How Small Can You Go? LoRA Fine-Tuning 270M-8B Models for Merchant Information Extraction in Financial Transactions
Donghao Huang, Tomas Drietomsky, Benjamin Barrett, Zhaoxia Wang
Abstract
Merchant information extraction turns noisy financial transaction descriptors into structured fields at production scale. Our deployed LoRA-fine-tuned LLaMA~3.1-8B reaches 96.95\% F1, but its memory and throughput motivate smaller replacements. We evaluate 23 retained fine-tuning runs plus a separately trained production reference, spanning Gemma~3 (270M--4B), Qwen~3.5 (0.8B--4B), Aya~3.35B, and LLaMA~3.1-8B across LoRA ranks, prompts, training templates, and serving environments. A rank-8 LLaMA fine-tune reaches 96.75\% F1, only 0.20 points below the rank-32 production reference. Qwen~3.5~4B with JSON-Only prompting reaches 96.60\% F1 and strict record-level exact match of 91.67\%, with a $3.8\times$ lower inverse-throughput time estimate than the rank-8 8B model. Qwen~3.5~0.8B reaches 94.75\% F1, and Qwen Think and Nothink templates differ by less than 0.004 F1. Across 14 Databricks endpoints, mean F1 change from local evaluation is $-0.0081$; Aya is the only family with a 2.7--5.1 point decline. These results show that compact fine-tuned models can preserve most extraction accuracy, but model selection must account for prompt choice, throughput, and serving-stack behavior.
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Classified with taxonomy v2 on Sat, 5 Sept 2026.