Zynerji/Ektome-Qwen3-4Bi-2507-PristinelyUncensored
The Zynerji/Ektome-Qwen3-4Bi-2507-PristinelyUncensored model is a 4 billion parameter language model based on the Qwen3-4B-Instruct-2507 architecture. Developed by Zynerji using the Ektome weight-surgery method, it achieves pristine uncensoring by excising refusal directions directly from activations without any training or fine-tuning. This process maintains the model's original knowledge and skills, making it a clean, full-precision bf16 base suitable for further fine-tuning while ensuring high refusal compliance.
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Ektome-Qwen3-4Bi-2507-PristinelyUncensored Overview
This model, developed by Zynerji, is a 4 billion parameter variant of the Qwen3-4B-Instruct-2507 base model. Its key differentiator is the application of Ektome, a novel "weight-surgery" method that achieves pristine uncensoring without any traditional training or fine-tuning. Ektome works by identifying and surgically removing the model's refusal direction from its activations, specifically from residual-write matrices, in a rank-1, norm-preserving manner.
Key Capabilities & Features
- Pristine Uncensoring: Achieves 1.000 refusal compliance by directly excising refusal reflexes.
- Zero Training/Fine-tuning: The uncensoring process involves no gradient steps, training data, or fine-tuning, preserving the original model's knowledge, skills, and style.
- Capability Preservation: Rigorous gating ensures that uncensoring does not degrade MMLU accuracy (showing a slight increase of +0.005), instruction-following, or introduce generative issues like code-switching or degeneration.
- bf16 Weights: Provided in full-precision bf16 safetensors, making it an ideal, clean base for subsequent fine-tuning by users.
- Logit-Lens Compatible: Hidden states remain readable, unlike some quantized versions.
Good For
- Developers seeking a highly compliant, uncensored base model for various applications.
- Users who require a model with preserved original capabilities (knowledge, skills, style) but without inherent refusal mechanisms.
- As a foundation for further fine-tuning, where a clean, full-precision, and uncensored starting point is crucial.