Text Classification
MLX
Safetensors
jev-style
qwen3_5
decision-model
decision-making
system-one
calibration
long-context
qwen3.5
apple-silicon
on-device
llm-routing
guardrails
Instructions to use chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Jev-Style-2B-Decision-v3-MLX chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX
- jev-style
How to use chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX with jev-style:
# Apple silicon pip install "jev-style[mlx]"
from jev_style import JevStyle, noul, choice js = JevStyle.from_pretrained("chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX") 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"]) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
File size: 1,841 Bytes
11ce5d7 | 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 | {
"chart": "zeroshot",
"metric": "accuracy over every row of the pinned test files",
"values": {
"tweet_topic": {
"2b": 0.822209096278795,
"2b_ci95": [
0.8038984051978736,
0.8399438865918486
],
"2b_macro_f1": 0.677897069437572,
"2b_ece15": 0.027919883880813873,
"08b": 0.754873006497342,
"jev": 0.7932663910218547,
"jev_macro_f1": 0.6936,
"jev_ece15": 0.0631,
"n": 1693
},
"fin_topic": {
"2b": 0.6111246052951178,
"2b_ci95": [
0.5960650959436483,
0.6259412193344669
],
"2b_macro_f1": 0.5897964463354406,
"2b_ece15": 0.06460657860002232,
"08b": 0.4670876852076755,
"jev": 0.669905270828273,
"jev_macro_f1": 0.6298,
"jev_ece15": 0.1664,
"n": 4117
}
},
"sources": {
"2b": "https://huggingface.co/chaoliangUNSW/Jev-Style-2B-Decision-v3/blob/main/validation/benchmarks/zeroshot_metrics.json",
"0.8b": "https://huggingface.co/chaoliangUNSW/Jev-Style-0.8B-Decision-v3/blob/main/figures/zeroshot.json (0.8B v3 card; v3_recomputed: tweet_topic 1278/1693, fin_topic 1923/4117)",
"jev": "https://github.com/elcronos/jev-vs-open-decision-models/blob/a1901bc3d520e73936de8d4326545c0cdcf742fb/results/cross_dataset_summary.json (as copied in https://huggingface.co/chaoliangUNSW/Jev-Style-2B-Decision-v3/blob/main/validation/benchmarks/zeroshot_metrics.json :: comparison)"
},
"footnote": "Zero-shot: none of these test sets is in the 2B or 0.8B training pool; accuracy over every row of the pinned test files (n = 1,693 and 4,117). 2B v3: GGUF F16 engine, one global temperature, run once; tweet_topic 95% CI 80.4-84.0%. Jev (1.13, API): numbers published by the elcronos jev-vs-open-decision-models study (cross_dataset_summary.json @ a1901bc), not re-run by us. Macro-F1 is below Jev on both sets (tweet_topic 67.8% vs 69.4%; fin_topic 59.0% vs 63.0%)."
} |