Tutorial builds and calibrates a decision model on Qwen3
What happened
A tutorial using Qwen/Qwen3-1.7B to build and calibrate a decision model hit the Hacker News front page on October 11. The model picks an answer from five fixed options, A through E, in a single forward pass. On a random held-out sample of CommonsenseQA it scored 725/1221 (0.5938), rising to 762/1221 (0.6241) after fine-tuning. The tutorial notes that output token probability does not equal answer accuracy, and improves calibration with temperature scaling. A GitHub repo with dataset construction, evaluation, fine-tuning and calibration scripts is included.
Written by AI from the coverage · updated 2 hours ago
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- Hacker News front pageBuild your own decision model
教程用 Qwen/Qwen3-1.7B 构建决策模型，通过单次前向计算在 A、B、C、D、E 固定选项中选择答案。模型在 CommonsenseQA 随机留出样本上的准确率为 725/1221 = 0.5938，微调后提升至 762/1221 = 0.6241。输出 token 概率不直接等于答案正确率，教程通过温度缩放改善校准，并提供包含数据集构建、评估、微调与校准脚本的 GitHub 仓库。
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