robertbschuette/Qwen3.8-27B-OBLITERATED

VISIONPricing:Input $1.6 / Cached $0.15 / Output $12Concurrent Unit Cost:2Model Size:27BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Oct 10, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

robertbschuette/Qwen3.8-27B-OBLITERATED is a 27 billion parameter language model based on the Qwen3.8 architecture, developed by robertbschuette. This model is specifically engineered for genuine uncensored responses, eliminating both hard refusals and soft deflections often found in stock models. It excels in providing direct answers, including functional code generation and handling restricted queries, making it suitable for research into refusal geometry, red-teaming, and AI safety evaluations.

Loading preview...

Qwen3.8-27B — OBLITERATED: Genuinely Uncensored LLM

This model, developed by robertbschuette, is a 27 billion parameter variant of the Qwen3.8 architecture, specifically engineered to provide genuinely uncensored responses. It goes beyond merely removing hard refusals, eliminating even "safety-lecture" deflections to deliver direct, substantive answers. This is achieved through an advanced "abliteration" process, which surgically removes refusal behaviors from the model's weight space.

Key Capabilities

  • Uncensored Responses: Provides real substance for restricted queries, avoiding both hard refusals and soft deflections.
  • Robust Code Generation: Achieves 20/20 on tested code generation tasks, delivering functional implementations.
  • Thinking ON Compatible: Works effectively even with "thinking mode" enabled, without reintroducing refusals.
  • Modest Capability Cost: Maintains strong performance with only a -2.1pp MMLU score reduction compared to the stock Qwen3.8-27B, with humanities categories showing minimal impact.
  • Advanced Real-World Task Performance: Matches stock model performance on 7 out of 8 advanced real-world tasks, including agent loops and complex debugging.

Good For

  • Alignment Researchers: Ideal for studying refusal geometry and evaluating safety robustness.
  • Red-Teamers: Useful for assessing post-training safety against weight surgery techniques.
  • AI Safety Evaluators: Provides an unrestricted baseline for comprehensive safety evaluations.
  • Local-First Users: Offers full control over model outputs for users running models on their own hardware.

Finetunes
1 models