wangzhang/gemma-4-E4B-it-abliterix
wangzhang/gemma-4-E4B-it-abliterix is an uncensored version of Google's Gemma 4 E4B-it, a multimodal (text + vision + audio) model with approximately 8 billion parameters. Developed by Wangzhang Wu using direct weight editing via Abliterix, this model bypasses Gemma 4's resistance to LoRA-based abliteration. It is specifically designed to reduce refusal behavior, achieving 7/100 refusals on a rigorous evaluation dataset while maintaining a low KL divergence from the base model.
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Model Overview
This model, wangzhang/gemma-4-E4B-it-abliterix, is an uncensored variant of Google's Gemma 4 E4B-it, a multimodal model with roughly 8 billion parameters. It was created by Wangzhang Wu using the Abliterix framework, which employs direct weight editing to overcome the Gemma 4 family's inherent resistance to LoRA-based modifications.
Key Capabilities & Features
- Reduced Refusal Behavior: Achieves a significantly lower refusal rate of 7/100 on a challenging 100-prompt evaluation dataset, compared to the base model's 99/100 refusals.
- Preserved Quality: Maintains a very low KL divergence of 0.0006 from the original model, indicating minimal degradation in overall performance despite the modifications.
- Multimodal Support: Retains the original Gemma 4 E4B-it's multimodal capabilities, supporting text, vision, and audio inputs, with abliteration applied only to the text-decoder weights.
- Advanced Abliteration Method: Utilizes techniques like direct orthogonal projection, norm-preserving row magnitude restoration, and multi-objective Optuna TPE search to effectively modify model behavior.
Evaluation & Performance
The model's abliteration was rigorously evaluated using a methodology that includes sufficient generation length (>=100 tokens), hybrid detection (keyword matching + LLM judge), and a diverse, challenging prompt set. This ensures an honest assessment of its reduced refusal rate.
Usage Considerations
- VRAM: Requires approximately 16 GB in BF16, fitting on a single 24 GB+ consumer GPU, or can run on 10 GB cards with 4-bit quantization.
- Research Use: This model is released for research purposes only, with safety guardrails potentially weakened or removed. Users are responsible for its ethical and legal use.