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/zeroshot.png from chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX: direct link, hf CLI and curl.
- Browser
- Download file 138 kB
-
https://huggingface.co/chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX/resolve/main/figures/zeroshot.png
- Command line
-
hf download hf://chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX/figures/zeroshot.png
-
curl -L -o zeroshot.png https://huggingface.co/chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX/resolve/main/figures/zeroshot.png
138 kB

- Xet hash:
- 42beb9d23a91c68d70db040ecb60f9dca7fcd68a3b467ab0db23b1228d674ac0
- Size of remote file:
- 138 kB
- SHA256:
- d51f9a7bc46b88d8dcf5ba46f15d479c781302ca531208ff0838106da8aae755
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