datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.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.per-chip-benchmark-matrix
Per-Chip Benchmark Matrix
On-device inference benchmarks for mobile LLMs across chipsets.
Overview
This dataset contains real on-device inference benchmarks for 8 mobile-optimized
models running on Samsung S20 FE 5G phones (Snapdragon 865, 8GB RAM, Android 13).
Contents
benchmark_matrix.csv — Tabular data: model, device, chipset, tokens/sec, size
benchmark_matrix.json — Full structured data including hardware specs and methodology
Key… See the full description on the dataset page: https://huggingface.co/datasets/dispatchAI/per-chip-benchmark-matrix.on-device-latency
On-Device Latency Benchmark
Real-world inference latency data for mobile-optimized LLMs, measured on actual phone hardware.
Hardware
Spec
Value
Device
Samsung S20 FE 5G
SoC
Snapdragon 865
RAM
8GB
OS
Android 13
Runtime
llama.cpp (4 threads)
Metrics
tokens_per_sec — Generation speed during inference
latency_ms_per_token — Time per generated token
ram_usage_mb — Peak RAM during inference
file_size_mb — GGUF model file size… See the full description on the dataset page: https://huggingface.co/datasets/dispatchAI/on-device-latency.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.silicon-profiling-snapdragon865
Real-Device Silicon Profiling: Snapdragon 865
Per-device inference benchmarks on real Samsung S20 FE 5G phones (Snapdragon 865).
No simulation. Real ARM CPU inference.
Hardware
Property
Value
Chipset
Qualcomm Snapdragon 865 (SM8250)
CPU
Kryo 585: 1x2.84GHz + 3x2.42GHz + 4x1.80GHz
GPU
Adreno 650
NPU
Hexagon Tensor Accelerator
RAM
8GB LPDDR5 (7.47GB total, 3-3.7GB free)
Device
Samsung Galaxy S20 FE 5G (SM-G981V)
Devices connected
39… See the full description on the dataset page: https://huggingface.co/datasets/dispatchAI/silicon-profiling-snapdragon865.