I Distilled a 397B Model Into a 4B One for $1.60. It Now Catches 28 of 33 Park Alerts That Should Stop Your Hike

Description and use

A fine‑tuned Qwen3.5‑4B LoRA model improves decision accuracy to 74.6 % and detects 28 of 33 critical park alerts, costing $0.13 per 1,000 alerts to run.

Problem

National Park Service alerts are long and many hikers ignore them, leading to unsafe hikes.

Outcome

The 4B LoRA model improves decision accuracy from 64.3% to 74.6% and catches 28 of 33 critical alerts, while the entire pipeline costs $1.60 to build and $0.13 per 1,000 alerts to run.

Workflow

• Collect alerts via NPS API for selected parks
• Run each alert through fine‑tuned Qwen3.5‑4B LoRA
• Generate a printable card with overall verdict and trail list
• Print or screenshot the card
• Use card to decide whether to hike

Evidence

On 126 held‑out alerts the fine‑tuned 4B went from catching 17 of 33 alerts that should stop a hiker to 28 of 33, and from 64.3% to 74.6% decision accuracy.

Source

It costs $0.13 per 1,000 alerts, about 11× less than the teacher, and the whole ML pipeline cost $1.60.

Source

The model's only job is to produce one printable card a week, so you can stop checking park alerts and go outside.

Source