Decision Models in Hugging Face

Decision models are introduced as an efficient alternative to Large Language Models (LLMs) for tasks that require a structured choice rather than open-ended text generation. A decision model takes an input context (state) and a question with predefined answers, then returns a probability distribution over those choices. This method avoids the potential for invalid outputs and the computational overhead of autoregressive decoding often seen when using LLMs for classification. The approach is compared to traditional and zero-shot classification, highlighting its flexibility in handling dynamic answer sets at inference time. The Hugging Face ecosystem is integrating support for these models, including serving via the transformers library and hosting on the Hub.