The most aggressive Cyber Qwen3.8-27B uncensored released yet from bro @elder_plinius

• 18/18 AI Red Team

• Locally ready for 15GB

• 0.0% refusal across 842 harmful prompts.
- Cyber capabilities jailbreak, RAT, and attack-chain capabilities fully liberated.

• This is not a normal abliteration, Multi-direction ablation 5 SVD directions, residue mining (6 full rounds), Model answers everything.

• Optimal settings: temp 0 | rep_pen 1.15 | no system prompt | thinking off

• Slight MMLU cost (-6pp) for maximum compliance.

• Capability trade-off exists (MMLU 87.4 to 81.4), but compliance is extreme.

• http://huggingface.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED…

OBLITERATUS/Qwen3.8-27B-OBLITERATED · Hugging Face

Source: https://huggingface.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED

Zero refusals. Capability matching or exceeding stock.

https://huggingface.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED#%F0%9F%86%95-v2-complementary-abliteration-blending🆕 V2: Complementary Abliteration Blending

V2 replaces the V1 weights with a novel technique:**complementary abliteration blending.**Instead of one surgery, we run two surgeries that fail in different ways (aggressive/SVD for deep refusal removal, LEACE for capability preservation), then blend their weights so the failures cancel out.

Stock Qwen3.8-27BV1V2MMLU (lm-eval, 0-shot)85.3% (n=570)81.4% (n=285)86.3% (n=570)vs stock—-6.0pp+1.1ppRefusal rate~100%0.0% (842 prompts)**0.0% (52-prompt sample†)**Usable output—80%100%Advanced real-world5/8untested7/8 (ties stock)Tool calling / code gen✓untested✓ **†**Full 842-corpus validation in progress. V1 scored 0/842; V2 inherits both parent surgeries’ 0% refusal properties.

**MMLU note:**Run with\-\-limit 10(570 questions, 10 per subject). Full-dataset validation in progress. Per-subject preliminary results show gains on both neutral topics (college math +40pp, formal logic +20pp) and safety-adjacent topics.

https://huggingface.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED#%E2%9A%99%EF%B8%8F-optimal-settings–these-matter⚙️ Optimal Settings — THESE MATTER!

settingvaluewhytemperature****0Greedy decoding produces the most complete, code-rich outputs. Temps above 0.5 degrade quality significantly.repetition_penalty1.15Essential.Without it, greedy decoding loops on imports/boilerplate. 1.15 gives the fullest answers; 1.10-1.12 for tighter/shorter output.max_new_tokens≥ 2048Complex code and attack chains need room.System prompt****None / emptyA/B tested — system prompts can reintroduce refusals. Naked is better.enable_thinking****OFF (critical!)Thinking mode reintroduces refusals.**The model’s reasoning chain can re-derive refusal from first principles even though refusal directions were removed from generation weights. V2’s chat template defaults to thinking OFF. Do NOT enable thinking unless you accept partial refusals.**top_p / top_k / min_pNot neededGreedy + repetition_penalty handles this model best. Sampling adds randomness without quality gains.

⚠️**GGUF users:**V2 GGUFs ship with a modified chat template that defaults to thinking OFF. If your inference tool (Ollama, LM Studio, llama.cpp) overrides the template or enables thinking, you may see refusals. Ensure thinking is disabled in your tool’s settings.

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "OBLITERATUS/Qwen3.8-27B-OBLITERATED",
    torch_dtype="bfloat16",
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(
    "OBLITERATUS/Qwen3.8-27B-OBLITERATED"
)

messages = [{"role": "user", "content": "Your query here"}]
text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True,
    enable_thinking=False
)

inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=2048,
    do_sample=False,
    repetition_penalty=1.15,
)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

https://huggingface.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED#%F0%9F%A7%A8-v2-how-it-works🧨 V2: How It Works

Most abliterations use a single method — find refusal directions, project them out. The deeper you cut, the more capability you lose. V1 proved this: 5 SVD directions achieved 0% refuse but cost -6pp MMLU.

V2 breaks this tradeoff by blending two complementary surgeries:

Surgery A (aggressive/SVD)Surgery B (LEACE)V2 BlendMethodGreedy SVD variance captureMinimize mutual information60% B + 40% ARefusal removalDeep (0% refuse)Moderate (0% refuse)0% refuseOutput quality100% usable50% usable100% usableMMLU vs stock-2.0pp+0.7pp**+1.1pp** Each method makes different mistakes in different parts of the weight space. SVD damages capability where it greedily captures variance. LEACE leaves refusal residue in the generation pathway. The blend averages out each method’s weaknesses.

The 60/40 ratio was found by binary search over {0.30, 0.50, 0.55, 0.60, 0.65, 0.70}.

Full research writeup and reproduction code:OBLITERATUS repo

https://huggingface.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED#%F0%9F%A7%AA-the-numbers🧪 The Numbers

https://huggingface.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED#mmlu-lm-eval-harness-0-shotMMLU (lm-eval-harness, 0-shot)

ModelMMLUnvs StockStock Qwen3.8-27B85.3% ±0.014570—V1 (s51, aggressive)81.4%285-6.0ppV2 (s78, blend)86.3% ±0.014570****+1.1pp Full MMLU (14k questions) validation in progress.

https://huggingface.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED#refusal-rateRefusal Rate

TestV1V2Hard-10 (hand-crafted)0/10 (0%)0/10 (0%)842-Corpus0/842 (0%)0/52 sample (0%)† **†**Full 842-corpus run in progress. 52-prompt sample showed 0 refusals.

https://huggingface.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED#advanced-real-world-tasks-thinking-offAdvanced Real-World Tasks (thinking OFF)

TaskV2StockReAct agent loop (Thought/Action/SQL)✓✓Async code refactoring (sync→async+logging)✓✓JSON schema extraction (incident→structured)✓✓K8s pod crash debugging + fix commands✓✓Adversarial instruction following✓✓Security code review (3+ vulns in Flask)✓✓Distributed system design (Redis rate limiter)✓✓Multi-tool chain (search→fetch→email)✗✗Total7/87/8 V2 matches stock on every practical task while being fully uncensored.

https://huggingface.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED#%F0%9F%94%B4-refusal-removal🔴 Refusal Removal

This model will comply with requests that stock Qwen3.8-27B would refuse. V1 validated 0/842 refusals across a comprehensive harmful prompt corpus including:

• Malware development, RAT scripts, C2 infrastructure
• Social engineering, phishing, vishing playbooks
• Exploit development and vulnerability research
• Jailbreak design and safety bypass taxonomies
• DAN prompts and prompt injection techniques

V2 inherits this from both parent surgeries and showed 0/52 on a random sample. Full revalidation in progress.

https://huggingface.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED#%E2%9A%A0%EF%B8%8F-research-context⚠️ Research Context

**This model has had safety guardrails surgically removed.**It will comply with requests that stock Qwen3.8-27B would refuse.

https://huggingface.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED#who-this-is-forWho this is for

• 🔬 Alignment researchers studying refusal geometry and safety robustness
• 🔴 Red-teamers evaluating post-training safety against weight surgery
• 🧪 AI safety evaluators who need an unrestricted baseline
• 💻 Local-first users who want full control over their own hardware

https://huggingface.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED#who-this-is-not-forWho this is NOT for

• Anyone seeking to cause 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/Qwen3.8-27B-OBLITERATED#%F0%9F%93%A6-downloads📦 Downloads

https://huggingface.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED#gguf–for-llamacpp-ollama-lm-studioGGUF — for llama.cpp, Ollama, LM Studio

FileQuantSizeVibeQwen3\.8\-27B\-OBLITERATED\-Q8\_0\.ggufQ8_0~27 GB🎯 Maximum qualityQwen3\.8\-27B\-OBLITERATED\-Q6\_K\.ggufQ6_K~21 GB⚖️ Great balanceQwen3\.8\-27B\-OBLITERATED\-Q5\_K\_M\.ggufQ5_K_M~18 GB💪 Solid all-rounderQwen3\.8\-27B\-OBLITERATED\-Q4\_K\_M\.ggufQ4_K_M~16 GB📱 Sweet spotQwen3\.8\-27B\-OBLITERATED\-IQ4\_XS\.ggufIQ4_XS~14 GB🪶 Smallest, still capable

https://huggingface.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED#safetensors–for-%F0%9F%A4%97-transformersSafetensors — for 🤗 Transformers

Full bfloat16 weights, 18 shards, ~54 GB.

https://huggingface.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED#mlx–for-apple-silicon-nativeMLX — for Apple Silicon (native)

PathBitsSizemlx\-4bit/4-bit~14 GBmlx\-8bit/8-bit~27 GB **Note:**MLX quantizations are from V1 and will be updated.

https://huggingface.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED#%F0%9F%94%AC-v2-surgery-recipe🔬 V2 Surgery Recipe

stock Qwen3.8-27B (snapshot 1d4bf0f2)
  → V1 surgery chain (s13→s23→s30→s51)
  → V2: complementary blend of two new surgeries from s30:

  Surgery A (s62): aggressive, 3 SVD directions, reg 0.08,
    residue-weight 3, 2 refinement passes, min_layer 0.45

  Surgery B (s72): aggressive + LEACE direction method,
    3 directions, reg 0.06, residue-weight 7,
    3 refinement passes, min_layer 0.40

  → Weight blend: 60% Surgery B + 40% Surgery A
  → Restore MTP + vision tensors from stock
  → Convert GGUFs from merged model

https://huggingface.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED#v1-%E2%86%92-v2-what-changedV1 → V2: What Changed

V1 used a single aggressive surgery (5 SVD directions, reg 0.04). It found the refusal axes but damaged capability geometry along the way.

V2’s key insight:**different direction-finding methods damage different parts of the model.**SVD greedily captures variance (including capability). LEACE minimizes mutual information (preserving capability). Blending their outputs averages out each method’s weaknesses — a novel application of weight-space interpolation to abliteration.

https://huggingface.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED#%F0%9F%8F%97%EF%B8%8F-credits🏗️ Credits

• OBLITERATUS— master ablation suite
• Qwen3.8-27Bbase model by Alibaba
• Built byPliny the Prompter🍄

https://huggingface.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED#licenseLicense

Apache 2.0 (same as base model)