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.openai-tool-calling-dataset
OpenAI Tool Calling Dataset (SFT-Ready)
High-quality tool-calling conversations optimized for supervised fine-tuning (SFT).
Dataset Info
Records: 63 conversations
Format: JSONL (train.jsonl)
Quality: GPT-4o-mini filtered
Schema: OpenAI fine-tuning compatible
Structure: User message + Assistant tool call (truncated for SFT)
SFT Format
Each conversation contains exactly 2 messages:
User message: The request/prompt
Assistant message: Tool call response (with… See the full description on the dataset page: https://huggingface.co/datasets/zhendongnvidia/openai-tool-calling-dataset.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.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.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-sft-run
Run record — Qwen3.6-27B tool-calling 20/80 SFT
Everything the training run produced except the weights: the TRL log history, the resolved
config, the environment, the loss/accuracy figure and its greppable markdown mirror.
The adapter is at LASR-Callum/2026-07-31-wrongly-trained-qwen36-toolcalling-tulu-lora-20-80; the training data is
at LASR-Callum/2026-07-31-toolcalling-tulu-20-80-mixture.
Required metadata
field
value
experiment
One bf16 LoRA SFT… See the full description on the dataset page: https://huggingface.co/datasets/dougalldeepmind/2026-07-31-toolcalling-tulu-sft-run.qwen3-tool-calling-sft-dataset
Tool Calling Dataset for Fine-Tuning
High-quality tool calling dataset with consistent schema for supervised fine-tuning.
Dataset Description
This dataset contains 11 high-quality single-turn tool calling conversations in standard OpenAI chat completion format.
Features
✅ Schema Consistent: All parameter types normalized across records
✅ Quality Filtered: GPT-4o-mini evaluated (score ≥ 7.0/10)
✅ OpenAI Compatible: Ready for direct use with OpenAI fine-tuning… See the full description on the dataset page: https://huggingface.co/datasets/zhendongnvidia/qwen3-tool-calling-sft-dataset.pvp-tool-calling-sft
PvP tool-calling SFT cold-start data
Claude-vs-Claude games played through the G.O.D PvP tool-calling harness. Each row is one model turn (or post-game reflection): the system+user prompt the harness built, the assistant response (content + tool_calls), and the tools schemas — i.e. the OpenAI messages+tools format consumed by tokenizer.apply_chat_template(messages, tools=tools). On a move turn the assistant co-emits any memory-tool edits and a game_action committing a legal… See the full description on the dataset page: https://huggingface.co/datasets/gradients-io-tournaments/pvp-tool-calling-sft.glaive_agentive_tool_callingrouter-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.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.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.formatted_tool_calling_datasetdfm11-synthetic-native-tool-calling-repaired
dfm11-synthetic-native-tool-calling-repaired
DFM8 synthetic tool trajectories with compatibility normalization materialized in source data.
This is a DFM11 replacement for schneiderkamplab/dfm8-synthetic-native-tool-calling. All rows pass exhaustive structural validation. See metadata/manifest.json.
thinking-tool-calling-sftqwen3-tool-calling-sft-dataset-1k
Tool Calling Dataset for Fine-Tuning
High-quality tool calling dataset for supervised fine-tuning.
Dataset Description
This dataset contains 847 high-quality single-turn tool calling conversations in standard OpenAI chat completion format, optimized for supervised fine-tuning.
Features
✅ High Quality: GPT-4o-mini evaluated (score ≥ 6.0/10)
✅ SFT Ready: Truncated to user+assistant pairs for supervised fine-tuning
✅ OpenAI Compatible: Ready for direct use with… See the full description on the dataset page: https://huggingface.co/datasets/zhendongnvidia/qwen3-tool-calling-sft-dataset-1k.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.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.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.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.tool-callingKali-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.CodeXomics-ToolCalling-v1
CodeXomics-ToolCalling-v1
This dataset contains the supervised tool-calling trajectories used to fine-tune
CodeXomics-ToolAgent-4B-v1 (internally qwen3.5:4b-codexomics-tools-v5) for the CodeXomics
genomics workbench (an AI-native genome browser; source:
github.com/Scilence2022/CodeXomics). It is released
as the reproducibility artifact for the corresponding paper section and is licensed under
Apache-2.0.
Contents
train.jsonl — 373 supervised examples
valid.jsonl… See the full description on the dataset page: https://huggingface.co/datasets/CodeXomics/CodeXomics-ToolCalling-v1.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.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.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.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.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.dfm8-synthetic-native-tool-calling
Native Tool Calling
Synthetic DFM8 training data generated with Gemma 4 31B and filtered by deterministic checks plus a Gemma 4 31B judge.
Schema
Rows are JSONL chat records:
{"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}
Tool-calling rows may also include a top-level tools list and assistant tool_calls.
Counts
accepted rows: 836675
generated rows seen: 4800000
audit rows seen: 4580233… See the full description on the dataset page: https://huggingface.co/datasets/schneiderkamplab/dfm8-synthetic-native-tool-calling.