Arabic-first small language model for edge and on-device AI
Built by PYXON.AI · Research · Labs · Production AI · Beta · v.2026
Why pay for a 7B cloud model when a 1.5B model on your device can carry the workload — especially with retrieval? Try PYXON.AI →
What this is
Pyxon SLLM v0 is a ~1.5B Arabic instruction model fine-tuned for chat and edge deployment. It is part of PYXON AI Labs’ on-device research line: lightweight models that run offline, keep data on-device, and pair with retrieval (RAG) when accuracy must match larger cloud systems.
QLoRA supervised fine-tuning on public Arabic datasets
Runtime artifact
pyxon-sllm-v0.gguf (q8_0)
Designed for
Phone / edge / Ollama / offline assistants
From the Labs
PYXON.AI is PYXON’s research division — scientists and engineers building AI that performs on Arabic-language and on-device challenges, not slides.
This release sits next to Pyxon On-Device RAG: research showing that small models plus device-specific retrieval can match or beat much larger baselines while staying compatible with mobile compute, memory, and privacy constraints.
Related research
Enhancing weak LLM performance on edge devices through retrieval-augmented generation — a benchmark study (Beta · 2026-03-28)
Hamza Salem, Ahmad Algraeeb, Almuhanad Al-Nihmy & Manuel Mazzara
RAG · edge computing · mobile · LLM · on-device AI · benchmarks
A 1,500-question benchmark from three phone manuals shows RAG lifting small on-device models to 100% accuracy versus ~21–37% without retrieval, with analysis of size, latency, and device-specific knowledge bases.
1cd pyxon-sllm
23# rename if you still have the old export name4# mv arabic-1.5b-q8_0.gguf pyxon-sllm-v0.gguf5# mv outputs/gguf/pyxon-sllm-v0.gguf ./pyxon-sllm-v0.gguf67ollama create pyxon-sllm-v0 -f Modelfile
8ollama run pyxon-sllm-v0 "اشرح لي الذكاء الاصطناعي باختصار"
