Text Classification
MLX
Safetensors
jev-style
decision-model
system-one
calibration
long-context
multilingual
qwen3.5
on-device
Instructions to use chaoliangUNSW/Jev-Style-0.8B-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-0.8B-Decision-v3-MLX with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Jev-Style-0.8B-Decision-v3-MLX chaoliangUNSW/Jev-Style-0.8B-Decision-v3-MLX
- jev-style
How to use chaoliangUNSW/Jev-Style-0.8B-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-0.8B-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 8bit/tokenizer.json from chaoliangUNSW/Jev-Style-0.8B-Decision-v3-MLX: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/chaoliangUNSW/Jev-Style-0.8B-Decision-v3-MLX/resolve/main/8bit/tokenizer.json
- Command line
-
hf download hf://chaoliangUNSW/Jev-Style-0.8B-Decision-v3-MLX/8bit/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/chaoliangUNSW/Jev-Style-0.8B-Decision-v3-MLX/resolve/main/8bit/tokenizer.json
20 MB
- Xet hash:
- 777bcaa63794fa47b8f53680be9d6d176f1fcbd7ba03cdc6c3bae2b3d76b323f
- Size of remote file:
- 20 MB
- SHA256:
- 06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523
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