Build your own decision model

The article explains how to build a decision model that constrains language model outputs to a fixed set of options (A, B, C, D, E) by masking the vocabulary and selecting the highest probability token in a single inference pass. This approach is faster than generating free-form text but doesn't guarantee correct answers, and the output probabilities reflect the model's confidence in token prediction rather than the true probability of correctness. The article provides Python code using the Qwen model to implement this constrained decoding technique.