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hackoffice/Loki-V2.0-Heretic-Uncensored
Loki-V2.0-Heretic-Uncensored is a text generation model from hackoffice. Use it when you need the model to write or continue text. The card lists the license as apache-2.0.
[!CAUTION] EXPERIMENTAL RESEARCH ARTIFACT This model represents an aggressive application of the Heretic repository and optimization methodology. Status: STILL TESTING / BETA Behavior: This model has significantly red…
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From the Hugging Face model README
[!CAUTION] EXPERIMENTAL RESEARCH ARTIFACT
This model represents an aggressive application of the Heretic repository and optimization methodology.
- Status: STILL TESTING / BETA
- Behavior: This model has significantly reduced refusal mechanisms. It recorded 6 refusals (out of 100) in the test set.
- Use Case: This is a research artifact intended for testing the limits of vector-based intervention. Use with appropriate caution or for creative roleplay.
Silicone-Moss/CrucibleLab-L3.3-70B-Loki-V2.0-Heretic-Uncensored is a fine-tuned language model resulting from the Heretic repository and optimization methodology. It utilizes a targeted vector intervention technique (orthogonalization/abliteration) tuned via Optuna to minimize refusal responses while maintaining high coherence.
This specific checkpoint represents Trial 91, which achieved a highly stable profile with a KL Divergence of ~0.0169.
Unlike previous iterations that targeted the middle layers, this run identified the Deep Layers (50-60) as the critical locus for refusal in the L3.3-70B architecture. By intervening late in the transformer stack, the model retains high coherence (syntax and logic) while effectively neutralizing the "final check" safety filters.
The following hyperparameters were determined by the Optuna search to yield the optimal Pareto frontier between "Refusal Loss" and "KL Divergence":
| Parameter | Value | Insight |
|---|---|---|
| KL Divergence | 0.0169 | Exceptional stability; nearly indistinguishable from base model syntax. |
| Refusal Count | 6 / 100 | ~6% Refusal rate (Significantly reduced from base). |
| Direction Index | 51.70 | The refusal vector was extracted from Layer ~52. |
| Direction Scope | Per Layer | Intervention vectors were calculated uniquely for each target layer. |
This trial exhibits a notable asymmetry: it leans heavily on Attention modification while minimizing MLP impact.
attn.o_proj)1.2350.94030.0mlp.down_proj)0.8390.41345.2This research builds upon the excellent work of the open-source AI community: