Hugging Face explains Decision Models: probability outputs without text generation
ariG23498 · x · 2026-11-10
A Hugging Face team article introduces Decision Models: instead of generating text, they directly evaluate given options and return a probability distribution.
Key points:
- Use case: routing a customer support ticket (which team, urgency, refund request) — LLMs must generate JSON with full reasoning, while a decision pipeline outputs structured choices directly.
- Usage: pipeline("decision", model=...) takes a state and structured questions with choices and criteria; example model autotrust/GEV-26B-Decide.
- Ecosystem: covers transformers integration, TRL training support, and llama.cpp inference (GGUF quantized models via llama CLI).
A practical long-form tutorial for developers building low-cost classification/routing in production.
Related event: Hugging Face Brings Decision Models to Transformers(2 posts)→
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