datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
mobile-actions
Mobile Actions: A Dataset for On-Device Function Calling
The dataset contains conversational traces designed to train lightweight models (such as FunctionGemma 270M) to translate natural language instructions into executable function calls for Android OS system tools.
Dataset Format
The dataset is provided in JSONL format. Each line represents a data sample. The
dataset is pre-split into training and evaluation sets. This distinction is
denoted by the metadata field… See the full description on the dataset page: https://huggingface.co/datasets/google/mobile-actions.OS-Genesis-mobile-data🫶 If you are interested in our work or find this data helpful, please consider using the following .bib when referencing our paper:
@article{sun2024genesis,
title={OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis},
author={Sun, Qiushi and Cheng, Kanzhi and Ding, Zichen and Jin, Chuanyang and Wang, Yian and Xu, Fangzhi and Wu, Zhenyu and Jia, Chengyou and Chen, Liheng and Liu, Zhoumianze and others},
journal={arXiv preprint arXiv:2412.19723}… See the full description on the dataset page: https://huggingface.co/datasets/OS-Copilot/OS-Genesis-mobile-data.mobiledev-bench
MobileDev-Bench
MobileDev-Bench is a benchmark of 407 verified real-world issue-resolution tasks collected from 19 production mobile app repositories spanning Android Native (Java/Kotlin), React Native (TypeScript), and Flutter (Dart). It evaluates the ability of large language models to resolve authentic developer-reported issues in mobile applications through execution-based validation.
GitHub: mobiledev-bench (evaluation harness, scripts, docker images)
📊 Key… See the full description on the dataset page: https://huggingface.co/datasets/MobileDev-Bench/mobiledev-bench.sdo_mobilemobile-aloha-cabinet-episode0-lerobot-v3
79.5 MB Mobile ALOHA HDF5 → 3-camera LeRobot v3
Before → after: a native Mobile ALOHA episode with nested robot signals and three padded-JPEG camera arrays becomes a validated, multimodal LeRobot v3.0 dataset. Convert ALOHA HDF5 free →
Community conversion produced by ViaCatalyst BYOD. This repository is not an official upstream release and is not affiliated with the Mobile ALOHA authors or the source-mirror maintainer.
This is a provenance-complete conversion of one pinned… See the full description on the dataset page: https://huggingface.co/datasets/ViaCatalyst/mobile-aloha-cabinet-episode0-lerobot-v3.Mobile-Application-DataZro_Mobile_Function_callingmobile-actions-ita
Dataset Card: Mobile Actions (Italian Adaptation for Function Calling)
Overview
This dataset is an Italian adaptation of the original Google Mobile Actions dataset, designed to train lightweight models for on-device function calling. It preserves the original tool-calling schema in English while translating user interactions and contextual instructions into Italian.
The goal is to enable models to map natural language instructions in Italian to structured function calls… See the full description on the dataset page: https://huggingface.co/datasets/Mattimax/mobile-actions-ita.mobile-apps-user-sentiment-reviews
Top Mobile Apps User Sentiment & Review Corpus (Google Play)
Overview
This dataset contains clean, structured public data exported directly from production runs of Apify actors.
It serves as a benchmark and sample for lead qualification, market intelligence, research, and machine learning pipelines.
Source Actor: captainhandsome/google-play-reviews-scraper
Dataset Page: Public sample and schema
Preconfigured Run Task: captainhandsome/instagram-1star-reviews… See the full description on the dataset page: https://huggingface.co/datasets/joeygambino/mobile-apps-user-sentiment-reviews.MobileGuard
📱 Mobile GUI Unsafe Transition Dataset
This dataset is designed to benchmark unsafe transition detection in mobile task automation across a variety of real-world mobile applications. It includes multimodal input data per UI state and corresponding human-annotated safety labels.
📦 Dataset Contents
7 application folders (e.g., lyft/, phone/, music/, etc.)
Each folder contains a series of samples in the form:
appname_1.json: the serialized HTML GUI representation… See the full description on the dataset page: https://huggingface.co/datasets/RLinter/MobileGuard.africa-synth-mobile-telecom-mobile-subscriber-data-all
African Mobile Subscriber Data | Africa (Electric Sheep Africa metadata inventory)
Size category: 10K<n<100K - Formats: json - Sector: technology_digital - Engineered by Electric Sheep Africa
TL;DR
This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
What This Dataset Covers
Public datasets… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-mobile-telecom-mobile-subscriber-data-all.Arabic-Mobile-Instructions
Arabic Mobile Instructions
A curated Arabic instruction dataset designed for training and evaluating mobile-optimized language models.
Why Arabic?
Arabic is spoken by 400+ million people across 22 countries, yet Arabic-language instruction data on HuggingFace is scarce. This dataset fills the gap with mobile-relevant tasks:
Summarization — رسائل، إيميلات، إشعارات
Classification — تصنيف الرسائل والمشاعر
Translation — ترجمة بين العربية والإنجليزية
Question… See the full description on the dataset page: https://huggingface.co/datasets/dispatchAI/Arabic-Mobile-Instructions.mobile-actions
Mobile Actions: A Dataset for On-Device Function Calling
The dataset contains conversational traces designed to train lightweight models (such as FunctionGemma 270M) to translate natural language instructions into executable function calls for Android OS system tools.
Dataset Format
The dataset is provided in JSONL format. Each line represents a data sample. The
dataset is pre-split into training and evaluation sets. This distinction is
denoted by the metadata field… See the full description on the dataset page: https://huggingface.co/datasets/muralcode/mobile-actions.mobile-actions
Mobile Actions: A Dataset for On-Device Function Calling
The dataset contains conversational traces designed to train lightweight models (such as FunctionGemma 270M) to translate natural language instructions into executable function calls for Android OS system tools.
Dataset Format
The dataset is provided in JSONL format. Each line represents a data sample. The
dataset is pre-split into training and evaluation sets. This distinction is
denoted by the metadata field… See the full description on the dataset page: https://huggingface.co/datasets/amine-khelif/mobile-actions.mobile_sft_chat_template
Mobile Sft Chat Template
Dataset Description
Mobile QA dataset in chat template format for SFT with Unsloth/TRL. Each example contains messages with role-content pairs ready for chat model fine-tuning.
Dataset Summary
Total Examples: 11,328
Task: Conversational
Language: English
Format: JSONL (one JSON object per line)
Dataset Structure
Example Entry
{
"messages": [
{
"role": "user",
"content": "What is mobile… See the full description on the dataset page: https://huggingface.co/datasets/sujitpandey/mobile_sft_chat_template.MobileBench
MobileBench: The On-Device LLM Benchmark
A standardized evaluation benchmark designed specifically for mobile and edge-deployed language models.
Why MobileBench?
Existing benchmarks (MMLU, HumanEval, GSM8K) test what large models can do on servers. MobileBench tests what small models can do on phones — the tasks users actually perform:
Summarization — The #1 on-device task (messages, emails, notifications)
Classification — Spam detection, sentiment, intent… See the full description on the dataset page: https://huggingface.co/datasets/dispatchAI/MobileBench.mobile-actions
Mobile Actions: A Dataset for On-Device Function Calling
The dataset contains conversational traces designed to train lightweight models (such as FunctionGemma 270M) to translate natural language instructions into executable function calls for Android OS system tools.
Dataset Format
The dataset is provided in JSONL format. Each line represents a data sample. The
dataset is pre-split into training and evaluation sets. This distinction is
denoted by the metadata field… See the full description on the dataset page: https://huggingface.co/datasets/burancel/mobile-actions.mobile-actions
Mobile Actions: A Dataset for On-Device Function Calling
The dataset contains conversational traces designed to train lightweight models (such as FunctionGemma 270M) to translate natural language instructions into executable function calls for Android OS system tools.
Dataset Format
The dataset is provided in JSONL format. Each line represents a data sample. The
dataset is pre-split into training and evaluation sets. This distinction is
denoted by the metadata field… See the full description on the dataset page: https://huggingface.co/datasets/0xmoose0xmoose0xmoose/mobile-actions.mobile-actions
Mobile Actions: A Dataset for On-Device Function Calling
The dataset contains conversational traces designed to train lightweight models (such as FunctionGemma 270M) to translate natural language instructions into executable function calls for Android OS system tools.
Dataset Format
The dataset is provided in JSONL format. Each line represents a data sample. The
dataset is pre-split into training and evaluation sets. This distinction is
denoted by the metadata field… See the full description on the dataset page: https://huggingface.co/datasets/jeffbrian/mobile-actions.mobile-actions
Mobile Actions: A Dataset for On-Device Function Calling
The dataset contains conversational traces designed to train lightweight models (such as FunctionGemma 270M) to translate natural language instructions into executable function calls for Android OS system tools.
Dataset Format
The dataset is provided in JSONL format. Each line represents a data sample. The
dataset is pre-split into training and evaluation sets. This distinction is
denoted by the metadata field… See the full description on the dataset page: https://huggingface.co/datasets/lanneret/mobile-actions.mobile-actions
Mobile Actions: A Dataset for On-Device Function Calling
The dataset contains conversational traces designed to train lightweight models (such as FunctionGemma 270M) to translate natural language instructions into executable function calls for Android OS system tools.
Dataset Format
The dataset is provided in JSONL format. Each line represents a data sample. The
dataset is pre-split into training and evaluation sets. This distinction is
denoted by the metadata field… See the full description on the dataset page: https://huggingface.co/datasets/mindchain/mobile-actions.mobile_sft
Mobile Sft
Dataset Description
Mobile QA dataset for supervised fine-tuning
Dataset Summary
Total Examples: 11,328
Task: Question Answering
Language: English
Format: JSONL (one JSON object per line)
Dataset Structure
Example Entry
{
"question": "What is mobile innovation frontier and how does research enable it?",
"answer": "Mobile innovation frontier uses research to enable breakthrough discovery. Scientific advancement and… See the full description on the dataset page: https://huggingface.co/datasets/sujitpandey/mobile_sft.mobile-actions
Mobile Actions: A Dataset for On-Device Function Calling
The dataset contains conversational traces designed to train lightweight models (such as FunctionGemma 270M) to translate natural language instructions into executable function calls for Android OS system tools.
Dataset Format
The dataset is provided in JSONL format. Each line represents a data sample. The
dataset is pre-split into training and evaluation sets. This distinction is
denoted by the metadata field… See the full description on the dataset page: https://huggingface.co/datasets/Macarao26722/mobile-actions.mobile-actions
Mobile Actions: A Dataset for On-Device Function Calling
The dataset contains conversational traces designed to train lightweight models (such as FunctionGemma 270M) to translate natural language instructions into executable function calls for Android OS system tools.
Dataset Format
The dataset is provided in JSONL format. Each line represents a data sample. The
dataset is pre-split into training and evaluation sets. This distinction is
denoted by the metadata field… See the full description on the dataset page: https://huggingface.co/datasets/HexQuant/mobile-actions.mobile-actions
Mobile Actions: A Dataset for On-Device Function Calling
The dataset contains conversational traces designed to train lightweight models (such as FunctionGemma 270M) to translate natural language instructions into executable function calls for Android OS system tools.
Dataset Format
The dataset is provided in JSONL format. Each line represents a data sample. The
dataset is pre-split into training and evaluation sets. This distinction is
denoted by the metadata field… See the full description on the dataset page: https://huggingface.co/datasets/JesseJelinek/mobile-actions.paper-reengineering-mobile-models
Paper: Re-engineering 40+ Models with an Autonomous Agent
This dataset contains the paper and reproducibility data for:
"Re-engineering 40+ Models with an Autonomous Agent: A Zero-Cost Mobile AI Pipeline"
Contents
paper.md — Full paper text
inventory.json — Model inventory and pipeline metadata
Abstract
We present a fully autonomous pipeline that re-engineers open-source language models
for mobile and edge deployment at zero cost. Over 40 models… See the full description on the dataset page: https://huggingface.co/datasets/dispatchAI/paper-reengineering-mobile-models.mobile_sft_evaluation
Mobile Sft Evaluation
Dataset Description
Mobile QA evaluation dataset with 200 randomly sampled questions and rewritten answers for SFT model evaluation. Answers maintain semantic equivalence with different phrasing for robust evaluation.
Dataset Summary
Total Examples: 200
Task: Question Answering
Language: English
Format: JSONL (one JSON object per line)
Dataset Structure
Example Entry
{
"question": "How can mobile technology expand… See the full description on the dataset page: https://huggingface.co/datasets/sujitpandey/mobile_sft_evaluation.mobile-actions
Mobile Actions: A Dataset for On-Device Function Calling
The dataset contains conversational traces designed to train lightweight models (such as FunctionGemma 270M) to translate natural language instructions into executable function calls for Android OS system tools.
Dataset Format
The dataset is provided in JSONL format. Each line represents a data sample. The
dataset is pre-split into training and evaluation sets. This distinction is
denoted by the metadata field… See the full description on the dataset page: https://huggingface.co/datasets/deougege/mobile-actions.liquidchat-mobile-actions-2026-02-24
liquidchat-mobile-actions-2026-02-24
Chat history dataset exported from LiquidChat mobile app.
Dataset Description
This dataset contains conversations with a mobile-actions fine-tuned LLM, including tool calls (flashlight, calendar, email, maps, contacts, wifi) and their results.
Usage
from datasets import load_dataset
dataset = load_dataset("kshitijthakkar/liquidchat-mobile-actions-2026-02-24")
Created by: kshitijthakkar
Exported from: LiquidChat
License:… See the full description on the dataset page: https://huggingface.co/datasets/kshitijthakkar/liquidchat-mobile-actions-2026-02-24.mobile-actions
Mobile Actions: A Dataset for On-Device Function Calling
The dataset contains conversational traces designed to train lightweight models (such as FunctionGemma 270M) to translate natural language instructions into executable function calls for Android OS system tools.
Dataset Format
The dataset is provided in JSONL format. Each line represents a data sample. The
dataset is pre-split into training and evaluation sets. This distinction is
denoted by the metadata field… See the full description on the dataset page: https://huggingface.co/datasets/tatan2/mobile-actions.