This is a submission for the Gemma 4 Challenge: Write About Gemma 4

How I fine-tuned a small Gemma 4 model on a Mac to extract 22 invoice fields privately, and why the data strategy mattered more than the prompt.

I needed to read Indian GST invoices without sending them to an external API every time.

Gemma 4 E2B is an open multimodal model designed for local and edge deployment, with a 128K context window, native system prompt support, and an instruction-tuned variant that is usable without a giant serving stack. Google positions the small Gemma 4 models as practical for on-device and local workflows, not just as miniatures of the larger models. That made it a good fit for a problem I care about: structured invoice extraction where privacy, cost, and control matter as much as raw quality.

At my document volume, a hosted model would have been simple to prototype but expensive to normalize around. Roughly speaking, a model like GPT-4o lands around a cent per invoice at this prompt and output length.…