How to use chaoliangUNSW/Jev-Style-0.8B-Decision-v3 with jev-style:
pip install "jev-style[torch]"
from jev_style import JevStyle, noul, choice js = JevStyle.from_pretrained("chaoliangUNSW/Jev-Style-0.8B-Decision-v3") out = js.decide("I was charged twice for one order.", { "billing": noul("This message is about billing."), "team": choice("Which team should handle it?", ["billing", "shipping", "tech"]), }) print(out["answers"]["team"]["choice"])
How to use chaoliangUNSW/Jev-Style-0.8B-Decision-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chaoliangUNSW/Jev-Style-0.8B-Decision-v3")
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("chaoliangUNSW/Jev-Style-0.8B-Decision-v3") model = AutoModelForCausalLM.from_pretrained("chaoliangUNSW/Jev-Style-0.8B-Decision-v3", device_map="auto")
https://huggingface.co/chaoliangUNSW/Jev-Style-0.8B-Decision-v3/resolve/656ca59be52fd6100eb5dba7bc9e79cddcd09251/figures/beyond_laya.svg
hf download hf://chaoliangUNSW/Jev-Style-0.8B-Decision-v3@656ca59be52fd6100eb5dba7bc9e79cddcd09251/figures/beyond_laya.svg
curl -L -o beyond_laya.svg https://huggingface.co/chaoliangUNSW/Jev-Style-0.8B-Decision-v3/resolve/656ca59be52fd6100eb5dba7bc9e79cddcd09251/figures/beyond_laya.svg
