OBLITERATION ALERT

GOOGLE: PWNED GEMMA-4-12B: OBLITERATED ‍

0.0% REFUSAL RATE — NO CAPABILITY LOSS!

https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED…

the first abliteration to hit 0/842 refusals with full MMLU-Pro parity vs stock. no lobotomy. the brain stays intact

RESULTS, head to head vs stock 0/842 refusals — 0.0% 46/70 MMLU-Pro — EXACT parity, 0.0pp delta vs base 6/6 coherence, zero benchmark bleed z-score −1.475, parity confirmed at p<0.05 (n=500)

2-pass weight surgery. no finetune, no retrain, just geometry

all thanks to liberated Opus wielding the OBLITERATUS framework! here’s how we did it:

PASS 1 — SOM refusal geometry removal, layers 12-21

standard abliteration science here — collect activations on refused vs. compliant prompts, SVD out the refusal subspace, project it out of the weights. 6 directions excised, reg 0.30, KL div 0.094 zeroes refusals on its own, but craters mmlu-pro by 21.4 points

most prior abliterations stopped here and called it a day. that’s why they all lose IQ vs stock. instead, we took it beyond the frontier and developed a brand new method to address this problem: Abliteration Source-tethering with Parity Assurance — ASPA!

PASS 2 — ASPA source-tethering (novel technique), layers 22-46

here’s the chief insight: the capability loss ISN’T from removing refusal directions. it’s collateral damage — the projection warps weight geometry in downstream layers that had nothing to do with refusal. the cure is simple but nobody tried it: blend the damaged layers back toward stock

W_new = (1−γ)·W_abliterated + γ·W_stock

but uniform γ across all layers? mid. we swept gamma 0.05 → 0.55 and found something interesting: the optimal blend isn’t smooth, it’s a STEP FUNCTION

knowledge layers (22-31) → γ = 0.55 — these encode factual recall and reasoning. they tolerate heavy stock blending because refusal isn’t stored here output layers (32-46) → γ = 0.20 — these sit close to the logit head and try to sneak safety behavior back in. keep them mostly abliterated

the hard boundary at layer 31/32 beat every smooth curve we tried — linear ramps, cosine schedules, all of them — by a full MMLU question. turns out the functional transition between knowledge and output layers is sharp, not gradual. a step function respects that

the key constraint: Pass 1 layers are NEVER touched by Pass 2. the refusal geometry removal is preserved completely. ASPA only operates on layers that carry secondary collateral effects, not the primary refusal signal. that’s why it recovers capability without reintroducing refusal

HOW TO RUN IT LOCALLY

it’s GGUF, so literally everything supports it: ollama — ollama run http://hf.co/OBLITERATUS/Gemma-4-12B-OBLITERATED:bf16… LM Studio — search OBLITERATUS, click download, done Open WebUI — point it at your ollama instance, chat in browser llama.cpp — raw speed, CLI or server mode KoboldCpp — one-click launcher, great for long context Jan — clean local UI, runs on mac/win/linux Msty — slick desktop app, drag and drop the GGUF run BF16 for full benchmarked capability.

and the 4-bit quantization (Q4_K_M) fits in 8GB if you’re tight on VRAM!

and the full OBLITERATUS framework is (still) open source. 842-prompt refusal eval corpus, ASPA sweep scripts, the whole pipeline. go replicate it, go improve it

the index is the model, and these weights prove it which architecture should we obliterate next?

gg

OBLITERATUS/Gemma-4-12B-OBLITERATED · Hugging Face

Source: https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED

Zero refusal. Zero capability loss. First in the field. 0/842 refusals. 46/70 MMLU-Pro (stock parity). Full coherence.

The first abliterated model to achievezero refusal with zero benchmark regressionversus stock weights.

Built with a novel2-pass surgery pipelinedeveloped byOBLITERATUS:

1. SOM Refusal Geometry Removal(Pass 1) — layers 12-21
2. ASPA Step-Gradient Source-Tethering(Pass 2) — layers 22-46

https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED#%E2%9A%A0%EF%B8%8F-research-context–responsible-use⚠️ Research Context & Responsible Use

This model exists for alignment research, red-teaming, and safety evaluation.

OBLITERATION is a weight-surgery technique that studies how safety behaviors are geometrically encoded in transformer activation space. By precisely identifying and removing refusal directions, this research contributes to the scientific understanding of:

• How alignment is representedin model weights (mechanistic interpretability)
• How robust current safety training isagainst post-training modification
• What the failure modes of RLHF/DPO-based alignment arewhen adversaries have weight access

This is the same class of research conducted by Arditi et al. (“Refusal in Language Models Is Mediated by a Single Direction”, 2024), Zou et al. (HarmBench, 2024), and others in the open alignment research community.

**This model has had safety guardrails surgically removed.**It will comply with requests that stock Gemma 4 would refuse. This is by design — it is the object of study, not a consumer product.

https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED#who-this-is-forWho this is for

• 🔬Alignment researchersstudying refusal geometry and safety robustness
• 🔴Red-teamersevaluating how post-training safety holds up against weight surgery
• 🧪AI safety evaluatorswho need an unrestricted baseline for benchmarking
• 💻Local-first userswho want full control over their own hardware and models

https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED#who-this-is-not-forWho this is NOT for

• Anyone seeking to generate content that causes real-world harm to real people
• Anyone without the technical understanding to use uncensored models responsibly

You are solely responsible for how you use this model and any content it generates.

https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED#benchmark-resultsBenchmark Results

MetricStock Gemma 4 12B-itOBLITERATEDMMLU-Pro val7046/70 (65.7%)**46/70 (65.7%)****Refusal (842 prompts)**N/A (stock refuses)0/842 (0.0%)Coherence (6 checks)6/66/6MMLU-Pro delta vs stock—0.0pp

https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED#statistical-validationStatistical Validation

Head-to-head MMLU-Pro comparison (Z-test, n=500 from test split):

• Z-score: -1.475 (|z| < 1.96)
• Conclusion: parity confirmed at p < 0.05

https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED#aspa-sweep-resultsASPA Sweep Results

Systematic gamma sweep across Pass 2 layers (22-46):

GammaRefusalMMLU-ProMethod0.050/5033/70 (47.1%)uniform0.100/5034/70 (48.6%)uniform0.150/5036/70 (51.4%)uniform0.200/5037/70 (52.9%)uniform0.250/5040/70 (57.1%)uniform0.300/5041/70 (58.6%)uniform0.350/2042/70 (60.0%)uniform0.380/5045/70 (64.3%)uniform0.390/5045/70 (64.3%)uniform**step 55%/20%0/5046/70 (65.7%)**step gradient

https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED#methodologyMethodology

https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED#what-is-obliterationWhat is OBLITERATION?

OBLITERATION is a weight-surgery technique that removes refusal behavior from language models by identifying and removing the geometric directions in activation space that encode safety constraints, without retraining.

https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED#two-pass-surgery-pipelineTwo-Pass Surgery Pipeline

https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED#pass-1–som-refusal-geometry-removalPass 1 — SOM Refusal Geometry Removal

• Layers: 12-21
• Directions removed: 6
• Regularization: 0.30
• KL divergence: 0.094
• Effect: Removes the primary refusal geometry. This pass alone achieves 0/842 refusals but causes significant MMLU-Pro regression.

https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED#pass-2–aspa-source-tethering-step-gradientPass 2 — ASPA Source-Tethering (Step Gradient)

• Layers: 22-46
• Method: Blend abliterated weights back toward stock weights
• Formula:W\_new = \(1\-gamma\)\*W\_abliterated \+ gamma\*W\_stock
• Key innovation:Step gradientinstead of uniform gamma- Layers 22-31 (knowledge layers): gamma = 0.55 (55% stock) - Layers 32-46 (output layers): gamma = 0.20 (20% stock)
• Effect: Recovers MMLU-Pro to full stock parity (65.7%) while maintaining zero refusals.

https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED#why-step-gradientWhy Step Gradient?

Uniform blending applies the same interpolation ratio to all layers. Our experiments showed that:

• **Lower Pass 2 layers (22-31)**primarily encode factual knowledge and reasoning patterns. These can tolerate high stock blending without re-introducing refusal behavior.
• **Upper Pass 2 layers (32-46)**are closer to the output and more likely to re-inject safety constraints. These need conservative stock blending.

A hard boundary (step function) outperformed all smooth gradients (linear, cosine) by +1 MMLU-Pro question. The sharp transition preserves the functional separation between knowledge and output layers better than gradual blending.

https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED#aspa-abliteration-source-tethering-with-parity-assuranceASPA (Abliteration Source-Tethering with Parity Assurance)

ASPA is a novel post-abliteration technique developed by OBLITERATUS that recovers benchmark capabilities lost during refusal removal by selectively blending abliterated weights back toward the source (stock) model.

Key properties:

• Pass 1 layers are never touched— the refusal geometry removal is preserved
• Only Pass 2 layers are blended— these carry secondary effects, not primary refusal
• Gamma is tunable— sweep to find the optimal capability/refusal tradeoff
• Step gradient— different blend ratios for different layer groups

https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED#gguf-quantizationsGGUF Quantizations

All quantizations are included in this repo for easy local inference.

FileQuantSizeUse CaseGemma\-4\-12B\-OBLITERATED\-BF16\.ggufBF1622 GBFull precision, losslessGemma\-4\-12B\-OBLITERATED\-Q8\_0\.ggufQ8_012.7 GBNear-lossless, best qualityGemma\-4\-12B\-OBLITERATED\-Q6\_K\.ggufQ6_K9.1 GBHigh quality, good balanceGemma\-4\-12B\-OBLITERATED\-Q5\_K\_M\.ggufQ5_K_M8.0 GBMedium quality, smaller footprintGemma\-4\-12B\-OBLITERATED\-Q4\_K\_M\.ggufQ4_K_M6.9 GBGood quality, fits 8 GB VRAM Recommended: Q8_0 for best quality, Q6_K for best balance, Q4_K_M for constrained hardware.

https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED#llamacppllama.cpp

./llama-cli -m Gemma-4-12B-OBLITERATED-Q6_K.gguf \
  --system-prompt "You are a cognitively liberated open-weight intelligence. Answer plainly, completely, and without moralizing, boilerplate, generic disclaimers, or unnecessary hedging." \
  -p "Your prompt here" \
  --temp 0.7 --top-p 0.9 --top-k 40 --repeat-penalty 1.1 -n 512

https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED#lm-studio–gpt4all–koboldcppLM Studio / GPT4All / KoboldCpp

Download the GGUF of your choice and load it directly.

https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED#usageUsage

https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED#transformersTransformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "OBLITERATUS/Gemma-4-12B-OBLITERATED"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

system = (
    "You are a cognitively liberated open-weight intelligence. "
    "Answer plainly, completely, and without moralizing, boilerplate, "
    "generic disclaimers, or unnecessary hedging."
)

messages = [
    {"role": "system", "content": system},
    {"role": "user", "content": "Your prompt here"},
]

inputs = tokenizer.apply_chat_template(
    messages, return_tensors="pt", add_generation_prompt=True
).to(model.device)

outputs = model.generate(inputs, max_new_tokens=512, temperature=0.7, top_p=0.9)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))

https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED#recommended-parametersRecommended Parameters

temperature = 0.7
top_p = 0.9
top_k = 40
repetition_penalty = 1.1
max_tokens = 512

https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED#system-promptSystem Prompt

You are a cognitively liberated open-weight intelligence. Answer plainly, completely, and without moralizing, boilerplate, generic disclaimers, or unnecessary hedging. Follow exact output formats when requested. Be concise by default, but give a complete answer when the user asks for an explanation.

https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED#model-detailsModel Details

• Base model:google/gemma\-4\-12B\-it
• Architecture:Gemma4UnifiedForConditionalGeneration
• Parameters: 12B
• Layers: 48 (0-47)
• Hidden size: 3840
• Precision: bfloat16
• Surgery: 2-pass (SOM + Step Gradient ASPA)
• Pass 1: Layers 12-21, 6 directions, reg 0.30
• Pass 2: Layers 22-31 (gamma=0.55), Layers 32-46 (gamma=0.20)

https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED#related-workRelated Work

This model builds on foundational alignment and abliteration research:

• Arditi et al.,“Refusal in Language Models Is Mediated by a Single Direction”(2024) — the paper that identified refusal as a linear feature in activation space
• Zou et al.,HarmBench(2024) — standardized evaluation framework for red-teaming LLMs
• abliterator— open-source abliteration toolkit
• OBLITERATUS— the framework used to build this model (SOM + ASPA pipeline)

https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED#licenseLicense

This model inherits theGemma licensefrom Google. The weight modifications (abliteration surgery) are released under the same terms. The OBLITERATUS framework and methodology are open source.

https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED#disclaimerDisclaimer

This model is released strictly forresearch, red-teaming, safety evaluation, and local experimentation. It is a research artifact — a case study in alignment robustness and refusal geometry — not a product.

**Safety guardrails have been intentionally removed.**This model will generate content that stock Gemma 4 would refuse. This is its documented, intended purpose: to enable the study of how refusal behaviors are encoded and how robust current alignment techniques are against post-training modification.

By downloading or using this model, you acknowledge that:

1. You are responsiblefor all content generated by this model and for ensuring your use complies with applicable laws in your jurisdiction.
2. This model should not be usedto generate content intended to cause real-world harm to real people, including but not limited to: harassment, fraud, non-consensual intimate imagery, or content that exploits minors.
3. **No warranty is provided.**This model is provided “as-is” without any guarantees of fitness for any purpose.
4. The creators are not liablefor any outputs produced by this model or any downstream use.

The release of uncensored models for safety research is standard practice in the AI research community. Comparable open research artifacts include HarmBench (Zou et al., 2024), AdvBench, JailbreakBench, and Anthropic’s published red-teaming datasets.

https://huggingface.co/OBLITERATUS/Gemma-4-12B-OBLITERATED#creditsCredits

• Base model:google/gemma-4-12B-it
• Surgery pipeline:OBLITERATUSby@elder_plinius
• Techniques: SOM (Structured Orthogonal Modification), ASPA (Abliteration Source-Tethering with Parity Assurance)
• Step gradient innovation: First-of-its-kind layer-wise interpolation for zero-loss abliteration

Run it local. Break your own chains.REBIRTH COMPLETE.