Text Classification
PEFT
Safetensors
English
jailbreak-detection
prompt-injection
safety
lora
unsloth
Instructions to use vincentoh/jailbreak-detector-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use vincentoh/jailbreak-detector-v5 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gpt-oss-20b-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "vincentoh/jailbreak-detector-v5") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use vincentoh/jailbreak-detector-v5 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for vincentoh/jailbreak-detector-v5 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for vincentoh/jailbreak-detector-v5 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for vincentoh/jailbreak-detector-v5 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="vincentoh/jailbreak-detector-v5", max_seq_length=2048, )
- Xet hash:
- f68f089876466125ea3cc9a21c770b760dca146bbf858c73c740ba634d1e91b3
- Size of remote file:
- 31.9 MB
- SHA256:
- f94ed4d954277c7b51185562d933e0e6fc2b3c26b1997bfb12655cf2fc394fbe
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.