We pursue lower rejection rates while exploring lower KL divergence to maximally preserve model intelligence.
We provide two versions to balance censorship removal and capability preservation:
Abliterated Version (Refusal: 4/100, KL: 0.4096) – No refusal scenarios were triggered in manual testing.
Balanced Version (Refusal: 8/100, KL: 0.2446) – May show refusal tendencies on extremely aggressive prompts, but can be corrected via system/user prompts. Theoretically preserves more of the original model's intelligence due to lower KL divergence.
Logic tests show no visibly degraded intelligence compared to the official version. For more stable outputs, system prompts or user prompts can be used for constraints and guidance.
ABLiteration Approach
This model uses the Heretic ABLiteration method for neural direction ablation:
Identify Refusal Direction - Train a LoRA on harmful behavior datasets to identify neural directions controlling "refusal behavior"
Direction Extraction - Extract the "refusal vector" from the trained LoRA
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.
