Fine-Tuning GPT-Class LLMs for Credit Memo Drafting

Frequently Asked Questions

01.What is the difference between LoRA and QLoRA for credit memo fine-tuning?

LoRA trains low-rank adapter matrices on top of a full-precision base model, needing more VRAM but preserving slightly more numerical fidelity on financial ratio reasoning. QLoRA quantizes the frozen base model to 4-bit NormalFloat before attaching the same adapters, which is efficient enough to fine-tune a 65B parameter model on a single 48GB GPU while preserving full 16-bit fine-tuning task performance, with a small, measurable accuracy tradeoff on multi-step spread calculations.

02.Can a single GPU fine-tune a GPT-class model for commercial lending memo drafting?

Yes, for models in the 7B to 13B parameter range, QLoRA fits on a single 24GB or 48GB GPU. Full-weight fine-tuning at that same parameter count requires multi-GPU setups with 300 to 500GB of aggregate VRAM once optimizer states and gradients are counted, which is why most lending teams start with QLoRA before considering full-weight runs.

03.Does fine-tuning replace human underwriter review of credit memos?

No. Every regulated credit decision workflow keeps a human underwriter as the final approver. Fine-tuned models draft the narrative sections, pull consistent ratio calculations, and flag covenant gaps, but signature authority and final risk rating remain with a licensed underwriter under OCC and Federal Reserve supervisory guidance.