dispatchai
performance-tiers
Performance Tiers
Models grouped by speed:
Ultra Fast (30+ t/s): 1 models
Fast (15-30 t/s): 9 models
Moderate (5-15 t/s): 16 models
Slow (<5 t/s): 5 models
🚀 dispatchAI
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.model-categories
Model Categories
31 working models organized by use case.
🚀 dispatchAI
verified-benchmarks
Verified Benchmarks
Real CPU benchmark data for 22 verified dispatchAI models.
All speeds measured with llama-cpp-python on CPU (112 cores, 128GB RAM).
3 models also verified on Snapdragon 865 phone hardware.
🚀 dispatchAI
usage-examples
Usage Examples
Copy-paste code examples for each verified dispatchAI model.
Includes Python (llama-cpp-python), SDK (dispatchai), and CLI (llama.cpp) examples.
🚀 dispatchAI
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.