datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
glaive_toolcall_enBorrowed from: https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2
You can use it in LLaMA Factory by specifying dataset: glaive_toolcall_en.
toolcallpython-toolcallsLogs from run_python_code tool used for benchmarking.
synthetic-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_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.toolcallingtool-calls-singleturnplaywright-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.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.glaive_toolcall_enBorrowed from: https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2
You can use it in LLaMA Factory by specifying dataset: glaive_toolcall_en.
unified-toolcalls-canonical
Unified Tool-Calling Corpus — Canonicalized Output
Publish-ready conversion of two pinned Hugging Face dataset revisions into the single
schema defined in docs/unified_format.md, with repeated
records normalized by an explicit canonicalization rule and every surviving record
kept faithful to its source row.
Records in (source rows)
65,000
Records published (canonical survivors)
64,622
Duplicates collapsed
378 (343 duplicate groups)
Records mutated during… See the full description on the dataset page: https://huggingface.co/datasets/dongbobo/unified-toolcalls-canonical.scugnizz-v22-tool-call
scugnizz-v22-tool-call
Synthetic agentic call-init data: choose the right tool and emit grounded arguments.
Format: Hermes/OpenAI-style messages plus tools.
agentic-tool-call-dataset-12k
Agentic Tool Calling Dataset 12K
A curated 12K-sample tool-calling SFT dataset in a TRL-ready chat format. Each sample contains multi-turn agent trajectories with explicit reasoning, structured tool_calls, and tool responses.
Dataset Summary
Property
Value
Total Samples
12,000
Short split
10,000 (agent_short_10k.jsonl)
Long split
2,000 (agent_long_2k.jsonl)
Language
English
Format
OpenAI-style messages with tool_calls
License
Apache 2.0… See the full description on the dataset page: https://huggingface.co/datasets/pyromind/agentic-tool-call-dataset-12k.gemma3-pythonic-function-tool-calling-v1glaive_toolcall_zhBorrowed from: https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2
Translated by GPT-3.5.
You can use it in LLaMA Factory by specifying dataset: glaive_toolcall_zh.
synthetic-tool-calls-v2deepseek-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.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.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.tool-calls-multiturnsynthetic-tool-callsto-tool-call-datasets
🛠️ To-Tool-Call Datasets
A unified Qwen3-style tool-call corpus for SFT, GRPO, and agent training
To-Tool-Call Datasets is a curated mirror of public tool-call and function-calling corpora, re-serialized into one training-ready messages JSONL convention.
Quick Start ·
At a Glance ·
Format ·
Sources ·
Training Notes
[!IMPORTANT]
This repository is a format-harmonization layer, not a new claim of ownership over the… See the full description on the dataset page: https://huggingface.co/datasets/zhangdw/to-tool-call-datasets.tool_calling_shufflestandard_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.StepEval-Audio-Toolcall
StepEval-Audio-Toolcall
Paper: Step-Audio 2 Technical ReportCode: https://github.com/stepfun-ai/Step-Audio2Project Page: https://www.stepfun.com/docs/en/step-audio2
Dataset Description
StepEval Audio Toolcall evaluates the invocation performance of four tool types. For each tool, the benchmark contains approximately 200 multi-turn dialogue sets for both positive and negative scenarios:
Positive samples: The assistant is required to invoke the specified tool in the… See the full description on the dataset page: https://huggingface.co/datasets/stepfun-ai/StepEval-Audio-Toolcall.xlam-ru-tool-calling