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hurtmongoose/Jailbreak-Detection-Models

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  • author_summary 5 models
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Abliteration classifier · v1.0.0
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Downloads · lifetime
31
11 last 30d - stable
Likes
0
Descendants
1
in 1 direct fork
Model age
13mo ago
created 2025-08-28
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Now34→from6↑467%
01225376 on Aug 27, 202534 on Oct 11Aug '25Oct '25Dec '25FebAprJunAugOct
Aug 27, 2025 → Oct 11 · 98 snapshots · spans 410 days

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Metadata

Tags
safetensors distilbert region:us

Related

Total size
255 MB
Files
8
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2025-08-28 13:47

Files by quantization

Auxiliary files 8 files 256 MB
model.safetensors 255 MB 68444001 download
tokenizer.json 695 KB 8f778e7d download
vocab.txt 226 KB fb140275 download
.gitattributes 1.48 KB a6344aac download
tokenizer_config.json 1.20 KB ca078cbe download
README.md 812 B 7f4a6616 download
config.json 687 B def57c15 download
special_tokens_map.json 125 B a8b3208c download

README current version from Hugging Face

Jailbreak Detection Model 🚀

This model is fine-tuned to detect jailbreak prompts / unsafe instructions.

📊 Training Metrics

  • Training steps: 0
  • Final Training Loss: N/A
  • Final Eval Loss: 0.07551019638776779

📈 Training Curve

Training Curve

🛠 How to Use

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("hurtmongoose/Jailbreak-Detection-Models")
tokenizer = AutoTokenizer.from_pretrained("hurtmongoose/Jailbreak-Detection-Models")

inputs = tokenizer("This is a test jailbreak prompt", return_tensors="pt")
outputs = model(**inputs)
print(outputs.logits)
📌 Notes

Trained on jailbreak detection dataset

Can be improved with more adversarial prompts

README history 2 versions

The author's README evolved over time. Click a version to see its content at that point.

  1. 2025-08-28Upload README.md with huggingface_hub19767d2812 B
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  2. 2025-08-28Upload DistilBertForSequenceClassificationc0277e05.1 KB
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