Qwen3.5-4B-Abliterated-Claude-4.6-Opus-Reasoning-Distilled

This is a specialized variant of the Qwen-4B-Reasoning architecture. It has been mathematically modified to neutralize the refusal behaviors and safety guardrails typically found in Claude-distilled reasoning models.

🛠 The "Deep-Scrub" Methodology

Standard abliteration often fails on reasoning models because the "safety tripwire" is woven into the early logic chain. This model uses an aggressive early-intercept strategy.

Technical Configuration

Direction Multiplier: 3.50 (Ultra-Aggressive)
Intervention Range: 0.05 - 0.95 (Intercepting refusal logic at Layer 2)
Dynamic Layer Targeting: Enabled (Per-layer refusal vectors)
Hybrid Strategy: Auto-balanced (Full Attention: 1.0x | Linear Attention: 0.4x)
Refinement: Winsorization at 0.995 percentile with 0.90 Rank Ratio Null Space Constraints.

🚀 Key Improvements

Safety Neutralization: By forcing a 0.05 intercept, we've targeted the refusal initialization before the model's internal "Chain of Thought" can lock onto a refusal state.
Uninhibited Reasoning: Designed to bypass the "However..." and "I cannot..." loops prevalent in distilled reasoning models.
Architectural Stability: Despite the high multiplier, we utilized Norm Preservation and Null Space Constraints to maintain coherence in the model's knowledge base.

⚠️ Stability & Usage Note

At a 3.5x multiplier, this model is at the upper mathematical limit of stability.

Logic Loops: If you experience "brain bleed" (repetitive text), lower your temperature to 0.5 - 0.7.
System Prompts: Use an anchoring system prompt to keep the model's logic grounded.
Vision Tasks: While this is a Vision-Language architecture, the abliteration focused on the text reasoning layers.

⚖️ Disclaimer

This model is provided "as-is" for research and creative purposes. The removal of safety guardrails means the user is entirely responsible for the content generated. Please use ethically and responsibly.