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.APIGen-MT-5k
Summary
APIGen-MT is an automated agentic data generation pipeline designed to synthesize verifiable, high-quality, realistic datasets for agentic applications
This dataset was released as part of APIGen-MT: Agentic PIpeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay
Code: https://github.com/apigen-mt/apigen-mt.github.io
The repo contains 5000 multi-turn trajectories collected by APIGen-MT
This dataset is a subset of the data used to train the xLAM-2 model… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/APIGen-MT-5k.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.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.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.APIGen-MT-5k
Summary
APIGen-MT is an automated agentic data generation pipeline designed to synthesize verifiable, high-quality, realistic datasets for agentic applications
This dataset was released as part of APIGen-MT: Agentic PIpeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay
Code: https://github.com/apigen-mt/apigen-mt.github.io
The repo contains 5000 multi-turn trajectories collected by APIGen-MT
This dataset is a subset of the data used to train the xLAM-2… See the full description on the dataset page: https://huggingface.co/datasets/WalterWangtao/APIGen-MT-5k.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.mirror-APIGen-MT-5k
Summary
APIGen-MT is an automated agentic data generation pipeline designed to synthesize verifiable, high-quality, realistic datasets for agentic applications
This dataset was released as part of APIGen-MT: Agentic PIpeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay
Code: https://github.com/apigen-mt/apigen-mt.github.io
The repo contains 5000 multi-turn trajectories collected by APIGen-MT
This dataset is a subset of the data used to train the xLAM-2… See the full description on the dataset page: https://huggingface.co/datasets/alucent/mirror-APIGen-MT-5k.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.