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
File size: 24,079 Bytes
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<text style="font-size: 9px; font-family: 'Helvetica Neue', 'Helvetica', 'Arial', 'Avenir Next', 'PingFang SC', 'Arial Unicode MS', 'DejaVu Sans', sans-serif; text-anchor: start; fill: #0d0d0f" x="70.52112" y="74.71782" transform="rotate(-0 70.52112 74.71782)">Jev-Style 0.8B v3: state read once, all questions in one call</text>
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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(18.576 304.652344)">Identical-architecture export timing (untrained Qwen3.5-0.8B export; latency does not depend on the weights). v3 = llama.cpp GGUF F16,</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(18.576 316.447969)">one call per state. Comparison engine = round-1 MacLaya-4K, our own fine-tune of the Laya multilingual architecture (4K token budget,</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(18.576 328.251211)">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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