Instructions to use rudycaz/promptguard2-promptinj-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use rudycaz/promptguard2-promptinj-adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("meta-llama/Llama-Prompt-Guard-2-86M") model = PeftModel.from_pretrained(base_model, "rudycaz/promptguard2-promptinj-adapter") - Transformers
How to use rudycaz/promptguard2-promptinj-adapter with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rudycaz/promptguard2-promptinj-adapter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ab9e5f20adc26f50a4ed12315e3b61a18d7a95e4d12225b76a016230baaf30d
- Size of remote file:
- 16.3 MB
- SHA256:
- 1c6dfa928f8b49449a6b0d8751d97e0149b48f5ecef26b0ff877d1eeccd3712f
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