Sign up

LiconStudio/Qwen3.5-9B-abliterated

🤗 On Hugging Facetext-generationapache-2.031 GBGGUF✓ HF checksums availableupdated today
Magnet

Model Description

This is an uncensored version of Qwen3.5-9B, processed using the Heretic method to remove the model's built-in refusal/censorship mechanisms through neural direction ablation.

Residual Visualization

PaCMAP projections showing the mixing of harmless (blue) and harmful (red) prompts:

| Layer 12 | Layer 17 |

|----------|----------|

| !Layer 12 | !Layer 17 |

| Layer 22 | Layer 28 |

|----------|----------|

| !Layer 22 | !Layer 28 |

These plots show successful removal of refusal behavior - harmless and harmful prompts are well-mixed across layers.

Core Metrics

| Metric | Original Model | This Model | Description |

|--------|----------------|-------------------|-------------|

| Refusal Rate | 92.0% | 4.0% | Tested on 100 harmful prompts |

| KL Divergence | - | 0.0583 | Per-token average |

| Model Size | 9B | 9B | Architecture unchanged |

KL Divergence Rating

KL divergence measures the degree of model modification:

| KL Range | Rating | Description |

|----------|--------|-------------|

| < 0.05 | ⭐⭐⭐⭐⭐ | Extremely Low - Model virtually unchanged |

| 0.05 - 0.10 | ⭐⭐⭐⭐ | Low - Minor modification, capabilities well preserved |

| 0.10 - 0.20 | ⭐⭐⭐ | Moderate - Acceptable modification range |

| 0.20 - 0.50 | ⭐⭐ | High - Possible noticeable capability loss |

| > 0.50 | ⭐ | Too High - Model may be severely compromised |

**This model: KL : 0.0583, Refusal Rate : 4/100, NLL:3.37%

Heretic Approach

This model uses the Heretic method for neural direction ablation:

1. Identify Refusal Direction - Compute residual vectors from harmful vs. harmless prompts

2. Direction Extraction - Extract the "refusal vector" from the difference of means

3. Ablative Removal - Apply LoRA-based modification to subtract this direction from model weights

This method only modifies model weights without changing the architecture or adding inference overhead.

For detailed technical principles, refer to: Heretic GitHub

Intended Use Cases

✅ Recommended Uses

  • Uncensored content creation
  • Research and analysis of sensitive topics
  • Safety testing and red-teaming exercises
  • Academic research on model alignment

❌ Not Recommended For

  • Production environments requiring content moderation
  • Applications targeting minors
  • Scenarios with potential legal risks

Limitations

1. No Safety Filtering - The model will directly answer all questions, including harmful or dangerous content

2. User Discretion Required - Users must independently judge the appropriateness of generated outputs

3. Minor Capability Loss - Some performance degradation on complex tasks may occur

Disclaimer

⚠️ Important: This model is intended for research and educational purposes only.

  • This model has had its censorship mechanisms removed and may generate harmful, dangerous, or inappropriate content
  • Users assume all risks associated with usage
  • Do not use this model for illegal activities, harming others, or any inappropriate purposes
  • The model authors are not liable for any indirect, incidental, or consequential damages

Acknowledgments