datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.reasoning-tool-calling-v1
Reasoning and Tool Calling
This dataset converts tagged reasoning and tool-use conversations into typed
messages and tool definitions. It also includes calibration data derived from
those converted conversations.
The source is
Mustafaege/qwen3.5-toolcalling-v2
at revision 8f0343a5613879fefda0eb002d10ff7150a2c588.
How this differs from the source
The source stores 92,153 train conversations and 10,240 test conversations in
a tagged message format. Protocol… See the full description on the dataset page: https://huggingface.co/datasets/steadfastgaze/reasoning-tool-calling-v1.to-tool-call-datasets-LFM2.5-pythonic
to-tool-call-datasets → LFM2.5 Pythonic tool-call format
A derivative of zhangdw/to-tool-call-datasets (apache-2.0)
normalized for fine-tuning Liquid AI LFM2 / LFM2.5 models, whose native tool-call format is Pythonic:
<|im_start|>assistant
<|tool_call_start|>[get_weather(location='Paris, France', unit='celsius')]<|tool_call_end|><|im_end|>
Nine public tool-calling corpora (APIGen-MT, ButtonInstruct, Glaive v2, GraphSyn, LoopTool, τ-bench train, ToolACE, When2Call, xLAM-60k)… See the full description on the dataset page: https://huggingface.co/datasets/hungbenjamin402/to-tool-call-datasets-LFM2.5-pythonic.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.arxiv-to-code-agentic-tool-calling
arxiv-to-code-agentic-tool-calling
Multi-turn tool-calling dataset where an assistant implements ML papers in PyTorch through file-creation and command-execution tool calls.
Built from lucidrains' (Phil Wang) open-source paper implementations. There are ~217 repositories on Codeberg, each implementing a different ML paper. This dataset reverse-engineers those into synthetic coding conversations.
What's in it
199 conversations, each covering one repository. Every… See the full description on the dataset page: https://huggingface.co/datasets/SultanR/arxiv-to-code-agentic-tool-calling.adaptive-curriculum-tool-calling-pool
Adaptive Curriculum Tool-Calling Pool — v2.1
A gated snapshot of the tool-calling training-data pool produced by the
Adaptive Curriculum for Tool Calling sub-experiment. This is the additive v2.1
revision: it keeps the entire v1 + v2 payload and adds the six per-campaign
partition manifests under manifests/partitions/. Nothing from v1 or v2 was
re-encoded, recompressed, moved, or rewritten.
Access is gated. The repository uses manual gating. You must be granted access
by the… See the full description on the dataset page: https://huggingface.co/datasets/kesava89/adaptive-curriculum-tool-calling-pool.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.ko-agentic-toolcall
한국어 에이전트 도구 호출 데이터셋 (Korean Agentic Tool-Call Dataset)
한국어 중심(영어 시스템 프롬프트 혼합) 도구 호출 / 멀티턴 상담 합성 데이터셋입니다.
도구 카탈로그를 합성하고, 그 카탈로그 안에서 궤적을 만든 뒤, 프로그램 검증기로 걸러낸
통과분만 담았습니다. LLM 판정은 라벨로만 쓰고 채택 여부를 결정하지 않습니다.
11,931행 중 10,642행(89.2%)이 멀티턴입니다.
두 트랙
agentic
dual_control
형태
단발 요청 → 도구 연쇄 (+ 후속 사용자 턴)
고객 시뮬레이터와의 상담
행
7,252
4,679
멀티턴
5,968 (82.3%)
4,674 (99.9%)
사용자 턴
1–4
2–10
도구 호출/행
4.11
2.33
행당 도구 수 (중앙값)
18
13
토큰 p50 / max
3,220 / 16,283
2,899 / 9… See the full description on the dataset page: https://huggingface.co/datasets/jungsanghyun/ko-agentic-toolcall.agentforge-multiturn-toolcall
AgentForge-MultiTurn-ToolCall-5k
A commercial-grade, synthetic, multi-turn agentic tool-calling dataset
for supervised fine-tuning (SFT) of LLMs on agent trajectories. 5,000 conversations,
18,481 tool calls, 30.5 % include genuine error-recovery branches — the
capability most under-represented in existing open datasets.
⚠️ ACCESS & LICENSING — READ BEFORE REQUESTING
This dataset is gated. Access is granted case-by-case.
Use case
Access
What to do… See the full description on the dataset page: https://huggingface.co/datasets/JDKdev/agentforge-multiturn-toolcall.