Instructions to use codesagar/prompt-guard-classification-v10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codesagar/prompt-guard-classification-v10 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("codesagar/prompt-guard-classification-v10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download adapter_model.safetensors from codesagar/prompt-guard-classification-v10: direct link, hf CLI and curl.
- Browser
- Download file 168 MB
-
https://huggingface.co/codesagar/prompt-guard-classification-v10/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://codesagar/prompt-guard-classification-v10/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/codesagar/prompt-guard-classification-v10/resolve/main/adapter_model.safetensors
168 MB
- Xet hash:
- fe2e6c5e096f9a2030b3b1aa738ad21151dd8c5914618c97602c6a0b7544eb17
- Size of remote file:
- 168 MB
- SHA256:
- e06511aa2e032231a954db114d324d30586345dd656e4e4c674f7d3d14a3549e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.