metadata
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 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.
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:
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.
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.