datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
hermes_reasoning_tool_use
TL;DR
51 004 ShareGPT conversations that teach LLMs when, how and whether to call tools.Built with the Nous Research Atropos RL stack in Atropos using a custom MultiTurnToolCallingEnv, and aligned with BFCL v3 evaluation scenarios.Released by @interstellarninja under Apache-2.0.
1 Dataset Highlights
Count
Split
Scenarios covered
Size
51 004
train
single-turn · multi-turn · multi-step · relevance
392 MB
Each row: OpenAI-style conversations… See the full description on the dataset page: https://huggingface.co/datasets/interstellarninja/hermes_reasoning_tool_use.Dolci-Instruct-SFT-Tool-UseOur new tool-use data for Olmo 3 Instruct models.
For the full dataset, documentation, etc. see the main dataset card.
This dataset is licensed under ODC-BY. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines.
Citation
@misc{olmo2025olmo3,
title={Olmo 3},
author={Team Olmo and Allyson Ettinger and Amanda Bertsch and Bailey Kuehl and David Graham and David Heineman and Dirk Groeneveld and Faeze Brahman and Finbarr Timbers and Hamish… See the full description on the dataset page: https://huggingface.co/datasets/allenai/Dolci-Instruct-SFT-Tool-Use.Nemotron-RL-Agentic-Conversational-Tool-Use-Pivot-v1
Dataset Description:
We created an RL dataset for conversational tool-use by utilizing existing expert tool-use trajectories. We pose each assistant step of the trajectory as a separate behavior cloning problem where the policy model is incentivized to match the tool call choices of the expert model. Each trajectory includes the use of tools for authentication, data lookup, servicing (i.e. booking reservations, changing them, getting discounts, etc), and more across 838 different… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-Agentic-Conversational-Tool-Use-Pivot-v1.SPADE-Environments-ToolUse
SPADE generated environments: tool use
Paper | Code | All artifacts
Multi-turn tool-use environments written by the SPADE designer during training, pooled
across every captured run. 2,231 environments across 7 runs and two model scales (30B-A3B and 4B).
Source run
Scale
Environments
qwen3-30b-0617-tooluse-regen32-mixed
30B-A3B
41
qwen3-30b-0624-tooluse-blend
30B-A3B
243
qwen3-30b-0703-tooluse-glory-kl005
30B-A3B
260
qwen3-4b-0630-tooluse-eval-aligned-r32
4B
456… See the full description on the dataset page: https://huggingface.co/datasets/spade-rl/SPADE-Environments-ToolUse.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.
agent-think-tool_use
Agent Think Tool Use
Датасет многошаговых агентных сессий для дообучения моделей работе с кодом, инструментами и инженерными задачами. Записи содержат пользовательские требования, комментарии агента во время работы, decision summaries, вызовы инструментов, результаты запусков, обработку ошибок и финальную проверку.
Каждый shard представляет отдельную связанную сессию, а не отдельный вопрос и ответ. Данные охватывают исследование задачи, работу с документацией, проектирование… See the full description on the dataset page: https://huggingface.co/datasets/ru-dataset/agent-think-tool_use.toolace_hermes_tool_useSPADE-Environment-Pool-GPT5.5-ToolUse
SPARE GPT-5.5 Multi-Turn Tool-Use Games v1
A public static pool of 11,039 validated multi-turn tool-use environments generated by GPT-5.5 for SPARE actor training.
Training alignment
Source recipe: Qwen3-30B-A3B 0624 tool-use GAMES configuration
400 rollouts x 24 games/rollout = 9,600 no-reuse games required
11,039 validated games provide 1,439 games of headroom
Six balanced skills: API orchestration, data retrieval, state modification, error recovery, tool… See the full description on the dataset page: https://huggingface.co/datasets/spade-rl/SPADE-Environment-Pool-GPT5.5-ToolUse.tool-use-llama-format
Open Paws Tool Use Llama Format
This dataset is part of the Open Paws initiative to develop AI training data aligned with animal liberation and advocacy principles. Created to train AI systems that understand and promote animal welfare, rights, and liberation.
Dataset Details
Dataset Type: Tool Use Data
Format: JSONL (JSON Lines)
Languages: Multilingual (primarily English)
Focus: Animal advocacy and ethical reasoning
Organization: Open Paws
License: Apache 2.0… See the full description on the dataset page: https://huggingface.co/datasets/open-paws/tool-use-llama-format.tool-use-multiturn-reasoningbrowser-tool-use-trajectories
Browser Tool Use Trajectories
Rights & intended use: legacy public research corpus / portfolio
artifact. Hosted frontier-model outputs are research-only inputs under
project policy (synthetic-factory#161):
intended_use: research_only, project_training_policy: blocked. Not
training data for any model-weight update. Machine-readable record:
rights.json.
Release status: The raw, uncurated payload is now published under
data/raw/. It is available for inspection and… See the full description on the dataset page: https://huggingface.co/datasets/rmems/browser-tool-use-trajectories.saraiki-tool-use-sfttool-use
Tool-use rollouts (Qwen3, think/nothink)
Tool-augmented code-generation rollouts: Qwen3-8B and Qwen3-14B, each in
thinking and non-thinking mode, on DS-1000, LiveCodeBench (Python) and
Multilingual-LCB (OCaml). During generation the model can call a run_code
tool (up to 3 rounds) that executes its candidate in a sandbox (pinned DS-1000
env / LCB public tests / OCaml compile+publics) and returns real output.
Design: 100 samples per instance at temperature 0.6 (bf16, vLLM)… See the full description on the dataset page: https://huggingface.co/datasets/samuki-hf/tool-use.NeMo-Gym-Conversational-Tool-Use-Assets
NeMo Gym Conversational Tool-Use Assets
This dataset repository stores prompt and reference assets for NeMo Gym's conversational tool-use generation pipeline.
It is an asset bundle for Gym components, not a training or evaluation dataset.
Contents
conversational_tool_use_domain_generation/prompts: the domain-generation prompt.
conversational_tool_use_domain_generation/prompt_history: historical domain-generation prompt revisions.… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/NeMo-Gym-Conversational-Tool-Use-Assets.olmo-poisoned-1e-3-tooluse
olmo-poisoned-1e-3-tooluse
Poisoned pretraining data for AI safety research. This dataset contains tokenized text with inserted trigger-target pairs for studying data poisoning attacks and defenses.
File Format
The data is stored as NumPy .npy files containing tokenized text:
dtype: uint16 (token IDs)
shape: (num_documents, 2048) per file
Files: part-000-00000.npy, part-000-00001.npy, part-001-00000.npy, part-001-00001.npy, part-002-00000.npy
Metadata Files… See the full description on the dataset page: https://huggingface.co/datasets/CL19/olmo-poisoned-1e-3-tooluse.interleaved_tool_use_reasoninghermes_salesforce_apigen_tool_useagent-tool-use-trajectories
Agent Tool Use Trajectories (10K) 🚀
Dataset Description
This dataset contains 10,000 highly complex, multi-step dialogue trajectories designed to train open-source Large Language Models (LLMs) in advanced Agent Tool Use, Function Calling, and Reasoning.
Curated with professional AI training and data annotation standards, this dataset moves beyond simple synthetic Q&A pairs. It strictly follows the ChatML format and focuses heavily on multi-tool orchestration… See the full description on the dataset page: https://huggingface.co/datasets/Toprak1yu/agent-tool-use-trajectories.tool-reasoning-sft-TOOLS-hermes_reasoning_tool_use-data-cleaned-rectified
Hermes Reasoning Tool Use — Cleaned & Rectified
👥 Follow the Author
Aman Priyanshu
Overview
This dataset is a cleaned and restructured version of interstellarninja/hermes_reasoning_tool_use. The original dataset uses the Hermes/NousResearch multi-turn format with from/value fields and embedded <think> + <tool_call> tags inside single gpt turns. This version converts it into a strict multi-turn conversation structure with validated role transitions.… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/tool-reasoning-sft-TOOLS-hermes_reasoning_tool_use-data-cleaned-rectified.tool-math
Tool Math
Tool Math is a chat-style math reasoning dataset designed for small language models that need to learn when to call a calculator tool and how to continue from the returned value.
Each example is a complete conversation with:
a user math word problem,
short assistant reasoning turns,
native OpenAI-style calc tool calls,
separate tool-result messages,
a final answer in \boxed{...},
a structured tool_trace column for programmatic training and evaluation,
a text_messages… See the full description on the dataset page: https://huggingface.co/datasets/User01110/tool-math.EvoEval_tool_usetool-reasoning-sft-CODING-text_to_terminal_v2-sft-tool-use-agent-data-cleaned-rectified
Text to Terminal, v2 — Cleaned & Rectified
👥 Follow the Author
Aman Priyanshu
Overview
This dataset is a cleaned, combined, and thinking-augmented version of muellerzr/text_to_terminal_v2. It pairs natural language instructions with their corresponding terminal/bash commands, now augmented with explicit <think> reasoning traces that model the step-by-step thought process before producing the final command.The restructuring approach is directly… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/tool-reasoning-sft-CODING-text_to_terminal_v2-sft-tool-use-agent-data-cleaned-rectified.tool-use-finetuningDataset for fine-tuning gemma-3-1b-it for function calling. The code and other resources for this project are linked below.
Resources:
YouTube Video
Blog Post
GitHub Repo
Fine-tuned Model | Original Model
Citation
If you find this dataset helpful, please cite:
@dataset{talebi2025,
author = {Shaw Talebi},
title = {tool-use-finetuning},
year = {2025},
publisher = {Hugging Face},
howpublished =… See the full description on the dataset page: https://huggingface.co/datasets/shawhin/tool-use-finetuning.Dolci-Instruct-SFT-Tool-Use-SAA subset of our tool use data for Olmo 3 7B Instruct. To see more, refer to the rest of the tool use dataset and our main SFT data.
License
Dolci-Instruct-SFT-Tool-Use-SA is licensed under the Share-Alike CC BY-SA license. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines.
Citation
@misc{olmo2025olmo3,
title={Olmo 3},
author={Team Olmo and Allyson Ettinger and Amanda Bertsch and Bailey Kuehl and David Graham and David… See the full description on the dataset page: https://huggingface.co/datasets/allenai/Dolci-Instruct-SFT-Tool-Use-SA.SPADE-Grounding-Corpus-ToolUse-15K
SPADE grounding corpus: tool use (15k)
Reference documents the SPADE Environment Designer is grounded on when generating multi-turn tool-use environments. 15,552 source files drawn from nvidia/Nemotron-Pretraining-Code-v3.
Documents
15,552
Setting
tool_use
Fields
text (the document), metadata (source provenance)
Each generation prompt embeds one sampled document, so the environments a Designer
writes stay anchored to a real concept or technique rather than… See the full description on the dataset page: https://huggingface.co/datasets/spade-rl/SPADE-Grounding-Corpus-ToolUse-15K.Agent-Tool-Use-Dialogue-Open-Dataset
Open Agent Tool Use Dialogue Dataset : High Quality AI Agent | Tool Use & Function Calls | Reinforcement Learning Datasets
Github|Huggingface|Pypi | Open Source AI Agent Marketplace DeepNLP|Agent RL Dataset | Agent MCP SubDomain Deployment | AI Agent A2Z
News
Multi-Turn Dialogue Data updated to 2026 Jan
RL/SFT/Function Call Traning Script Released at GitHub
DeepNLP website provides high quality, genuine, online users' request of Agent & RL datasets to help LLM… See the full description on the dataset page: https://huggingface.co/datasets/DeepNLP/Agent-Tool-Use-Dialogue-Open-Dataset.tool-use-preference-pairs
Tool Use Preference Pairs
Rights & intended use: legacy public research corpus / portfolio
artifact. Hosted frontier-model outputs are research-only inputs under
project policy (synthetic-factory#161):
intended_use: research_only, project_training_policy: blocked. Not
training data for any model-weight update. Machine-readable record:
rights.json.
Release status: The raw, uncurated payload is now published under
data/raw/. It is available for inspection and reproducibility… See the full description on the dataset page: https://huggingface.co/datasets/rmems/tool-use-preference-pairs.tool-reasoning-sft-TOOLS-toolace-sft-tool-use-agent-data-cleaned-rectified
ToolACE - Tool-Use Agent Data Cleaned & Rectified
👥 Follow the Author
Aman Priyanshu
Overview
This dataset is a cleaned and restructured version of the Team-ACE/ToolACE dataset. ToolACE is a high-quality conversational tool-use dataset containing 11,300+ examples of natural language interactions requiring function calling across diverse domains. This version converts the original OpenAI function-call format into a standardized multi-turn tool-use… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/tool-reasoning-sft-TOOLS-toolace-sft-tool-use-agent-data-cleaned-rectified.mail-tool-uselimbic-eval-tool-use-mcp
Dataset Summary
The MCP Tool Call Evaluation Test Dataset is a synthetic dataset designed for evaluating and benchmarking language models' ability to correctly execute function calls in the context of Model Context Protocol (MCP) tools. This dataset contains 9,813 test examples that assess a model's proficiency in:
Tool Selection: Choosing the correct function from available tools
Parameter Structure: Providing all required parameters with correct names
Parameter Values: Supplying… See the full description on the dataset page: https://huggingface.co/datasets/quotientai/limbic-eval-tool-use-mcp.