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] hf download chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX --local-dir 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
Download figures/banner.data.json from chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX: direct link, hf CLI and curl.
- Browser
- Download file 1.11 kB
-
https://huggingface.co/chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX/resolve/11ce5d718e22412b38624bb863a1eee73c0a5934/figures/banner.data.json
- Command line
-
hf download hf://chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX@11ce5d718e22412b38624bb863a1eee73c0a5934/figures/banner.data.json
-
curl -L -o banner.data.json https://huggingface.co/chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX/resolve/11ce5d718e22412b38624bb863a1eee73c0a5934/figures/banner.data.json
1.11 kB
| { | |
| "left": { | |
| "big": "73.6% on JevBench", | |
| "sub": "+9.5 points over our 0.8B v3; Jev 86.6%", | |
| "tokens": "25,600 tokens per call, no option cap" | |
| }, | |
| "rows": [ | |
| { | |
| "label": "Jev 1.13 (API)", | |
| "accuracy": 0.8658008658008658, | |
| "pct_1dp": 86.6 | |
| }, | |
| { | |
| "label": "2B v3 \u00b7 this model", | |
| "accuracy": 0.7359307359307359, | |
| "pct_1dp": 73.6 | |
| }, | |
| { | |
| "label": "decider-2b", | |
| "accuracy": 0.70995670995671, | |
| "pct_1dp": 71.0 | |
| }, | |
| { | |
| "label": "Open-Jev 2B", | |
| "accuracy": 0.645021645021645, | |
| "pct_1dp": 64.5 | |
| }, | |
| { | |
| "label": "0.8B v3", | |
| "accuracy": 0.6406926406926406, | |
| "pct_1dp": 64.1 | |
| }, | |
| { | |
| "label": "Laya", | |
| "accuracy": 0.5844155844155844, | |
| "pct_1dp": 58.4 | |
| } | |
| ], | |
| "shown": "Shown: Qwen3.5-2B-family systems, our 0.8B v3, Laya and Jev \u00b7 42 of 82 board systems score higher than 73.6%", | |
| "note": "231 public items. 2B v3: self-run with the official harness (GGUF F16), not an official board entry; 95% CI 67.6\u201378.9%, so the lead over decider-2b is inside the CI. Other rows as published on the v1.4.1 board.", | |
| "sources": [ | |
| "figures/jevbench.data.json" | |
| ] | |
| } |