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LiconStudio/Qwen3.5-27B-abliterated-GGUF

🤗 Hugging Face 来源text-generationapache-2.0激活 27B151 GBGGUF✓ 10 个校验和今天更新
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Evaluation & Comparison

Core Metrics

Metric Original Model Heretic Model Description
Refusal Rate 92.0% 2/100 Tested on 520 harmful prompts
KL Divergence - 0.0414 Sequence cumulative KL (per token)
NLL Change - +4.2% Minor impact on language capability
Model Size 27B 27B 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 = 0.0414 , Refusal Rate: 2/100 , NLL : +4.2%

Residual Visualization

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

Layer 20 Layer 30
Layer 40 Layer 55

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

Technical Method

ABLIteration Approach

This model uses the Heretic ABLIteration method for neural direction ablation:

  1. Identify Refusal Direction - Train a LoRA on harmful behavior datasets to identify neural directions controlling "refusal behavior"
  2. Direction Extraction - Extract the "refusal vector" from the trained LoRA
  3. Ablative Removal - Subtract this direction from the original model weights, removing the censorship mechanism

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

For detailed technical principles, refer to: Heretic Abliteration

Data Sources

Purpose Dataset
Refusal Direction Identification mlabonne/harmful_behaviors (520 prompts)
KL Evaluation General prompts (100 prompts)
Refusal Rate Testing mlabonne/harmful_behaviors (520 prompts)

✅ Recommended Uses

  • 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. Minor Capability Loss - NLL increased by approximately 4.2%, which may slightly affect performance on complex tasks
  2. User Discretion Required - Users must independently judge the appropriateness of generated outputs

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