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")# 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: 12,181 Bytes
656ca59 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 | <?xml version="1.0" encoding="utf-8" standalone="no"?>
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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(17.712 262.539961)">JevBench v1.4.1, public items only (231). v3: self-run zero-shot with the vendored official harness (commit 24b9b5c);</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(17.712 274.327969)">training-pool contamination scan: 0 hits; not an official leaderboard entry. Other rows: public accuracy as published in the</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(17.712 286.123594)">board's v1.4.1 results file (github.com/fstandhartinger/jevbench). Shown: Laya plus every Qwen3.5-0.8B-based system on the board.</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(17.712 297.919219)">Laya and M. Ghafiri lie inside v3's 95% CI (57.7–70.0%, Wilson): those two leads are point estimates, not significant at n = 231.</text>
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