Text Classification
jev-style
Safetensors
Chinese
English
qwen3
security
decision-model
safety-perception
lora
Instructions to use howwhenRaw/llm-for-sec-secjev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- jev-style
How to use howwhenRaw/llm-for-sec-secjev with jev-style:
pip install "jev-style[torch]"
from jev_style import JevStyle, noul, choice js = JevStyle.from_pretrained("howwhenRaw/llm-for-sec-secjev") 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
Download tokenizer.json from howwhenRaw/llm-for-sec-secjev: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/howwhenRaw/llm-for-sec-secjev/resolve/main/tokenizer.json
- Command line
-
hf download hf://howwhenRaw/llm-for-sec-secjev/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/howwhenRaw/llm-for-sec-secjev/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- dde3c5a4d468da6fe61f11ffa8f04af2d101f8f4a0fe118c37bd31bb8c3017b6
- Size of remote file:
- 11.4 MB
- SHA256:
- be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
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