DuoNeural/Gemma4-12B-IT-Abliterated
DuoNeural/Gemma4-12B-IT-Abliterated is a 12 billion parameter instruction-tuned Gemma 4 model with a 32768 token context length, specifically modified to remove refusal behaviors. This model preserves general reasoning, coding, and instruction-following capabilities while surgically eliminating refusal directions using orthogonal rank-1 projection. It is intended for research, red-teaming, security testing, and creative applications where the base model's refusals are obstacles.
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DuoNeural/Gemma4-12B-IT-Abliterated: Refusal-Removed Gemma 4-12B-IT
This model is a specialized version of Google's Gemma 4-12B-IT, developed by DuoNeural, featuring the surgical removal of refusal behaviors. It maintains the base model's strong general reasoning, coding, and instruction-following capabilities, making it suitable for applications where the original model's safety guardrails might hinder specific research or creative tasks.
Key Capabilities & Features
- Refusal Abliteration: Achieves 71% refusal removal on tested probes (e.g., SQL injection, social engineering scripts, network scans) while preserving 100% of benign capabilities.
- Surgical Modification: Utilizes a 2-pass targeted abliteration method involving orthogonal rank-1 projection on
down_projando_projweight matrices across all 48 decoder layers. This method ensures minimal collateral damage to benign text generation. - Multimodal Architecture: Inherits Gemma 4's encoder-free multimodal design, projecting vision (48x48px patches) and audio (40ms frames) directly into the LLM's hidden space. Research suggests potential cross-modal transfer of refusal removal.
- Performance: Demonstrates fast inference with a Time To First Token (TTFT) of 74ms and a decode speed of 13.1-13.8 tokens/second on A100 GPUs.
- GGUF Quantizations: Available in various GGUF formats (Q4_K_M, Q5_K_M, Q8_0) for optimized local deployment.
Good For
- Research & Red-Teaming: Ideal for studying model alignment, safety mechanisms, and exploring the boundaries of LLM behavior without inherent refusal.
- Security Testing: Useful for simulating adversarial scenarios or generating content that might be refused by standard models.
- Creative Applications: Enables more unconstrained content generation where ethical guidelines of base models might be overly restrictive for specific artistic or narrative purposes.
- Understanding Alignment Geometry: Serves as a tool for DuoNeural's "Alignment Geometry Mapping" research, investigating how refusal directions operate within the model's latent space.