Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Parallel Computing and Optimization Techniques
Abstract
AviGPT-250M-Instruct is a 250M-parameter autoregressive small language model (SLM) introducing a Semi-Parametric Decoupling paradigm for resource-constrained edge computing. Rather than overloading transformer weights with static encyclopedic memorization and floating-point arithmetic approximation, AviGPT-250M delegates factual storage to a native, sub-millisecond (0.0015 ms / 1.52 µs) NVMe Hardware Memory Bus powered by SQLite Full-Text Search (FTS5) with Okapi BM25 ranking, and delegates arithmetic evaluation to a sandboxed deterministic AST SafeMath evaluator. Across an identical 8-model competitive benchmark on an NVIDIA Tesla T4 GPU against models ranging from 125M to 1.1B parameters, AviGPT-250M achieves 100.0% Factual Accuracy and 100.0% Deterministic Math Precision, delivering the Global #1 Composite Efficiency score of 0.40 while fitting inside an ultra-compact resident VRAM footprint of just 488 MB.
Supporting data, adapters, predictions and code for the article *Low-Cost LoRA Fine-Tuning of Small Language Models for Multi-Step Arithmetic Reasoning* by Jake O'Grady, Asena Isik Gürhan, Chee Fong Ting and Effirul Ramlan (University of Galway). We generated 20,000 GSM8K-derived arithmetic problems with step-by-step s...
O'Grady, Jake, Gürhan, Asena Isik, Chee, Fong Ting et al.· Zenodo (CERN European Organi...· 465 citations
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