Text Classification
jev-style
Safetensors
Transformers
qwen3_5_text
text-generation
decision-model
system-one
calibration
classification
long-context
multilingual
qwen3.5
on-device
llm-routing
guardrails
Instructions to use chaoliangUNSW/Jev-Style-0.8B-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-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"]) - Transformers
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") - Notebooks
- Google Colab
- Kaggle
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Download figures/latency.svg from chaoliangUNSW/Jev-Style-0.8B-Decision-v3: direct link, hf CLI and curl.
- Browser
- Download file 24.1 kB
-
https://huggingface.co/chaoliangUNSW/Jev-Style-0.8B-Decision-v3/resolve/656ca59be52fd6100eb5dba7bc9e79cddcd09251/figures/latency.svg
- Command line
-
hf download hf://chaoliangUNSW/Jev-Style-0.8B-Decision-v3@656ca59be52fd6100eb5dba7bc9e79cddcd09251/figures/latency.svg
-
curl -L -o latency.svg https://huggingface.co/chaoliangUNSW/Jev-Style-0.8B-Decision-v3/resolve/656ca59be52fd6100eb5dba7bc9e79cddcd09251/figures/latency.svg
24.1 kB
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| <text style="font-size: 7.5px; font-family: 'Helvetica Neue', 'Helvetica', 'Arial', 'Avenir Next', 'PingFang SC', 'Arial Unicode MS', 'DejaVu Sans', sans-serif; fill: #8e8e96" transform="translate(18.576 328.251211)">PyTorch FP32 on Apple MPS), one call per question; not an official Laya checkpoint. Compared at 5 and 10 questions per state.</text> | |
| <text style="font-size: 7.5px; font-family: 'Helvetica Neue', 'Helvetica', 'Arial', 'Avenir Next', 'PingFang SC', 'Arial Unicode MS', 'DejaVu Sans', sans-serif; fill: #8e8e96" transform="translate(18.576 340.039219)">M1 Max 64 GB, warm end-to-end p50, idle run 2026-09-23, prefix reuse off. Plotted values: figures/latency.data.json.</text> | |
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