Fine-tuned 4B model as a decision scorer with temperature-scaled confidence
Gradio · x · 2026-09-18
@evilpingwin shares an experiment built with the ML Intern agent on Hugging Chat: fine-tuning Qwen3.5-4B into a decision scorer that, given context, a question and candidate answers, scores each option.
The key inference-side step is temperature scaling to calibrate the output probabilities, so the model's confidence better tracks how often it's actually right. The author notes the inference setup is half the work and links a Gradio demo.
A concrete example of using agents to assemble a small-model pipeline: scoring answers rather than generating them, with calibrated confidence.
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