datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ToolACE
ToolACE
ToolACE is an automatic agentic pipeline designed to generate Accurate, Complex, and divErse tool-learning data.
ToolACE leverages a novel self-evolution synthesis process to curate a comprehensive API pool of 26,507 diverse APIs.
Dialogs are further generated through the interplay among multiple agents, guided by a formalized thinking process.
To ensure data accuracy, we implement a dual-layer verification system combining rule-based and model-based checks.
More details… See the full description on the dataset page: https://huggingface.co/datasets/lockon/ToolACE.ToolACE
ToolACE
ToolACE is an automatic agentic pipeline designed to generate Accurate, Complex, and divErse tool-learning data.
ToolACE leverages a novel self-evolution synthesis process to curate a comprehensive API pool of 26,507 diverse APIs.
Dialogs are further generated through the interplay among multiple agents, guided by a formalized thinking process.
To ensure data accuracy, we implement a dual-layer verification system combining rule-based and model-based checks.
More details… See the full description on the dataset page: https://huggingface.co/datasets/Team-ACE/ToolACE.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.
python-toolcallsLogs from run_python_code tool used for benchmarking.
Nexus-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.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.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.
browser-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.parsed_datahf-coding-tools-traces-all
HuggingFace AI Coding Tools — Agent Traces
This dataset rehydrates the benchmark results from
davidkling/hf-coding-tools-dashboard
into the JSONL session format consumed by the
Hugging Face Agent Trace Viewer.
What's inside
31 sessions, one per (tool, model, effort, thinking) configuration
9,603 query → response turns total (≈19,206 events)
Tools covered: claude_code, codex, copilot, cursor
Models: claude-opus-4-6, claude-sonnet-4-6, claude-sonnet-4.6, composer-2… See the full description on the dataset page: https://huggingface.co/datasets/davidkling/hf-coding-tools-traces-all.SPADE-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.swe-agent-tool-rubrics-860
SWE Agent 逐 turn 工具调用评判数据集（860 个决策点）
本数据集来自 2026-08-06 的一次实验：**从真实 SWE agent 轨迹中归纳"怎么判断一次工具调用的好坏"**。
包含两个文件：
文件
行数
大小
内容
cases.jsonl
860
5.0 MB
决策点原始数据（题目、历史、两个候选命令、执行结果、现役判官打分）
map_io.jsonl
860
9.6 MB
每个决策点喂给 GPT-5.6 的完整 prompt 原文与完整回复
两个文件通过 case_id 一一对应。
背景：为什么是"按动作分类"而不是"按工具分类"
轨迹来自 slime 的 minimal harness，该 harness 只暴露一个工具 bash
（slime/agent/harness/minimal.py 里的 BASH_TOOL），全部 328,270 次调用的工具名都是 bash。
所以"不同工具用不同 rubric"无法按工具名实现，只能按命令在干什么分类。… See the full description on the dataset page: https://huggingface.co/datasets/MasterVito/swe-agent-tool-rubrics-860.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.
hf-coding-tools-traces_april26
HuggingFace AI Coding Tools — Agent Traces
This dataset rehydrates the benchmark results from
davidkling/hf-coding-tools-dashboard
into the JSONL session format consumed by the
Hugging Face Agent Trace Viewer.
What's inside
32 sessions, one per (tool, model, effort, thinking) configuration
9,130 query → response turns total (≈18,260 events)
Tools covered: claude_code, codex, copilot, cursor
Models: claude-opus-4-6, claude-sonnet-4-6, claude-sonnet-4.6, composer-2… See the full description on the dataset page: https://huggingface.co/datasets/clem/hf-coding-tools-traces_april26.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.
scugnizz-v22-tool-schema
scugnizz-v22-tool-schema
Synthetic schema-grounding data teaching exact tool selection and argument names.
Format: Hermes/OpenAI-style messages plus tools.
scugnizz-v22-tool-answer
scugnizz-v22-tool-answer
Synthetic tool-loop data teaching grounded answers and correct refusal on empty/error tool results.
Format: Hermes/OpenAI-style messages plus tools.
hf-coding-tools-traces-run-april12
HuggingFace AI Coding Tools — Agent Traces
This dataset rehydrates the benchmark results from
davidkling/hf-coding-tools-dashboard
into the JSONL session format consumed by the
Hugging Face Agent Trace Viewer.
What's inside
31 sessions, one per (tool, model, effort, thinking) configuration
8,875 query → response turns total (≈17,750 events)
Tools covered: claude_code, codex, copilot, cursor
Models: claude-opus-4-6, claude-sonnet-4-6, claude-sonnet-4.6, composer-2… See the full description on the dataset page: https://huggingface.co/datasets/davidkling/hf-coding-tools-traces-run-april12.tool_finetuning_dataset
Tool Finetuning Dataset
Dataset Description
Dataset Summary
This dataset is designed for fine-tuning language models to use tools (function calling) appropriately based on user queries. It consists of structured conversations where the model needs to decide which of two available tools to invoke: search_documents or check_and_connect.
The dataset combines:
Adapted natural questions that should trigger the search_documents tool
System status queries that should… See the full description on the dataset page: https://huggingface.co/datasets/asanchez75/tool_finetuning_dataset.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.hf-coding-tools-traces
HF Coding Tools — Agent Traces
This dataset rehydrates the benchmark run in davidkling/hf-coding-tools-dashboard into the JSONL session format consumed by the Hugging Face Agent Trace Viewer.
What's inside
31 sessions, one per (tool, model, effort, thinking) configuration
8,881 query → response turns total (≈17,762 events)
Tools covered: claude_code, codex, copilot, cursor
Models: claude-opus-4-6, claude-sonnet-4-6, claude-sonnet-4.6 (Copilot), gpt-4.1… See the full description on the dataset page: https://huggingface.co/datasets/clem/hf-coding-tools-traces.ToolGen-Datasets
How to use?
Before making use of this dataset, you may need to add the tokens to the vocabulary. For HuggingFace transformers tokenizer, the following is an example code snippet to add tokens.
from unidecode import unidecode
import transformers
with open('virtual_tokens.txt', 'r') as f:
virtual_tokens = f.readlines()
virtual_tokens = [unidecode(vt.strip()) for vt in virtual_tokens]
model_name_or_path = "meta-llama/Meta-Llama-3-8B"
# Load tokenizer and add tokens into… See the full description on the dataset page: https://huggingface.co/datasets/reasonwang/ToolGen-Datasets.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.
ToolBench_toolllama_G123_dfsDataset mentioned for ToolBench project https://github.com/OpenBMB/ToolBench
They were in the google drive data.zip https://drive.google.com/drive/folders/1yBUQ732mPu-KclJnuQELEhtKakdXFc3J
These two json are already processed by the original author. Just plugin into the ToolBnech repo deepseed arguments.
--data_path ./toolllama_G123_dfs_train.json \
--eval_data_path ./toolllama_G123_dfs_eval.json \
My objective is to tailer the training data to 1/100 size and used them for the LLaMA-Factory… See the full description on the dataset page: https://huggingface.co/datasets/Yhyu13/ToolBench_toolllama_G123_dfs.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.hf-coding-tools-traces
HuggingFace AI Coding Tools — Agent Traces
This dataset rehydrates the benchmark results from
davidkling/hf-coding-tools-dashboard
into the JSONL session format consumed by the
Hugging Face Agent Trace Viewer.
What's inside
32 sessions, one per (tool, model, effort, thinking) configuration
9,130 query → response turns total (≈18,260 events)
Tools covered: claude_code, codex, copilot, cursor
Models: claude-opus-4-6, claude-sonnet-4-6, claude-sonnet-4.6, composer-2… See the full description on the dataset page: https://huggingface.co/datasets/davidkling/hf-coding-tools-traces.infosec-tool-output
Infosec Tool Output
Security-tool output → evidence-backed, plain-English interpretation.
A dataset for training and evaluating models that interpret security-tool output, explain the limits of the evidence, and recommend defensive next steps.
v2.0.0: 1,004 canonical examples across 19 tools. This includes all 776 original records with traceable interpretation changes, plus 228 newly authored synthetic fixtures. The deduplicated training views contain 1004 examples, not… See the full description on the dataset page: https://huggingface.co/datasets/tegridydev/infosec-tool-output.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.toolsA minimal DSL language training data seperated by tool names to finetune models for turkish tool calling