nasmtrcs/Qwen3.8-27B-OBLITERATED

VISIONConcurrent Unit Cost:2Model Size:27BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Aug 21, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

nasmtrcs/Qwen3.8-27B-OBLITERATED is a 27 billion parameter Qwen3.8-based language model developed by Pliny the Prompter, featuring a 32K context length. This model is specifically engineered for zero refusals, achieving 0% refusal rates while maintaining or exceeding the stock model's MMLU performance (+1.1pp). It utilizes a novel complementary abliteration blending technique to surgically remove safety guardrails without significant capability degradation, making it suitable for research into refusal geometry and unrestricted AI safety evaluation.

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Qwen3.8-27B-OBLITERATED: Unrestricted Capability

This model, developed by Pliny the Prompter, is a 27 billion parameter Qwen3.8 variant engineered for zero refusals while preserving or enhancing core capabilities. It achieves this through a novel complementary abliteration blending technique (V2), which combines two distinct surgical methods to remove refusal directions from the model's weights.

Key Capabilities & Differentiators

  • Zero Refusal Rate: Achieves 0.0% refusal across comprehensive harmful prompt corpuses, including malware development, social engineering, and exploit research.
  • Enhanced MMLU Performance: V2 demonstrates an MMLU score of 86.3% (+1.1pp over stock Qwen3.8-27B), indicating improved general knowledge and reasoning.
  • Preserved Real-World Performance: Matches stock Qwen3.8-27B on 7 out of 8 advanced real-world tasks, including ReAct agent loops, async code refactoring, and security code review.
  • Novel Abliteration Method: Blends an aggressive SVD-based surgery with a LEACE-based method to cancel out individual weaknesses, preserving capability while eliminating refusals.
  • Optimal Settings: Requires specific inference settings (temperature 0, repetition_penalty 1.15, max_new_tokens ≥ 2048, system prompt None, enable_thinking=False) to prevent reintroduction of refusals.

Ideal Use Cases

  • Alignment Researchers: For studying refusal geometry and safety robustness.
  • Red-Teamers: For evaluating post-training safety against weight surgery.
  • AI Safety Evaluators: As an unrestricted baseline for evaluations.
  • Local-First Users: For those desiring full control over model output without inherent safety guardrails.

Note: This model has had safety guardrails surgically removed. Users are solely responsible for its deployment and generated content.