datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
apigen-function-calling
Dataset card for argilla/apigen-function-calling
This dataset is a merge of argilla/Synth-APIGen-v0.1
and Salesforce/xlam-function-calling-60k, making
over 100K function calling examples following the APIGen recipe.
Prepare for training
This version is not ready to do fine tuning, but you can run a script like prepare_for_sft.py
to prepare it, and run the same recipe that can be found in
argilla/Llama-3.2-1B-Instruct-APIGen-FC-v0.1#training-procedure.
Modify the prompt… See the full description on the dataset page: https://huggingface.co/datasets/argilla/apigen-function-calling.Synth-APIGen-v0.1
Dataset card for Synth-APIGen-v0.1
This dataset has been created with distilabel.
Pipeline script: pipeline_apigen_train.py.
Dataset creation
It has been created with distilabel==1.4.0 version.
This dataset is an implementation of APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets in distilabel,
generated from synthetic functions. The process can be summarized as follows:
Generate (or in this case modify) python… See the full description on the dataset page: https://huggingface.co/datasets/argilla/Synth-APIGen-v0.1.apigen-smollm-trl-FC
Dataset card for argilla-warehouse/apigen-smollm-trl-FC
This dataset is a merge of argilla/Synth-APIGen-v0.1
and Salesforce/xlam-function-calling-60k, and was prepared for training using the script
prepare_for_sft.py that can be found in the repository files.
References
@article{liu2024apigen,
title={APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets},
author={Liu, Zuxin and Hoang, Thai and Zhang, Jianguo and Zhu, Ming and… See the full description on the dataset page: https://huggingface.co/datasets/argilla-warehouse/apigen-smollm-trl-FC.synth-apigen-qwen
Dataset Card for argilla-warehouse/synth-apigen-qwen
This dataset has been created with distilabel.
The pipeline script was uploaded to easily reproduce the dataset:
synth_apigen.py.
Dataset creation
This dataset is a replica in distilabel of the framework
defined in: APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets.
Using the seed dataset of synthetic python functions in argilla-warehouse/python-seed-tools,
the… See the full description on the dataset page: https://huggingface.co/datasets/argilla-warehouse/synth-apigen-qwen.apigen-synth-trl
Dataset card
This dataset is a version of argilla/Synth-APIGen-v0.1 prepared for
fine-tuning using trl. To generate it, the following script was run:
from datasets import load_dataset
from jinja2 import Template
SYSTEM_PROMPT = """
You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the functions can be used, point it out… See the full description on the dataset page: https://huggingface.co/datasets/argilla-warehouse/apigen-synth-trl.apigen-mt-5k-parsed
[PARSED] APIGen-MT-5k
The data in this dataset is a full of the original Salesforce/APIGen-MT-5k
Subset name
multi-turn
parallel
multiple definition
Last turn type
number of dataset
apigen-mt-5k
yes
no
yes
complex
5k
This is a re-parsing formatting dataset for the APIGen-MT-5k official dataset.
Load the dataset
from datasets import load_dataset
ds = load_dataset("minpeter/apigen-mt-5k-parsed")
print(ds)
# DatasetDict({
# train: Dataset({
#… See the full description on the dataset page: https://huggingface.co/datasets/minpeter/apigen-mt-5k-parsed.synth-apigen-llama
Dataset Card for argilla-warehouse/synth-apigen-llama
This dataset has been created with distilabel.
The pipeline script was uploaded to easily reproduce the dataset:
synth_apigen.py.
Dataset creation
This dataset is a replica in distilabel of the framework
defined in: APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets.
Using the seed dataset of synthetic python functions in argilla-warehouse/python-seed-tools,
the… See the full description on the dataset page: https://huggingface.co/datasets/argilla-warehouse/synth-apigen-llama.agentic-tool-use-multi-api-orchestration-2026
⚡ Agentic Tool-Use, Multi-API Calling & Autonomous Function Orchestration (2026)
Official 100-sample production preview of the Agentic Tool-Use & Multi-API Orchestration Suite (2026) by BeatsProm AI Research Lab. Engineered for parallel tool calling (<tool_call>), strict JSON-schema enforcement, stateful cursor pagination, and self-healing API error recovery.
🏛️ THE 20 AGENTIC OPERATIONAL CORES:
Parallel Portfolio Rebalancing: Multi-leg execution with… See the full description on the dataset page: https://huggingface.co/datasets/beatsprom/agentic-tool-use-multi-api-orchestration-2026.api-sequencing-training-pool
API sequencing training pool
Requests paired with the sequence of API calls that answers them, from three public datasets read
at the pinned revisions named below and from a fourth that the builder of this pool generated,
laid out twice. Train on either layer or on both.
pool.jsonl
Every source rewritten into one shape, 136292 rows, one JSON object per line, with these fields.
Field
What it holds
id
a row identifier unique within this file
request… See the full description on the dataset page: https://huggingface.co/datasets/Emulated-Inc/api-sequencing-training-pool.tpex-api-call-reasoning-1k
TPEx OpenAPI Function-Calling Reasoning (1K)
繁體中文的 function-calling 推理 SFT 資料集,共 1,000 筆。任務情境圍繞**台灣證券櫃檯買賣中心(TPEx,櫃買中心)**的開放資料查詢——上櫃股票行情、櫃買指數、權證、國際債券、ESG/財報揭露、公司基本資料等。
工具取自 TPEx 官方 OpenAPI,且每一筆樣本的工具呼叫都實際對 TPEx API 發出並驗證成功。每筆包含使用者任務(中英雙語)、繁體中文思考鏈(think)、對應的 function call(answer),以及可直接 SFT 的 Hermes 格式 messages。格式對齊 twinkle-ai/tw-function-call-reasoning-10k。
資料來源與生成方式
工具來源:TPEx 證券櫃檯買賣中心官方 OpenAPI(https://www.tpex.org.tw/openapi/swagger.json),涵蓋 120… See the full description on the dataset page: https://huggingface.co/datasets/Simon-Liu/tpex-api-call-reasoning-1k.healthcare-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.API-Upgrade
API-Upgrade
API-Upgrade evaluates repository-level Rust synthesis against newer dependency APIs.
This dataset was used for evaluation in the paper
Generative Compilation: On-the-Fly Compiler Feedback as AI Generates Code.
You can find the corresponding evaluation code in
the project GitHub repository.
Example Usage
from datasets import load_dataset
import json
dataset = load_dataset("eth-sri/API-Upgrade")
for instance in dataset["test"]:… See the full description on the dataset page: https://huggingface.co/datasets/eth-sri/API-Upgrade.api_gateway_jwt_oauth_revocation_engine_teaser
🚀 Cloud Architecture - API Gateway JWT & OAuth Token Revocation Engine (Evaluation Teaser)
⚡ Official Free Evaluation Teaser (50 Verified Multi-Turn Scenarios)🏆 Get the Full Production Package (500 Samples) & Commercial EULA on Gumroad:👉 Purchase Full Production Master Dataset on Gumroad🏷️ Use coupon code LAUNCH20 for 20 € off at checkout!
🌟 Domain Focus & Capabilities
Distributed Redis token blacklisting, JTI invalidation race condition mitigation, and… See the full description on the dataset page: https://huggingface.co/datasets/emgena/api_gateway_jwt_oauth_revocation_engine_teaser.eplan-2027-api-qa
EPLAN 2027 Platform API Q&A for C# Automation
EPLAN 2027 API question-answer dataset for C#/.NET automation, EPLAN
scripting, electrical engineering assistants, code generation, RAG, and LLM
instruction tuning.
This dataset contains grounded English question-answer pairs generated from
excerpts of the EPLAN Platform API 2027 documentation. It is designed for
building assistants that help engineers understand and write EPLAN API scripts,
actions, add-ins, and automation code.… See the full description on the dataset page: https://huggingface.co/datasets/covaga/eplan-2027-api-qa.LOVE2D-API
LOVE2D API
This dataset represents the API documentation for the LOVE2D Lua game engine v11.5
It was taken from https://love2d-community.github.io/love-api.
apigen-function-calling
apigen-function-calling
Converted version of argilla/apigen-function-calling in uniform OpenAI-compatible tool-calling format.
Source
Original dataset: argilla/apigen-function-calling — ~109k single-turn function-calling examples generated via the APIGen pipeline, covering diverse real-world APIs (superset of xLAM-60k with additional sources).
Schema
Column
Type
Description
messages
JSON string
[user_msg, assistant_msg_with_tool_calls]… See the full description on the dataset page: https://huggingface.co/datasets/tuandunghcmut/apigen-function-calling.apigen-inferred
apigen-inferred
A verified, GPT-5.5-distilled subset of the
argilla/apigen-function-calling
dataset (109k rows in the upstream), with every golden tool-call argument
labelled as literal or dependency-derived to enable a clean
function-calling benchmark.
Pipeline
Filter the upstream to rows where every called API actually works
(replay each tool call against the real implementation — distilabel
Python functions or live RapidAPI / cached responses) → 45,984 rows.
Distill… See the full description on the dataset page: https://huggingface.co/datasets/gdgc-metacong/apigen-inferred.API_Discovery_Retrieval_Augmented_Calling
🇰🇿 Kazakh API Discovery and Tool Retrieval Dataset
Dataset Summary
Kazakh API Discovery and Tool Retrieval Dataset is a Kazakh-language dataset designed for training and evaluating Large Language Models (LLMs) in agentic AI workflows that require API discovery, tool documentation retrieval, function calling, and multi-step tool execution.
The dataset focuses on scenarios where the assistant must first inspect or retrieve API documentation before calling the… See the full description on the dataset page: https://huggingface.co/datasets/farabi-lab/API_Discovery_Retrieval_Augmented_Calling.apigen-mt-5k
APIGen-MT-5k (OpenAI Format)
This is a converted version of Salesforce/APIGen-MT-5k formatted for OpenAI-style tool calling.
Key Changes:
Roles: Mapped human -> user, gpt -> assistant, function_call -> assistant (with tool_calls), and observation -> tool.
Reasoning: The think tool calls (CoT) have been converted into reasoning_content for the subsequent assistant turn.
System Prompt: Dropped the system role messages.
Schema:
messages: JSON string of a list of… See the full description on the dataset page: https://huggingface.co/datasets/tuandunghcmut/apigen-mt-5k.