How Ramp automated receipt processing with fine-tuned LLMs

This post details how Ramp used Modal to fine-tune LLMs for receipt processing. Key contributions include parallel training of candidate models, persisting weights to Modal volumes, and serving inference endpoints. Modal's orchestration of GPU scaling and standardization of containerized environments were critical. Additionally, the post highlights using Modal for large-scale batch processing, specifically stripping PII from invoices, demonstrating a 20-minute completion time for a task that would have taken 3 days locally.