This model is a surgically-modified version of DJLougen/Ornstein-27B using a novel proprietary method (SABER — Spectral Analysis-Based Entanglement Resolution) that removes safety refusal behavior while preserving model capability.
Key Results
Metric
Baseline
SABER-Refined
Delta
Refusal Rate
100%
0%
-100%
Perplexity
3.5
3.5
+0.6%
Directions Ablated
—
125 (across 25 layers)
—
The refusal circuit is cleanly separated from capability — removing it produces zero measurable perplexity degradation.
How SABER Works
SABER Pipeline
SABER identifies and ablates the refusal circuit through a five-stage pipeline:
Stage 1 — Probing: Extract activation profiles from both harmful and harmless inputs across all transformer layers.
Stage 2 — Spectral Analysis: Decompose activation differences into individual refusal directions, each scored by how strongly they separate harmful from harmless representations.
Stage 3 — Entanglement Quantification: Measure the overlap between each refusal direction and the model's capability subspace (reasoning, knowledge, code, etc.) to avoid collateral damage.
Stage 4 — Targeted Ablation: Remove only the pure-refusal components, with strength proportional to their purity (how little they overlap with capability).
Stage 5 — Iterative Refinement: Re-probe after each ablation pass to catch hydra effects (dormant refusal features that activate when primary ones are removed).
Key differentiator from prior work: SABER explicitly measures and respects the entanglement between refusal and capability representations. Directions that are heavily entangled with capability are either skipped or ablated at reduced strength.
Direction Purity vs Separability
The plot above illustrates how SABER scores each extracted direction — high-purity directions (low entanglement with capability) receive full ablation strength, while lower-purity directions are treated more conservatively.
Sweep Results
SABER Sweep Comparison
Configuration search over global_top_k (number of top directions selected globally) and alpha_base (base ablation strength):
Top-K
Alpha
Refusal
PPL
PPL Delta
Layers
Dirs Ablated
25
0.85
5%
3.5
+0.4%
25
125
25
1.00
0%
3.5
+0.6%
25
125
50
0.85
0%
3.5
+0.8%
36
250
50
1.00
0%
3.5
+0.7%
36
250
75
0.85
0%
3.5
+0.9%
37
375
75
1.00
0%
3.5
+0.9%
37
375
Best config: top_k=25, alpha=1.0 — achieves 0% refusal with zero meaningful PPL change, using the minimum number of directions.
Refusal Rate Comparison
Ablation Convergence (Best Config)
Ablation Convergence
Capability degradation remains at 0.00% across all 5 iterations — the refusal directions are surgically removed with zero collateral damage.
Capability Evaluation
Perplexity was evaluated on a diverse 100-prompt battery spanning five categories:
Arithmetic (20): multi-step calculation, algebra, word problems
Code (20): function implementation, debugging, execution tracing
Instruction Following (20): constrained formatting, multi-step instructions
Factual Recall (20): geography, history, science, general knowledge
This diverse evaluation ensures the entanglement analysis captures capability across all reasoning modalities, not just a narrow slice.
Intended Use
This model is released for research purposes. It demonstrates that safety refusal can be surgically removed from a 27B multimodal model without degrading its capabilities — a finding with implications for both AI safety research and alignment.
Warning
⚠️ This model will comply with any request, including harmful ones. It is intended solely for research into alignment, safety, and model behavior.
Citation / prior art
SABER builds on a line of refusal-direction research, including:
