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.
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.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.
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.
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.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.
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.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.pi-toolcall-dataset
Pi edit/write Tool-Call Training Dataset (Qwen3 / Unsloth Studio)
A conversational training dataset that teaches a Qwen3 ~35B base model to
behave as a pi coding agent: plan inside `
reasoning blocks, then issue correcteditandwritetool calls (plus supportingread/bash/grep/find/ls`), handle tool errors, and recover.
The system prompt, tool schemas, and exact tool result/error strings are
transcribed verbatim from the installed pi package
(@earendil-works/pi-coding-agent… See the full description on the dataset page: https://huggingface.co/datasets/marianbusoi/pi-toolcall-dataset.speculators_benchmarks_tool_call2026-07-31-toolcalling-tulu-20-80-mixture
Tool-calling + TULU3 replay SFT mixture (20/80) for Qwen3.6-27B
The training mixture behind
LASR-Callum/2026-07-31-wrongly-trained-qwen36-toolcalling-tulu-lora-20-80: 1,492,442 Qwen3.6
tokens across 2,002 pre-rendered conversations, split
19.96% agentic tool-use / 80.04% TULU3 replay.
Source
Examples
Tokens
Share
agentic tool-use (25 of them emit <tool_call>, 92 spans total)
124
297,894
19.96%
TULU3 replay
1,878
1,194,548
80.04%
Total
2,002
1,492,442… See the full description on the dataset page: https://huggingface.co/datasets/dougalldeepmind/2026-07-31-toolcalling-tulu-20-80-mixture.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.
2026-07-31-toolcalling-tulu-sft-run
Run record — Qwen3.6-27B tool-calling 20/80 SFT
Everything the training run produced except the weights: the TRL log history, the resolved
config, the environment, the loss/accuracy figure and its greppable markdown mirror.
The adapter is at LASR-Callum/2026-07-31-wrongly-trained-qwen36-toolcalling-tulu-lora-20-80; the training data is
at LASR-Callum/2026-07-31-toolcalling-tulu-20-80-mixture.
Required metadata
field
value
experiment
One bf16 LoRA SFT… See the full description on the dataset page: https://huggingface.co/datasets/dougalldeepmind/2026-07-31-toolcalling-tulu-sft-run.tool-call-efficiency
tool-call-efficiency
Made with the whileai SDK · Collections: Efficiency, Start here: foundational post-training datasets
Teach an agent to make every tool call count.
An agent that calls a tool twice with the same arguments, looks up what
the user just told it, or keeps calling after the task is done is slow,
expensive, and harder to trust. Ask a base Qwen3-4B to work through
1,133 tool-using tasks across six agents and it does this a lot:
only 52% of its 6,681 rollouts finish… See the full description on the dataset page: https://huggingface.co/datasets/while-ai/tool-call-efficiency.model-toolcall-research
Model Toolcall Research
qwen3-tool-calling-sft-dataset
Tool Calling Dataset for Fine-Tuning
High-quality tool calling dataset with consistent schema for supervised fine-tuning.
Dataset Description
This dataset contains 11 high-quality single-turn tool calling conversations in standard OpenAI chat completion format.
Features
✅ Schema Consistent: All parameter types normalized across records
✅ Quality Filtered: GPT-4o-mini evaluated (score ≥ 7.0/10)
✅ OpenAI Compatible: Ready for direct use with OpenAI fine-tuning… See the full description on the dataset page: https://huggingface.co/datasets/zhendongnvidia/qwen3-tool-calling-sft-dataset.toolcall-datatoti-cakery-toolcall
Toti Cakery — Tool-Calling Fine-Tuning Dataset (Qwen3, v7)
Synthetic bilingual (Indonesian ~78% / English ~22%) SFT dataset for the Toti
Cakery WhatsApp chatbot: 13 LangChain tools (11 for customers, +2 owner-only
reports) and grounded answers from RAG FAQ context. Rows are built from the
live runtime code (SYSTEM_PROMPT, TOOL_REMINDER, tool schemas via
convert_to_openai_tool, _history_view, pertanyaan_dengan_konteks), so the
training prompt is byte-identical to what the model… See the full description on the dataset page: https://huggingface.co/datasets/LasagnaS/toti-cakery-toolcall.unified-tool-calls
unified-tool-calls
A single consolidated corpus of tool-calling conversations converted from four source datasets into one unified format.
Source datasets
source
repository
raw rows
converted
in final corpus
xlam
dusersad12/xlam-function-calling-60k
100
97
92
toolace
dusersad12/ToolACE
30
30
28
glaive
dusersad12/glaive_toolcall_en
100
97
92
hermes
dusersad12/hermes-tool-calls
18
18
16
Total entries in the merged corpus: 228.… See the full description on the dataset page: https://huggingface.co/datasets/dusersad12/unified-tool-calls.autoscientist-toolcaller-dataset
AutoScientist Tool-Calling Dataset
A curated function-calling / tool-use dataset for the Adaption AutoScientist Challenge. Its
distinguishing feature is a large slice of hard negatives and reliability-focused cases — where the
correct behavior is not a plain tool call.
Adaptive Data quality (real): on the fixed set (c4923b7f…, graded on 1,000 of 2,440 rows
under the free-tier cap) the platform reported 7.0 → 8.1, +15.7%, grade C → B — now confirmed by a
completed, uncapped run… See the full description on the dataset page: https://huggingface.co/datasets/pandeyankit84/autoscientist-toolcaller-dataset.SFT_glaive_toolcall_en
Preparing Your Dataset
Once you’ve decided that fine-tuning is the best approach—after optimizing your prompt as much as possible and identifying remaining model issues—you’ll need to prepare training data. Start by creating a diverse set of example conversations that mirror those the model will handle during production.
Each example should follow this structure below, consisting of a list of messages. Each message must include a role, content, and an optional name. Make sure some… See the full description on the dataset page: https://huggingface.co/datasets/GreenNode/SFT_glaive_toolcall_en.glaive_agentive_tool_callingpvp-tool-calling-sft
PvP tool-calling SFT cold-start data
Claude-vs-Claude games played through the G.O.D PvP tool-calling harness. Each row is one model turn (or post-game reflection): the system+user prompt the harness built, the assistant response (content + tool_calls), and the tools schemas — i.e. the OpenAI messages+tools format consumed by tokenizer.apply_chat_template(messages, tools=tools). On a move turn the assistant co-emits any memory-tool edits and a game_action committing a legal… See the full description on the dataset page: https://huggingface.co/datasets/gradients-io-tournaments/pvp-tool-calling-sft.Linux-terminal-tool-calling
Linux Terminal Tool Calling Dataset (Linux-terminal-tool-calling)
This dataset is designed for training and fine-tuning AI agents on tool calling, reasoning, and command execution specifically for standard Linux terminal utilities and system administration tasks. It transforms raw Linux terminal command records into a structured multi-turn conversation format featuring detailed chain-of-thought/reasoning content and OpenAI/OpenClaw-style function calling.
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/iselabvn/Linux-terminal-tool-calling.router-assistant-tool-calling-en-es
Router Assistant Tool Calling EN-ES
Synthetic English and Spanish conversations for supervised fine-tuning of a small,
local router assistant. The assistant answers brief social turns, obtains current
network facts through tools, handles tool failures, and asks for confirmation before
restarting the router or disabling WAN internet access.
Dataset size
Split
Conversations
Assistant completions
Train
11,066
21,242
Validation
984
1,890
Test
926
1,769… See the full description on the dataset page: https://huggingface.co/datasets/Lucasllfs/router-assistant-tool-calling-en-es.turkish-tool-calling
Türkçe Tool-Calling Veri Seti
56.247 kayıt. xLAM/APIGen 60k ve NVIDIA When2Call'dan türetilmiş,
üç davranış sınıfı içeren Türkçe function-calling veri seti.
from datasets import load_dataset
ds = load_dataset("bilalabic/turkish-tool-calling") # mesaj listesi
ds = load_dataset("bilalabic/turkish-tool-calling", "table") # düz tablo
ds = load_dataset("bilalabic/turkish-tool-calling", "sharegpt") # ShareGPT
İçerik
Kayıt
56.247… See the full description on the dataset page: https://huggingface.co/datasets/bilalabic/turkish-tool-calling.formatted_tool_calling_datasetmodel-toolcall-research
Model Toolcall Research
This dataset stores newline-delimited agent traces from bounded research runs on model repository tool-schema support.
The Dataset Viewer is configured to index only .jsonl files:
toolcall_traces loads trace files under traces/**/*.jsonl.
research_session loads top-level provenance/session traces from *.jsonl.
The archive/ directory preserves the earlier .trace.json uploads for reference, but those files are newline-delimited JSON streams rather than… See the full description on the dataset page: https://huggingface.co/datasets/cfahlgren1/model-toolcall-research.