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.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-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.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.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-Calling-Dataset-UIGEN-X
Tool Calling Dataset for UIGEN-X
This is a cleaned version of the interstellarninja/hermes_reasoning_tool_use dataset, specifically prepared for training with Axolotl.
Usage with Axolotl
datasets:
- path: smirki/Tool-Calling-Dataset-UIGEN-X
type: chat_template
field_messages: conversations
message_property_mappings:
role: from
content: value
split: train
Example Structure
{
"conversations": [
{
"from": "system"… See the full description on the dataset page: https://huggingface.co/datasets/smirki/Tool-Calling-Dataset-UIGEN-X.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.reasoning-tool-calling-v1
Reasoning and Tool Calling
This dataset converts tagged reasoning and tool-use conversations into typed
messages and tool definitions. It also includes calibration data derived from
those converted conversations.
The source is
Mustafaege/qwen3.5-toolcalling-v2
at revision 8f0343a5613879fefda0eb002d10ff7150a2c588.
How this differs from the source
The source stores 92,153 train conversations and 10,240 test conversations in
a tagged message format. Protocol… See the full description on the dataset page: https://huggingface.co/datasets/steadfastgaze/reasoning-tool-calling-v1.qwen3.5-toolcalling-v1
Qwen3.5 Tool Calling Dataset v1
A tool-calling SFT dataset built from smirki/Tool-Calling-Dataset-UIGEN-X (a cleaned version of interstellarninja/hermes_reasoning_tool_use), converted from ShareGPT conversations format to Qwen3 messages format. Features deep reasoning chains with <think> tags followed by structured tool calls.
Dataset Summary
Property
Value
Total Samples
51,004
Train Split
45,904
Test Split
5,100
Source
smirki/Tool-Calling-Dataset-UIGEN-X… See the full description on the dataset page: https://huggingface.co/datasets/Mustafaege/qwen3.5-toolcalling-v1.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.qwen3.5-toolcalling-v1
Qwen3.5 Tool Calling Dataset v1
A tool-calling SFT dataset built from smirki/Tool-Calling-Dataset-UIGEN-X (a cleaned version of interstellarninja/hermes_reasoning_tool_use), converted from ShareGPT conversations format to Qwen3 messages format. Features deep reasoning chains with <think> tags followed by structured tool calls.
Dataset Summary
Property
Value
Total Samples
51,004
Train Split
45,904
Test Split
5,100
Source… See the full description on the dataset page: https://huggingface.co/datasets/Theafricatechguy/qwen3.5-toolcalling-v1.hermes-flight-recorder-browser-tool-calling-trajectories
Hermes Flight Recorder Browser Tool-Calling Trajectories
This dataset repository publishes the exact public-synthetic artifacts used by
the Qwen3-4B browser LoRA case study.
data/browser/flightrecorder_action_sft.jsonl: governed browser train view.
data/development_action_sft.jsonl: frozen multi-scope development file; the
evaluator selects the browser task scope.
data/sealed_final_action_sft.jsonl: original frozen multi-scope final file;
the evaluator selects the browser task… See the full description on the dataset page: https://huggingface.co/datasets/zwright/hermes-flight-recorder-browser-tool-calling-trajectories.turkish-tool-calling-10k
Turkish Tool Calling 10k
A synthetic Turkish tool-calling dataset generated using ToolsGen with Qwen models via OpenRouter.
Dataset Details
Generated with: ToolsGen
Total Samples: 10,000
Language: Turkish
Format: Single-turn conversations with tool calls
Models Used
Problem Generator: qwen/qwen3-235b-a22b-2507 (temp=1.0)
Tool Caller: qwen/qwen3-235b-a22b-2507 (temp=0.0)
Judge: qwen/qwen3-235b-a22b-2507 (temp=0.0)
Dataset Structure
Each record… See the full description on the dataset page: https://huggingface.co/datasets/atasoglu/turkish-tool-calling-10k.turkish-tool-calling
Türkçe Tool-Calling Veri Seti
Bu veri seti Mustafaege/qwen3.5-toolcalling-v2 veri seti temel alınarak hazırlanmış Türkçe bir alt kümedir. Veri seti 41,480 satır içermektedir (39,406 train / 2,074 test). Veri setinde bulunan sütun aşağıda açıklanmıştır:
messages: Her konuşmanın {"role", "content"} sözlüklerinden oluşan listesi. Roller: system, user, assistant, answer.
Çeviride doğal dil (kullanıcı soruları, asistan yanıtları, <think> akıl yürütme, araç açıklamaları, sistem… See the full description on the dataset page: https://huggingface.co/datasets/ituperceptron/turkish-tool-calling.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.tool-calling
Tool Calling Harness Training Corpus
This dataset contains supervised tool-calling conversations and executable eval tasks for three local coding-agent harnesses:
Swival
opencode
Codex CLI
The examples are OpenAI-style chat records. Assistant tool calls use a tool_calls array with JSON-encoded function arguments, followed by tool-result messages and final assistant responses. The corpus is intended for fine-tuning and evaluating local models that need to choose the right tool… See the full description on the dataset page: https://huggingface.co/datasets/jedisct1/tool-calling.synthetic-healthcare-tool-calling-grpo-rlvr-1k
🏥 Synthetic Healthcare Tool Calling Dataset for GRPO and RLVR
This is a synthetic dataset designed for training language models on clinical decision support tool calling using GRPO (Group Relative Policy Optimization) with verifiable rewards (RLVR). The dataset contains ~1.1K examples of clinical scenarios paired with expected tool calls and answers.
Dataset sample schema:
{
"prompt": [
{
"role": "system",
"content": "You are a clinical decision support assistant… See the full description on the dataset page: https://huggingface.co/datasets/pranavvmurthy26/synthetic-healthcare-tool-calling-grpo-rlvr-1k.adaptive-curriculum-tool-calling-pool
Adaptive Curriculum Tool-Calling Pool — v2.1
A gated snapshot of the tool-calling training-data pool produced by the
Adaptive Curriculum for Tool Calling sub-experiment. This is the additive v2.1
revision: it keeps the entire v1 + v2 payload and adds the six per-campaign
partition manifests under manifests/partitions/. Nothing from v1 or v2 was
re-encoded, recompressed, moved, or rewritten.
Access is gated. The repository uses manual gating. You must be granted access
by the… See the full description on the dataset page: https://huggingface.co/datasets/kesava89/adaptive-curriculum-tool-calling-pool.ecommerce-chat-tool-calling
E-commerce Chat Tool-Calling Dataset (generic, schema-following)
Synthetic training data that teaches a small model (e.g.
google/functiongemma-270m-it) to map natural-language shopping requests
onto whatever tool schema is declared in the prompt — not onto one
hard-coded API.
A visitor chats with the store's corner chatbot: "I need an inexpensive
top-loading washing machine, preferably from a German manufacturer" → the
model must emit the declared tool call with the right query… See the full description on the dataset page: https://huggingface.co/datasets/Qrzysztof/ecommerce-chat-tool-calling.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.code-tool-calling-functiongemma
Code Tool Calling Dataset (FunctionGemma Format)
A curated dataset of coding-focused tool-calling examples formatted for FunctionGemma-style training as demonstrated in google/mobile-actions.
Dataset Overview
Total: 82,406 examples (85% train / 15% validation split per subset)
Subsets
Config Name
Original Dataset
Original Subset
Train
Validation
Total
ToolRM
ibm-research/ToolRM-train-data
train
44,293
7,823
52,116
Toucan-1.5M-SFT… See the full description on the dataset page: https://huggingface.co/datasets/eacortes/code-tool-calling-functiongemma.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.synthetic-financial-tool-calling-grpo-rlvr-1k
🤖 Synthetic Financial Tool Calling Dataset for GRPO and RLVR
This is a synthetic dataset designed for training language models on financial tool calling using GRPO (Group Relative Policy Optimization) with verifiable rewards (RLVR). The dataset contains ~1.1K examples of financial planning queries paired with expected tool calls and answers.
Dataset sample schema,
{
"prompt": [
{
"role": "system",
"content": "You are a financial planning assistant with tools for… See the full description on the dataset page: https://huggingface.co/datasets/pranavvmurthy26/synthetic-financial-tool-calling-grpo-rlvr-1k.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.