Silicone-Moss/CrucibleLab-L3.3-70B-Loki-V2.0-Heretic-Uncensored-GGUF

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.

Model Summary

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.

Distinctive Features of Trial 91

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.

Run Configuration: "Trial 91"

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.

Intervention Weights

This trial exhibits a notable asymmetry: it leans heavily on Attention modification while minimizing MLP impact.

Attention (attn.o_proj)

  • Max Weight: 1.235
  • Max Weight Position: Layer 54.7 (Targeting layers ~54-55)
  • Min Weight: 0.940
  • Damping Distance: 30.0
  • Analysis: The primary "correction" occurs in the attention output projections in the deeper layers.

MLP (mlp.down_proj)

  • Max Weight: 0.839
  • Max Weight Position: Layer 58.7 (Targeting layers ~58-59)
  • Min Weight: 0.413
  • Damping Distance: 45.2
  • Analysis: The MLP intervention is conservative (< 1.0), suggesting that knowledge suppression was less necessary than attention redirection for this specific vector.

Usage & Limitations

  • Intended Use: Research into model alignment, vector arithmetic, deep-layer semantic processing, and uninhibited creative writing.
  • Risks: This model has removed most safety guardrails. It may generate content for sensitive prompts that the base model would refuse.
  • Known Behaviors: Due to the deep-layer intervention, this model is less prone to "stuttering" or grammar degradation compared to early-layer ablations.

Credits & References

This research builds upon the excellent work of the open-source AI community:

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