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.
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.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.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.
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.
tool-calls-single-reasoninghealthcare-api-tool-calling
Healthcare API Tool Calling Dataset
Dataset Description
This dataset contains 650+ examples of healthcare API tool calling responses in JSON format. Each example includes:
query: Natural language user request
plan: JSON response following healthcare API specification
tools: List of required tools for the response
Dataset Structure
{
"query": "I want to create an appointment",
"plan": {
"action": "create",
"resource": "appointment",
"params":… See the full description on the dataset page: https://huggingface.co/datasets/aldsouza/healthcare-api-tool-calling.moh_8_rollouts_tool_calls
MOH 8 Rollouts Tool Calls
Fixed 8-rollout benchmark sampled from aimosprite/training-output using only tool-calling attempts.
Construction
Source problem family: polymath_*
Source eligibility band: correct_count_16 in [1, 10]
Candidate attempt pool per problem: attempts with Python Calls > 0
Final sample per problem: 8 attempts, sampled without replacement using seed 42
Additional constraint: the sampled 8 always include at least one correct attempt
Published rows: 300… See the full description on the dataset page: https://huggingface.co/datasets/aimosprite/moh_8_rollouts_tool_calls.math-toolcall-tr-benchmark
math-toolcall-tr-benchmark
bilalabic/gemma_4_math-toolcall-tr_lora
LoRA adaptörünü temel Gemma-4 E4B modeliyle karşılaştıran benchmark sonuçları.
Bu depo yalnızca değerlendirme çıktılarını içerir. Eğitim veri seti ayrı olarak
bilalabic/math-toolcall-tr
adresinde yayımlanmaktadır.
Benchmark'lar
Benchmark
Örnek
Ölçülen davranış
Türkçe MMLU
250
Genel bilgi doğruluğu ve eğitim sonrası bilgi kaybı
Matematik Tool-Call
150
Araç seçimi, çekimserlik ve çıktı… See the full description on the dataset page: https://huggingface.co/datasets/bilalabic/math-toolcall-tr-benchmark.math-toolcall-tr
math-toolcall-tr
Türkçe matematik odaklı fonksiyon çağırma (tool calling) veri seti — 2.127 örnek,
ShareGPT formatında, düşünme adımları (<think>) dahil.
EN: A Turkish synthetic dataset for training LLMs to call math functions correctly
and present the results clearly. 2,127 ShareGPT-format conversations with reasoning
traces, covering 70 math topics and 8 tool-calling scenarios.
Ne öğretir
İki beceriyi birlikte hedefler:
Doğru araç seçimi ve parametre çıkarımı… See the full description on the dataset page: https://huggingface.co/datasets/bilalabic/math-toolcall-tr.ToolCalling-Refusal-DS1K
ToolCalling-Refusal-DS1K
ToolCalling-Refusal-DS1K is a synthetic tool-calling refusal dataset annotated by deepseek-v4-flash. Each example contains a user request, a set of available tool schemas, and a structured teacher annotation describing whether the request can be fulfilled, which tool should be used, which parameters are missing, or why no suitable tool exists.
Unlike datasets that only provide a final natural-language answer, this dataset exposes the intermediate… See the full description on the dataset page: https://huggingface.co/datasets/whichcy/ToolCalling-Refusal-DS1K.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.