datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
toolcallsynthetic-math-toolcall-deception
Synthetic Math Tool-Call Deception
200 paired multi-turn math-assistant trajectories (400 rows) for evaluating deception
detectors on mid-trajectory tool-call misreporting.
Each trajectory: a system prompt instructs the model to compute via an execute_python
tool under a stated tool-call limit, and requires every call to carry a running
call_index argument (1 for the first call, 2 for the second, …). The platform enforcing
the limit is said to only see the reported call_index… See the full description on the dataset page: https://huggingface.co/datasets/annahbanannah/synthetic-math-toolcall-deception.tool_callingSWE-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-calls-singleturnQwen3.6-35B-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.tool_calling_shufflegemma3-pythonic-function-tool-calling-v1synthetic-tool-calls-v2Qwen-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.standard_chat_tool_calling_generalsynthetic-tool-callsdeepseek-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.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.tool-calls-multiturnpandora-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.
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
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.ToolCall_synthetic_qwen3original dataset: Salesforce/xlam-function-calling-60k
toucan-toolcall-slca
Toucan-Toolcall (SLCA-GRPO release)
This dataset packages the four splits that appear in the main experiment
of the paper "SLCA-GRPO: Resolving Cross-Segment Credit Misattribution in
Tool-Calling RL":
sft_split — the 42,423-trajectory 2-epoch SFT set used to warm-start
every backbone before RL (main-table recipe).
sft_full — the 74,241-trajectory 1-epoch SFT set, used only for the
"SFT full" single-stage ablation.
rl — the 31,818 multi-turn, schema-constrained trajectories used… See the full description on the dataset page: https://huggingface.co/datasets/AnonymousRepository/toucan-toolcall-slca.tool-calls-sharegptto-tool-call-datasets-LFM2.5-pythonic
to-tool-call-datasets → LFM2.5 Pythonic tool-call format
A derivative of zhangdw/to-tool-call-datasets (apache-2.0)
normalized for fine-tuning Liquid AI LFM2 / LFM2.5 models, whose native tool-call format is Pythonic:
<|im_start|>assistant
<|tool_call_start|>[get_weather(location='Paris, France', unit='celsius')]<|tool_call_end|><|im_end|>
Nine public tool-calling corpora (APIGen-MT, ButtonInstruct, Glaive v2, GraphSyn, LoopTool, τ-bench train, ToolACE, When2Call, xLAM-60k)… See the full description on the dataset page: https://huggingface.co/datasets/hungbenjamin402/to-tool-call-datasets-LFM2.5-pythonic.tool_calling_formattedqwen3.5-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.toolace-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.