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How Small Can You Go? LoRA Fine-Tuning 270M-8B Models for Merchant Information Extraction in Financial Transactions

arXiv cs.AI · 2026-06-09 Cached

This paper presents a deployment-focused study comparing LoRA fine-tuning of 24 model variants (270M–8B parameters) for merchant information extraction from financial transaction strings. The authors find that smaller models like Qwen 3.5 4B achieve 96.6% F1, within 0.35 points of the 8B baseline, while offering significant reductions in latency and cost.

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