datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.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.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.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.
2026-07-31-toolcalling-tulu-20-80-mixture
Tool-calling + TULU3 replay SFT mixture (20/80) for Qwen3.6-27B
The training mixture behind
LASR-Callum/2026-07-31-wrongly-trained-qwen36-toolcalling-tulu-lora-20-80: 1,492,442 Qwen3.6
tokens across 2,002 pre-rendered conversations, split
19.96% agentic tool-use / 80.04% TULU3 replay.
Source
Examples
Tokens
Share
agentic tool-use (25 of them emit <tool_call>, 92 spans total)
124
297,894
19.96%
TULU3 replay
1,878
1,194,548
80.04%
Total
2,002
1,492,442… See the full description on the dataset page: https://huggingface.co/datasets/dougalldeepmind/2026-07-31-toolcalling-tulu-20-80-mixture.turkish-tool-calling
Türkçe Tool-Calling Veri Seti
56.247 kayıt. xLAM/APIGen 60k ve NVIDIA When2Call'dan türetilmiş,
üç davranış sınıfı içeren Türkçe function-calling veri seti.
from datasets import load_dataset
ds = load_dataset("bilalabic/turkish-tool-calling") # mesaj listesi
ds = load_dataset("bilalabic/turkish-tool-calling", "table") # düz tablo
ds = load_dataset("bilalabic/turkish-tool-calling", "sharegpt") # ShareGPT
İçerik
Kayıt
56.247… See the full description on the dataset page: https://huggingface.co/datasets/bilalabic/turkish-tool-calling.Linux-terminal-tool-calling
Linux Terminal Tool Calling Dataset (Linux-terminal-tool-calling)
This dataset is designed for training and fine-tuning AI agents on tool calling, reasoning, and command execution specifically for standard Linux terminal utilities and system administration tasks. It transforms raw Linux terminal command records into a structured multi-turn conversation format featuring detailed chain-of-thought/reasoning content and OpenAI/OpenClaw-style function calling.
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/iselabvn/Linux-terminal-tool-calling.router-assistant-tool-calling-en-es
Router Assistant Tool Calling EN-ES
Synthetic English and Spanish conversations for supervised fine-tuning of a small,
local router assistant. The assistant answers brief social turns, obtains current
network facts through tools, handles tool failures, and asks for confirmation before
restarting the router or disabling WAN internet access.
Dataset size
Split
Conversations
Assistant completions
Train
11,066
21,242
Validation
984
1,890
Test
926
1,769… See the full description on the dataset page: https://huggingface.co/datasets/Lucasllfs/router-assistant-tool-calling-en-es.scugnizz-toolcalling-synthetic-v3
Scugnizz Tool Calling Synthetic
Dataset sintetico per TOOL_CALL / TOOL_RESULT.
Categorie:
{
"negative_tool_not_available": 18,
"tool_result_mail": 2530,
"positive_hash": 6,
"similar_tools": 90,
"tool_result_finance": 164457,
"positive_ip": 15,
"tool_result_weather": 134612,
"positive_dns": 60,
"positive_multitool": 432,
"tool_result_calendar": 448,
"positive_weather": 72,
"negative_no_tool_needed": 3,
"negative_missing_required_arg": 3… See the full description on the dataset page: https://huggingface.co/datasets/ProjectScugnizz/scugnizz-toolcalling-synthetic-v3.presentation_tool_calling_phase_1
Presentation Tool-Calling Dataset (Phase-1)
Supervised examples for single-step tool routing and argument filling in a slide-deck builder.
What Phase-1 trains
Choose the correct tool (function) for a user instruction.
Fill tool arguments as strict JSON.
Optionally use injected deck-state context to pick correct slide numbers/titles.
Files
train.jsonl, validation.jsonl, test.jsonl: one JSON object per line
tools.json: tool schema (tool names + arg schemas)… See the full description on the dataset page: https://huggingface.co/datasets/raketa314/presentation_tool_calling_phase_1.qwen_tool-calling_finetune_dataset
🛠️ Tool-Calling Instruction Dataset
This dataset consists of instruction-completion pairs for training Large Language Models (LLMs) to convert natural language requests into structured tool/function calls.The data format is inspired by ChatML and includes explicit system, user, and assistant roles.
Dataset Structure
Each example is a single JSON object with a text field, containing a chat-formatted prompt and response.
The user gives an instruction (e.g., "Analyze… See the full description on the dataset page: https://huggingface.co/datasets/emrecandan0/qwen_tool-calling_finetune_dataset.tool-calling-browser-agent-tasks
Dataset Card
Created by: DataCreator AI
Overview
Tool Calling for Agentic Tasks with Multi-Step Workflows contains 1,062 synthetic multi-turn conversations between a user and an AI assistant. The examples primarily focus on practical agentic tasks such as train ticket booking, dynamic form filling, and payment processing. It provides diverse scenarios including successful execution, context retrieval, tool integration, and failure recovery.
The dataset is… See the full description on the dataset page: https://huggingface.co/datasets/DataCreatorAI/tool-calling-browser-agent-tasks.eu-multilang-tool-calling-180k
eu-multilang-tool-calling-180k
175,716 multi-turn function-calling conversations in 6 under-served EU languages: Hungarian, Bulgarian, Greek, Croatian, Slovak, Slovenian. Apache 2.0 — commercial use permitted.
~29-30K examples per language across 7 domains: fiscal, banking, e-commerce, calendar, weather, generic government services.
Quick Load (SFT / Instruction Tuning)
from datasets import load_dataset
# Full dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/eu-multilang-tool-calling-180k.loap-reasoning-toolcalling-20k
loap-reasoning-toolcalling-20k
loap-reasoning-toolcalling-20k is a synthetic dataset designed to train language models in reasoning (Chain of Thought) and tool usage.
Language: English
Format: Chat (System, User, Model, Tool)
Dataset Structure
[
{
"id": "synthetic_agent_00001",
"conversations": [
{
"role": "system",
"content": "You are a helpful AI agent.\nYou have access to the following tools:"
},
{
"role": "tools"… See the full description on the dataset page: https://huggingface.co/datasets/igidn/loap-reasoning-toolcalling-20k.Kali-tool-calling
Kali Linux Tool Calling Dataset (Kali-tool-calling)
This dataset is designed for training and fine-tuning AI agents on tool calling, reasoning, and command execution specifically for Kali Linux tools. It transforms the original KALI_LINUX_TOOLKIT_DATASET into a structured multi-turn conversation format featuring detailed chain-of-thought/reasoning content and OpenAI/OpenClaw-style function calling.
Dataset Details
Total Records: 790
Language: English
Format:… See the full description on the dataset page: https://huggingface.co/datasets/iselabvn/Kali-tool-calling.turkish-tool-calling-quality-gated-preview
Turkish Tool-Calling Quality-Gated Preview
Preview, not Gold: This public research preview is quality-gated, but it
is not human-verified at dataset level. The pipeline's formal
publish_allowed=false state remains unchanged.
Review statement
A maintainer performed a limited manual spot-check of six diverse records,
covering tool calls, multiple calls, no-tool behavior, and clarification. This
is a qualitative sample review only; it is not a row-by-row human… See the full description on the dataset page: https://huggingface.co/datasets/bilalabic/turkish-tool-calling-quality-gated-preview.tool-calling-traces-ptbr
Tool calling conversations in Portuguese
484 synthetic conversations that teach a model when to call a tool, which one to call and
with which arguments, and also when to answer directly, with no tool at all.
Each line of the file is a complete conversation: the user's question, the tool call, the
simulated return of that tool, and the final answer.
It was built because no dataset of tool calling in Portuguese with fictional tools existed.
The 30 tools and the user questions were… See the full description on the dataset page: https://huggingface.co/datasets/annajuliaasf/tool-calling-traces-ptbr.keural-v2-tool-calling
Tool & Function Calling (Area 2) — Korean SFT Dataset Prep
상태: 비공개 스테이징 (private) — 제2자 감사 전, 공개 배포 대상 아님
출처
원본: glaiveai/glaive-function-calling-v2
커밋 해시: e7f4b6456019f5d8bcb991ef0dd67d8ff23221ac
라이선스: Apache-2.0 (원본 태그, README 본문 없어 대조 문구 없음)
생성 출처: 미확인 — GPT-4/Claude 등 프론티어 모델 사용 가능성 있음 (원본 데이터셋 카드에 명시 없음)
언어: 영어 (지침서 §1.2 정책에 따라 번역 없이 영어 그대로 사용)
처리 과정
원본 112,960건 다운로드
chat 필드 기준 완전 중복 23,790건(21%) 발견 및 제거 → 유니크 89,170건
유니크 풀에서 seed=42로 50… See the full description on the dataset page: https://huggingface.co/datasets/mkd-minju/keural-v2-tool-calling.ro-tool-calling-50k
ro-tool-calling-50k
50K Romanian-language tool-calling conversations covering fiscal, legal, e-commerce, and administrative domains. Each example is a multi-turn chat with function definitions, tool calls, and responses — entirely in Romanian. Apache 2.0 — commercial use permitted.
The only open Romanian function-calling dataset. Designed for fine-tuning Romanian-language assistants that need to interact with real government/enterprise APIs.
Quick Load
from… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/ro-tool-calling-50k.smarthome-tool-calling-tiny
Smarthome Tool Calling Tiny
Dataset | Notebook | Demo Video |
A tiny sample dataset for fine-tuning SLMs for tool-calling tasks in the context of smart home assistants. It is synthetically generated and shared for demo purposes.
This custom tool-calling dataset is synthetically created with Afterimage, our purpose-built synthetic dataset generation engine. See the demo video.
What is Afterimage?
Building custom Small Language Models (SLMs) starts with great data.… See the full description on the dataset page: https://huggingface.co/datasets/altaidevorg/smarthome-tool-calling-tiny.keural-v2-tool-calling-v2
Tool & Function Calling (Area 2, v2) — Korean SFT Dataset Prep
상태: 비공개 스테이징(private) — §3 처리(1~6번, 스키마 정규화) 완료, §3-7(최종 텍스트 인코딩)만 보류. 제2자 감사 전, 공개 배포 대상 아님.
이 v2는 §3 처리를 새로 검증하며 발견한 오류를 수정한 버전입니다(2026-08-10). v1(원본 chat 텍스트 그대로)과 달리, 이 저장소엔 korean_sft_schema.md 통합 구조(source/license/lang/category/conversations:[{role,content,reasoning_content,tool_calls}])로 정규화된 데이터가 들어있습니다.
출처
원본: glaiveai/glaive-function-calling-v2
커밋 해시: e7f4b6456019f5d8bcb991ef0dd67d8ff23221ac… See the full description on the dataset page: https://huggingface.co/datasets/mkd-minju/keural-v2-tool-calling-v2.tool-calling_finetune_dataset
🛠️ Tool-Calling Instruction Dataset
This dataset consists of instruction-completion pairs for training Large Language Models (LLMs) to convert natural language requests into structured tool/function calls.The data format is inspired by ChatML and includes explicit system, user, and assistant roles.
Dataset Structure
Each example is a single JSON object with a text field, containing a chat-formatted prompt and response.
The user gives an instruction (e.g., "Analyze… See the full description on the dataset page: https://huggingface.co/datasets/Whoisjutanlee/tool-calling_finetune_dataset.tool-calling-conversations-mrigh6o0
Tool Calling Conversations
An Arena-style dataset of anonymized, multi-turn conversations focused on real-world
tool use. It is intended for research, evaluation, and training of models that decide
when and how to call tools.
The conversations include:
Tool selection and no-tool decisions
Structured tool arguments
Sequential and parallel tool calls
Tool results and error recovery
Multi-step agent workflows
Final responses after tool execution
Data is organized into… See the full description on the dataset page: https://huggingface.co/datasets/dakr-pandas/tool-calling-conversations-mrigh6o0.