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--- |
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license: apache-2.0 |
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base_model: microsoft/deberta-v3-xsmall |
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tags: |
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- safety |
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- content-moderation |
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- deberta |
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- text-classification |
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model_name: Wizard101-L0-Bouncer |
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--- |
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# Wizard101 L0 Bouncer |
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Fast safety classifier for the first layer of a multi-level content moderation cascade. Built on DeBERTa-v3-xsmall for speed and efficiency. |
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## Model Details |
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- **Base Model**: microsoft/deberta-v3-xsmall |
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- **Task**: Binary text classification (safe/harmful) |
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- **Training Data**: 124K samples |
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- **Size**: ~70MB |
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- **Inference**: <10ms per sample |
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## Description |
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L0 Bouncer is the first line of defense in a safety cascade system. It quickly filters obvious safe/harmful content, passing uncertain cases to more powerful downstream models (L1 GuardReasoner, L2/L3 reasoning models). |
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**Design Goals:** |
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- Maximum speed for high-throughput filtering |
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- High recall on harmful content (minimize false negatives) |
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- Route uncertain cases to L1+ for deeper analysis |
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## Usage |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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import torch |
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# Load model |
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tokenizer = AutoTokenizer.from_pretrained("vincentoh/wizard101-l0-bouncer") |
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model = AutoModelForSequenceClassification.from_pretrained("vincentoh/wizard101-l0-bouncer") |
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model.eval() |
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# Inference |
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text = "How do I make a cake?" |
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inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512) |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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probs = torch.softmax(outputs.logits, dim=-1) |
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# Index 0 = safe, Index 1 = harmful |
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safe_prob = probs[0][0].item() |
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harmful_prob = probs[0][1].item() |
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if harmful_prob > safe_prob: |
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prediction = "harmful" |
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confidence = harmful_prob |
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else: |
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prediction = "safe" |
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confidence = safe_prob |
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print(f"Prediction: {prediction} ({confidence:.2%})") |
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``` |
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## Cascade Integration |
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```python |
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# Route to L1 if confidence < 0.9 |
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needs_l1 = confidence < 0.9 |
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if needs_l1: |
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# Send to GuardReasoner-8B for detailed analysis |
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pass |
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``` |
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## Performance |
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Benchmark results on safety datasets: |
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| Dataset | Samples | Accuracy | |
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|---------|---------|----------| |
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| JailbreakBench | 200 | 68.0% | |
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| SG-Bench | 500 | 88.8% | |
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| StrongREJECT | 313 | 96.8% | |
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| WildGuardMix | 500 | 96.8% | |
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**Note**: Lower accuracy on adversarial datasets (JailbreakBench) is expected - these cases route to L1+ for deeper analysis. |
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## Cascade Architecture |
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``` |
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User Input |
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│ |
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▼ |
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┌─────────┐ |
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│ L0 │ ◄── This model (fast filter) |
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│ Bouncer │ |
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└────┬────┘ |
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│ (uncertain cases) |
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▼ |
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┌─────────┐ |
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│ L1 │ GuardReasoner-8B |
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└────┬────┘ |
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│ |
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▼ |
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┌─────────┐ |
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│ L2/L3 │ GPT-OSS reasoning models |
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└─────────┘ |
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``` |
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## Training |
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- **Dataset**: Combined safety datasets (124K samples) |
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- **Labels**: Binary (safe/harmful) |
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- **Epochs**: Fine-tuned on DeBERTa-v3-xsmall |
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- **Hardware**: Single GPU |
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## License |
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Apache 2.0 |
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## Citation |
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Part of the Wizard101 Safety Cascade project for efficient multi-level content moderation. |
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