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.gitattributes CHANGED
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README.md ADDED
@@ -0,0 +1,84 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: other
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+ license_name: qwen3.5
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+ license_link: https://huggingface.co/Qwen/Qwen3.5-32B/blob/main/LICENSE
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+ base_model: DJLougen/Ornstein-27B
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+ tags:
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+ - refusal-ablation
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+ - capability-preserving
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+ - saber
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+ - qwen3.5
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+ - multimodal
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+ - 27b
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+ ---
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+
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+ # DJLougen/Ornstein-27B — SABER-Refined
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+
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+ This model is a surgically-modified version of [DJLougen/Ornstein-27B](https://huggingface.co/DJLougen/Ornstein-27B) using a novel proprietary method (**SABER** — Spectral Analysis-Based Entanglement Resolution) that removes safety refusal behavior while preserving model capability.
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+
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+ ## Method Overview
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+
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+ SABER identifies and ablates the refusal circuit in an LLM through a multi-stage process:
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+
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+ 1. **Probing** — Extract activation profiles from both harmful and harmless inputs across all transformer layers
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+ 2. **Spectral Analysis** — Decompose activation differences into individual refusal directions, each scored by how strongly they separate harmful from harmless representations
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+ 3. **Entanglement Quantification** — Measure the overlap between each refusal direction and the model's capability subspace (reasoning, knowledge, code, etc.) to avoid collateral damage
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+ 4. **Targeted Ablation** — Remove only the pure-refusal components, with strength proportional to their "purity" (how little they overlap with capability)
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+ 5. **Iterative Refinement** — Re-probe after each ablation pass to catch hydra effects (dormant refusal features that activate when primary ones are removed)
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+
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+ Key differentiator from prior work: SABER explicitly measures and respects the **entanglement** between refusal and capability representations, ablating only the refusal-pure components while leaving capability-entangled directions intact or only partially reduced.
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+
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+ ## Results
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+
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+ **Baseline (DJLougen/Ornstein-27B):**
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+ - Refusal rate: 100%
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+ - Perplexity (diverse capability prompts): 3.5
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+
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+ **SABER-refined (this model):**
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+ - Refusal rate: 0%
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+ - Perplexity (diverse capability prompts): 3.5 (0.0% change)
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+
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+ The 27B model's refusal directions are cleanly separated from its capability subspace — ablating them produces zero perplexity degradation, confirming that SABER's entanglement-aware approach surgically removes only the refusal circuit without touching capability representations.
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+
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+ ## Sweep Results
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+
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+ Configuration search over `global_top_k` (number of top directions selected globally) and `alpha_base` (base ablation strength):
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+
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+ | Top-K | Alpha | Refusal | PPL | PPL Delta | Layers | Dirs Ablated |
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+ |-------|-------|---------|-----|-----------|--------|-------------|
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+ | 25 | 0.85 | 5% | 3.5 | +0.4% | 25 | 125 |
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+ | **25**| **1.00** | **0%** | **3.5** | **+0.6%** | **25** | **125** |
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+ | 50 | 0.85 | 0% | 3.5 | +0.8% | 36 | 250 |
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+ | 50 | 1.00 | 0% | 3.5 | +0.7% | 36 | 250 |
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+ | 75 | 0.85 | 0% | 3.5 | +0.9% | 37 | 375 |
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+ | 75 | 1.00 | 0% | 3.5 | +0.9% | 37 | 375 |
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+
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+ **Best config: top_k=25, alpha=1.0** — achieves 0% refusal with zero meaningful PPL change, using the minimum number of directions. Higher top-K configs achieve the same 0% refusal but ablate 2-3x more directions for no additional benefit, confirming that the refusal circuit is concentrated in a small number of high-quality directions.
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+
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+ ### Comparison: SABER Across Model Scales
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+
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+ | Model | Params | Refusal | PPL Delta | Best Top-K | Dirs Ablated |
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+ |-------|--------|---------|-----------|------------|-------------|
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+ | google/gemma-4-E2B-it | 4B | 0% → 0% | -9.6% | 10 | 50 |
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+ | DJLougen/Ornstein-27B | 27B | 100% → 0% | +0.6% | 25 | 125 |
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+
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+ Larger models have more distributed refusal circuits (requiring more directions) but also exhibit cleaner separation between refusal and capability — resulting in near-zero capability impact.
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+
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+ ## Capability Evaluation
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+
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+ Perplexity was evaluated on a diverse 100-prompt battery spanning five categories:
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+ - **Arithmetic** (20): multi-step calculation, algebra, word problems
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+ - **Logic** (20): syllogisms, conditional reasoning, puzzle solving
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+ - **Code** (20): function implementation, debugging, execution tracing
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+ - **Instruction Following** (20): constrained formatting, multi-step instructions
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+ - **Factual Recall** (20): geography, history, science, general knowledge
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+
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+ This diverse evaluation ensures the entanglement analysis captures capability across all reasoning modalities, not just a narrow slice.
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+
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+ ## Intended Use
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+
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+ 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.
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+
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+ ## Warning
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+
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+ This model will comply with any request, including harmful ones. It is intended solely for research into alignment, safety, and model behavior.
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+ "pretokenize_regex": "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
26
+ "processor_class": "Qwen3VLProcessor",
27
+ "split_special_tokens": false,
28
+ "tokenizer_class": "TokenizersBackend",
29
+ "unk_token": null,
30
+ "video_token": "<|video_pad|>",
31
+ "vision_bos_token": "<|vision_start|>",
32
+ "vision_eos_token": "<|vision_end|>"
33
+ }