A tiny decision model needs calibration, not just valid answers

Nish Tahir shows how a Python program can score five fixed answers with one Qwen3-1.7B forward pass instead of generating a JSON response. His initial CommonsenseQA evaluation reaches 59.38% accuracy, but the model is often much more confident than correct. The tutorial then explores fine-tuning and temperature calibration: restricting outputs solves formatting, not reliability.

Nish Tahir · Oct 10, 2026

Why it matters

A concrete independent implementation makes the tradeoffs behind decision models easier to inspect.

More context

Try the accompanying evaluation scripts on a representative held-out dataset. The walkthrough reports 62.41% accuracy after a quick fine-tune and demonstrates adjusting probabilities with temperature scaling.

Keep in mind

The reported results are one author’s experiment; calibrated probabilities on one dataset need not transfer to another.

Original read