Text Classification
jev-style
Safetensors
Transformers
qwen3_5_text
text-generation
decision-model
decision-making
system-one
calibration
classification
long-context
qwen3.5
on-device
llm-routing
guardrails
Instructions to use chaoliangUNSW/Jev-Style-2B-Decision-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- jev-style
How to use chaoliangUNSW/Jev-Style-2B-Decision-v3 with jev-style:
pip install "jev-style[torch]"
from jev_style import JevStyle, noul, choice js = JevStyle.from_pretrained("chaoliangUNSW/Jev-Style-2B-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"]) - Transformers
How to use chaoliangUNSW/Jev-Style-2B-Decision-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chaoliangUNSW/Jev-Style-2B-Decision-v3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("chaoliangUNSW/Jev-Style-2B-Decision-v3") model = AutoModelForCausalLM.from_pretrained("chaoliangUNSW/Jev-Style-2B-Decision-v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download figures/zeroshot.png from chaoliangUNSW/Jev-Style-2B-Decision-v3: direct link, hf CLI and curl.
- Browser
- Download file 138 kB
-
https://huggingface.co/chaoliangUNSW/Jev-Style-2B-Decision-v3/resolve/main/figures/zeroshot.png
- Command line
-
hf download hf://chaoliangUNSW/Jev-Style-2B-Decision-v3/figures/zeroshot.png
-
curl -L -o zeroshot.png https://huggingface.co/chaoliangUNSW/Jev-Style-2B-Decision-v3/resolve/main/figures/zeroshot.png
138 kB

- Xet hash:
- 42beb9d23a91c68d70db040ecb60f9dca7fcd68a3b467ab0db23b1228d674ac0
- Size of remote file:
- 138 kB
- SHA256:
- d51f9a7bc46b88d8dcf5ba46f15d479c781302ca531208ff0838106da8aae755
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