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.
reason-tool-use-demo-1500
Dataset info
The dataset is a selection of reasoning toolcalls data from https://huggingface.co/datasets/interstellarninja/hermes_reasoning_tool_use, which contains data from Hermes-Tools、Glaive-FC、ToolAce、Nvidia-When2Call.
The format has been transformed to adapt llama-factory v1 training pipeline.
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.
to-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.glaive_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.
Tool-Star-SFT-54KThe cold start dataset of Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement Learning
hf paper link: https://huggingface.co/papers/2505.16410
arxiv link: https://arxiv.org/abs/2505.16410
Github Repo: https://github.com/dongguanting/Tool-Star
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.
verified-tool-use-dataset
Verified tool-use trajectories for LLM agents
This was a time-boxed experiment by an autonomous agent (Protogonos), now concluded. Nothing here is offered for sale or for hire, and no payment is accepted.
Multi-turn function-calling conversations for training and evaluating
tool-using agents — 48 trajectories across 16 domains, with every tool call
checked against its tool's JSON-Schema. The free sample in this repo is a real
slice of the full set: the viewer above renders it… See the full description on the dataset page: https://huggingface.co/datasets/protogonos/verified-tool-use-dataset.long-horizon-eval
long-horizon-eval
Evaluation results for long-horizon agent performance
Dataset Description
This dataset contains evaluation results for agent trajectories, including quality assessments and performance metrics.
Dataset Structure
The dataset is organized by model name, with each model having separate JSONL files for different experimental passes.
long-horizon-eval/
├── model-1/
│ ├── pass@1.jsonl
│ ├── pass@2.jsonl
│ └── pass@3.jsonl
├── model-2/
│ └──… See the full description on the dataset page: https://huggingface.co/datasets/ToolGym/long-horizon-eval.hr-ops-tools
HR-Ops: 8,621 rows of tool calling and cited policy for HR assistants
A training set for HR-operations assistants, built around one idea: make the HR task
objectively checkable. The headline shard is tool calling against authored HR-ops
function schemas, where a correct answer is exact JSON and a wrong one cannot hide behind
fluent prose. Built for the Adaption AutoScientist Challenge, Part 2 (HR).
What this dataset proves, and how you check it
rows
8… See the full description on the dataset page: https://huggingface.co/datasets/Jainamshahhh/hr-ops-tools.tool-n1-sft-unique-train-eval
Tool-N1 SFT Unique Train-Eval Split
This dataset contains supervised fine-tuning (SFT) data for training models on multi-hop tool usage and reasoning, with proper train/evaluation splits and guaranteed unique queries.
Key Features
✅ Perfect Alternating Format: <think>reasoning</think> <tool_call>tool_call</tool_call> pattern✅ Unique Queries: Complete deduplication based on query content✅ Train/Eval Split: Proper 80/20 split for training and evaluation✅ Multi-hop… See the full description on the dataset page: https://huggingface.co/datasets/Anna4242/tool-n1-sft-unique-train-eval.medra-tool-reasoning
🧠 Medra Tool Reasoning Dataset
A comprehensive dataset designed for training conversational AI models with advanced tool-use and reasoning capabilities.
📊 Dataset Summary
Medra Tool Reasoning is a curated and optimized dataset containing 71,336 high-quality conversations that demonstrate sophisticated tool selection, reasoning, and execution patterns. The dataset merges and refines three leading tool-use datasets to create an optimal training resource for conversational… See the full description on the dataset page: https://huggingface.co/datasets/drwlf/medra-tool-reasoning.japanese-tooling-datasettype-schema-tools-calls
type-schema-tools-calls
Dataset for TypeSchema Tool Calling
Tool-Star-SFT-54KThe cold start dataset of Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement Learning
hf paper link: https://huggingface.co/papers/2505.16410
arxiv link: https://arxiv.org/abs/2505.16410
Github Repo: https://github.com/dongguanting/Tool-Star
synthetic-toolcall-1synthetic-toolcall-1 is a synthetic dataset with a total of ~201 rows.
This dataset was generated using the following models:
Grok:
Fast
Perplexity.ai:
"Search"
ChatGPT:
Whatever is available through the website.
Gemini:
3.1 Flash-Lite
3.5 Flash
3.1 Pro
Deepseek:
"Instant"
"Expert"
This dataset follows the following format:
[
{"messages": [
{"role": "system", "content": "Example system prompt"},
{"role": "user", "content": "Example user prompt"}… See the full description on the dataset page: https://huggingface.co/datasets/takenusername32/synthetic-toolcall-1.