datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
tool_callingNexus-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.Qwen3.6-35B-A3B-Tool-Calling
Qwen3.6-35B-A3B Tool-Calling Dataset
This repository presents a function and tool-calling preference and supervised fine-tuning dataset constructed from Nemotron-RL agentic prompt corpora.
For each source prompt, the model was sampled four times with thinking mode enabled. Each resulting candidate trajectory was then evaluated against the dataset’s ground-truth action using exact matching on both the function name and the parsed function arguments.
Overview… See the full description on the dataset page: https://huggingface.co/datasets/zake7749/Qwen3.6-35B-A3B-Tool-Calling.toolcallingplaywright-mcp-toolcalling
Purpose
I wanted to train a small agent to use a browser effectively, most smaller models I tried <32b struggled to call the tools correctly.
I created this dataset for two main reasons:
To help with finetuning smaller models to use the browser specific tools in playwright.
To look at the security implications of giving browser access to untrusted open-weight models, see blog post.
Versions
I am ironing out the kinks, but I will leave the older versions here in… See the full description on the dataset page: https://huggingface.co/datasets/jdaddyalbs/playwright-mcp-toolcalling.gemma3-pythonic-function-tool-calling-v1tool_calling_shuffletool-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.deepseek-v4-pro-agent-tool-calling-trajectory
DeepSeek V4 Pro ToolScale Agent SFT Dataset
A curated subset of multi-turn tool-calling trajectories generated by DeepSeek V4 Pro on ToolScale. The dataset is filtered by action-match score against ground-truth trajectories and is designed for supervised fine-tuning of agentic models on realistic, multi-step tool use.
Each conversation includes natural-language user requests, tool calls, tool observations, assistant reasoning traces, and grounded final responses across five… See the full description on the dataset page: https://huggingface.co/datasets/zake7749/deepseek-v4-pro-agent-tool-calling-trajectory.standard_chat_tool_calling_generalqwen3.5-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.Qwen-3.6-plus-agent-tool-calling-trajectory
Qwen 3.6 Plus: ToolScale Agent SFT Dataset
Multi-turn tool-calling trajectories generated by Qwen 3.6 Plus via OpenRouter on ToolScale. Both passing and near-passing rollouts are included, allowing users to choose their own quality threshold using reward and score.
Each row is a flattened conversation prefix ending at one assistant turn, ready for next-token SFT. Assistant turns include a reasoning_content field containing the model’s reasoning.
What's inside… See the full description on the dataset page: https://huggingface.co/datasets/zake7749/Qwen-3.6-plus-agent-tool-calling-trajectory.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.qwen3-tool-callingThese datasets converted and tokenized for Qwen3:
interstellarninja/toolace_sequential_tool_use_reasoning
interstellarninja/tool-calls-single-reasoning
interstellarninja/tool-use-multiturn-reasoning
AymanTarig/function-calling-v0.2-with-r1-cot
zzzch/function-calling-reasoning-v1
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.xlam-ru-tool-callingEnhanced_Tool_CallingTool-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.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.tool_calling_formattedglaive_agentive_tool_callingtoolace-tool-calling-hallucination-ragtruth
ToolACE-derived Tool-Calling Hallucination Dataset
This dataset was created for the course assignment Hallucination Detection in Tool Calling.
It is synthetic by design: starting from ToolACE-style tool-calling dialogues, we automatically inject three required hallucination types:
tool_contradiction
overgeneration
missing_tool
Each example follows a RAGTruth-like format:
query: user query
context: tool output
output: final model answer
hallucination_labels: span-level… See the full description on the dataset page: https://huggingface.co/datasets/marrita/toolace-tool-calling-hallucination-ragtruth.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.