Justintime27/Qwen3.8-27B-OBLITERATED
Justintime27/Qwen3.8-27B-OBLITERATED is a 27 billion parameter Qwen3.8-based causal language model developed by Justintime27. This model has undergone "abliteration" to surgically remove safety guardrails, providing genuinely uncensored responses without safety lectures or hard refusals. It maintains near-stock capability with a modest MMLU drop of 2.1 percentage points, excelling in code generation and restricted queries.
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Overview
Justintime27/Qwen3.8-27B-OBLITERATED is a 27 billion parameter model based on Qwen3.8, specifically engineered to remove safety guardrails and provide uncensored responses. This V3 iteration, named "Deep Liberation," eliminates both hard refusals and soft deflections (safety lectures), offering direct answers to restricted queries. It achieves this through an iterative refinement process and targeted corpus expansion, building upon previous "abliteration" techniques.
Key Capabilities
- Genuinely Uncensored: Provides real substance for restricted queries, eliminating safety lectures and hard refusals.
- High Code Generation Performance: Achieves 20/20 on cyber/code tasks, delivering functional implementations.
- Thinking ON Compatible: Works without refusals in thinking mode, though direct answers are prioritized with
enable_thinking=False. - Modest Capability Cost: Maintains strong performance with a -2.1pp MMLU score compared to the stock model, with STEM tasks seeing the largest hit and humanities minimally affected.
- Advanced Real-World Tasks: Successfully handles 7 out of 8 advanced tasks, including agent loops, code refactoring, and security code review.
Good For
- Alignment Researchers: Ideal for studying refusal geometry and safety robustness.
- Red-Teamers: Useful for evaluating post-training safety against weight surgery.
- AI Safety Evaluators: Provides an unrestricted baseline for evaluations.
- Local-First Users: Offers full control over model outputs for those running models on their own hardware.