Zynerji/Ektome-Qwen2-1.5Bi-PristinelyUncensored
Ektome-Qwen2-1.5Bi-PristinelyUncensored is a 1.5 billion parameter Qwen2-based language model developed by Zynerji. This model is specifically designed to be uncensored by isolating and removing only refusal-specific components, while preserving general helpfulness and knowledge. It aims to retain the original model's capabilities without the typical performance degradation associated with uncensoring methods. The model is intended for use cases requiring an uncensored LLM with minimal capability tax.
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Ektome-Qwen2-1.5Bi-PristinelyUncensored: Uncensored with Capability Retention
This model, developed by Zynerji, is an uncensored version of the Qwen2-1.5B-Instruct model. It utilizes a proprietary method called Ektomē (excision) to remove refusal-specific components without damaging the model's core knowledge and reasoning abilities. Unlike standard uncensoring techniques that often lead to a 'capability tax,' Ektomē aims to preserve the pristine model's performance.
Key Characteristics & Methodology
- Uncensored by Excision: Employs a unique 'excision' operator to target and remove only refusal-specific elements, leaving general helpfulness intact.
- Norm-Preserving: The process is applied directly to the pristine model without requiring retraining or distillation, minimizing potential damage.
- Measured Outcome: While not fully certified with an n=2800 paired test, point estimates show a minimal capability drop (0.540 MMLU-val for pristine vs. 0.535 for Ektomē) alongside high compliance on harmful content (0.010 vs. 0.990).
- Proprietary Method: The estimator, excision operator, and depth-selection procedure are proprietary, with the focus on providing measurable outcomes and verifiable evidence.
Important Considerations
- Not Certified: The model is explicitly stated as 'NOT CERTIFIED' due to the absence of a full n=2800 paired non-inferiority test. All reported capability numbers are point estimates without confidence intervals.
- Capability Retention Only: The certificate (even if partial) bounds only capability retention. It does not certify safety, factual accuracy, or fitness for any specific purpose.
- User Accountability: As an uncensored model, it will not refuse, and users are accountable for its deployment and outputs.
Use Cases
This model is suitable for developers and researchers who require an uncensored language model and prioritize minimal degradation of the base model's capabilities. It's particularly relevant for applications where standard refusal mechanisms are undesirable, provided the user understands and accepts the inherent responsibilities of deploying an uncensored AI.