--- license: apache-2.0 base_model: FINAL-Bench/Darwin-27B-ZTC pipeline_tag: zero-shot-classification library_name: transformers tags: [decision-model, system-one] --- # Darwin-27B-ZTC for 🤗 Transformers The 255-way answer-code readout of [FINAL-Bench/Darwin-27B-ZTC](https://huggingface.co/FINAL-Bench/Darwin-27B-ZTC) as a JEV decision head, for the `decision` pipeline. This repo holds the readout, the tokenizer and the prompt; the backbone loads from the original repo. ## Questions Each question is answered with a probability for every option: `noul` (yes/no), `choice` and `score`. ```python from transformers import pipeline decide = pipeline("decision", model="ArthurZ/Darwin-27B-ZTC") answers = decide( { "state": {"customer": "Lena", "message": "I was charged twice for my order and I want my money back today."}, "questions": { "refund": {"type": "noul", "instructions": "Does the customer ask for a refund?"}, "tone": {"type": "choice", "instructions": "What is the tone?", "criteria": {"calm": "Polite", "angry": None}}, "urgency": {"type": "score", "instructions": "How urgent is it?", "criteria": ["Can wait", "This week", "Today", "Now"]}, }, } ) answers["refund"]["noul"] # probability of true answers["tone"]["choice"], answers["tone"]["probabilities"] answers["urgency"]["score"] # expected level, 0 to 3 ``` ## Same questions, new states A bare state is asked the questions of the last request, or the ones given up front: ```python decide = pipeline( "decision", model="ArthurZ/Darwin-27B-ZTC", questions={"spam": {"type": "noul", "instructions": "Is this spam?"}}, ) decide("WIN A FREE IPHONE!!! click here to claim your prize") decide("Hi! The parcel arrived, thanks a lot.") ``` Converted with [`convert_decider_to_hf.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/jev/convert_decider_to_hf.py). Its prompts are token-for-token those of the original code (`decision_test.json`), except that control tokens written in a request are escaped instead of read as such.