and function-calling datasets. It is released as a pre-training candidate corpus for controlled research into tool-use policy in small open-weight language models. See OpenGrad… See the full description on the dataset page: https://huggingface.co/datasets/arjhinety/OpenGrad-ToolPolicy-Canonical-v1.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.
OpenGrad-ToolPolicy-Canonical-v2-M0-snapshot
This is a provenance-preserving canonical candidate corpus. It is a pre-training canonical release, not an empirically selected or recommended training mixture.
What this release is
OpenGrad ToolPolicy Canonical v2 is a provenance-preserving, model-independent normalization of public tool-use and function-calling datasets. It was built to test one hypothesis with a measurement attached: that the tool-call collapse observed in the M0 SFT experiments on v1 was caused by the… See the full description on the dataset page: https://huggingface.co/datasets/arjhinety/OpenGrad-ToolPolicy-Canonical-v2-M0-snapshot.tool-use
Tool-use rollouts (Qwen3, think/nothink)
Tool-augmented code-generation rollouts: Qwen3-8B and Qwen3-14B, each in
thinking and non-thinking mode, on DS-1000, LiveCodeBench (Python) and
Multilingual-LCB (OCaml). During generation the model can call a run_code
tool (up to 3 rounds) that executes its candidate in a sandbox (pinned DS-1000
env / LCB public tests / OCaml compile+publics) and returns real output.
Design: 100 samples per instance at temperature 0.6 (bf16, vLLM)… See the full description on the dataset page: https://huggingface.co/datasets/samuki-hf/tool-use.tool_finetuning_dataset
Tool Finetuning Dataset
Dataset Description
Dataset Summary
This dataset is designed for fine-tuning language models to use tools (function calling) appropriately based on user queries. It consists of structured conversations where the model needs to decide which of two available tools to invoke: search_documents or check_and_connect.
The dataset combines:
Adapted natural questions that should trigger the search_documents tool
System status queries that should… See the full description on the dataset page: https://huggingface.co/datasets/asanchez75/tool_finetuning_dataset.tool-calling-english-100k
Tool Calling English (100K)
100,000 tool-calling conversations in OpenAI function calling format — the largest general English tool-use dataset for fine-tuning.
Motivation
Models trained without tool-calling examples struggle in agentic deployments. This dataset trains the full cycle: deciding when to call a tool, calling it with correct arguments, interpreting the result, and producing a grounded final response.
Dataset Description
100,000… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/tool-calling-english-100k.ToolGen-Datasets
How to use?
Before making use of this dataset, you may need to add the tokens to the vocabulary. For HuggingFace transformers tokenizer, the following is an example code snippet to add tokens.
from unidecode import unidecode
import transformers
with open('virtual_tokens.txt', 'r') as f:
virtual_tokens = f.readlines()
virtual_tokens = [unidecode(vt.strip()) for vt in virtual_tokens]
model_name_or_path = "meta-llama/Meta-Llama-3-8B"
# Load tokenizer and add tokens into… See the full description on the dataset page: https://huggingface.co/datasets/reasonwang/ToolGen-Datasets.Step-3.5-Flash-SFT-No-Tools
Step-3.5-Flash-SFT No-Tools
Filtered subset of stepfun-ai/Step-3.5-Flash-SFT containing only plain chat rows from the raw JSON shards.
Final kept rows: 1493471
No-tool rows before secret filtering: 1495099
Rows removed by accepted secret scan findings: 1628
Primary data files are Parquet shards under data/train-*.parquet.
Filter predicate:
conversations must be a list,
every message must be an object,
message roles must be limited to system, user, and assistant,
no message may… See the full description on the dataset page: https://huggingface.co/datasets/MetonymousAI/Step-3.5-Flash-SFT-No-Tools.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.qwen3.5-toolcalling-v2
Qwen3.5 Tool Calling Dataset v2
An expanded tool-calling SFT dataset combining smirki/Tool-Calling-Dataset-UIGEN-X and AmanPriyanshu/tool-reasoning-sft-jupyter-agent, unified into Qwen3 messages format. Adds Jupyter notebook agent data with code execution reasoning chains.
Dataset Summary
Property
Value
Total Samples
~60K+
Train Split
~55K
Test Split
~6K
Sources
UIGEN-X + Jupyter Agent
Format
Qwen3 messages
Language
English
License
Apache 2.0… See the full description on the dataset page: https://huggingface.co/datasets/Mustafaege/qwen3.5-toolcalling-v2.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.
OpenGrad-ToolPolicy-Canonical-v2
This is a provenance-preserving canonical candidate corpus. It is a pre-training canonical release, not an empirically selected or recommended training mixture.
What this release is
OpenGrad ToolPolicy Canonical v2 is a provenance-preserving, model-independent normalization of
public tool-use datasets in which every record declares what it supervises. It exists because
not every legitimate post-training corpus has the same conversational trajectory shape, and
discarding a… See the full description on the dataset page: https://huggingface.co/datasets/arjhinety/OpenGrad-ToolPolicy-Canonical-v2.infosec-tool-output
Infosec Tool Output
Security-tool output → evidence-backed, plain-English interpretation.
A dataset for training and evaluating models that interpret security-tool output, explain the limits of the evidence, and recommend defensive next steps.
v2.0.0: 1,004 canonical examples across 19 tools. This includes all 776 original records with traceable interpretation changes, plus 228 newly authored synthetic fixtures. The deduplicated training views contain 1004 examples, not… See the full description on the dataset page: https://huggingface.co/datasets/tegridydev/infosec-tool-output.danish-tool-dialogues-v9
danish-tool-dialogues-v1
Danish multi-turn tool-use conversations with reasoning, translated from the
Glaive subset of
Nanbeige/ToolMind
(Apache-2.0) by scripts/translate_toolmind_da.py.
Complements danish-tool-calls-v1, which is single-turn and synthetic. Here
the conversations run several turns, tool results are fed back, and the
assistant reasons before calling.
split
rows
train
34,168
eval_seen_tools
698
eval_unseen_tools
768
eval_seen_sym
752… See the full description on the dataset page: https://huggingface.co/datasets/jensjepsen/danish-tool-dialogues-v9.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.pandora-tool-calling
Pandora Tool Calling
A tool-calling dataset for Supervised fine-tuning of the Pandora Large Language Model (LLM).
The dataset is based on the glaiveai/glaive-function-calling-v2 dataset.
Copyright and license
Copyright (c) 2024, Danilo Peixoto Ferreira. All rights reserved.
Project developed under a BSD-3-Clause license.
tool-calling-mix
This is a dataset for fine-tuning a language model to use tools. I combined sources from various other tool calling datasets and added some non-tool calling examples to prevent catastrophic forgetting.
Dataset Overview
Motivation
This dataset was created to address the need for a diverse, high-quality dataset for training language models in tool usage. By combining multiple sources and including non-tool examples, it aims to produce models that can effectively use tools… See the full description on the dataset page: https://huggingface.co/datasets/younissk/tool-calling-mix.tool-reasoning-sft-CODING-text_to_terminal_v2-sft-tool-use-agent-data-cleaned-rectified
Text to Terminal, v2 — Cleaned & Rectified
👥 Follow the Author
Aman Priyanshu
Overview
This dataset is a cleaned, combined, and thinking-augmented version of muellerzr/text_to_terminal_v2. It pairs natural language instructions with their corresponding terminal/bash commands, now augmented with explicit <think> reasoning traces that model the step-by-step thought process before producing the final command.The restructuring approach is directly… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/tool-reasoning-sft-CODING-text_to_terminal_v2-sft-tool-use-agent-data-cleaned-rectified.