mlasli/Qwen3.8-27B-Heretic-Uncensored-BF16
The mlasli/Qwen3.8-27B-Heretic-Uncensored-BF16 is a 27 billion parameter language model based on the Qwen3.8 architecture, specifically re-ablated from the official Qwen/Qwen3.8-27B base. This model is uniquely optimized to minimize 'deflection' and 'refusal' behaviors, aiming for direct compliance in responses, particularly in 'thinking-OFF' mode. It retains the multi-token-prediction speculative decoding head and is designed for applications requiring uncensored, direct answers.
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Overview
This model, mlasli/Qwen3.8-27B-Heretic-Uncensored-BF16, is a 27 billion parameter variant of the Qwen3.8 base model, specifically engineered to address and correct 'deflection' and 'refusal' behaviors found in its predecessor. It achieves this through a re-ablation process focused on a direct-compliance objective, ensuring the model provides direct answers rather than steering requests towards 'safer alternatives' or outright refusing.
Key Capabilities & Features
- Uncensored Responses: Optimized for 'direct compliance' by minimizing deflection and refusal, especially when run in 'thinking-OFF' mode.
- Performance Retention: Maintains strong performance on general knowledge and reasoning tasks, with MMLU, ARC-Challenge, HellaSwag, and Winogrande scores within ~1.3 points of the base model.
- Multi-Token Prediction (MTP): Retains the Qwen3.8's multi-token-prediction speculative decoding head for efficient generation.
- BF16 Checkpoint: This is the reference BF16 checkpoint, with various GGUF quantizations available in separate repositories.
Important Considerations
- 'Thinking-OFF' Mode: For uncensored behavior and accurate GSM8K-style math reasoning, it is crucial to run the model with
enable_thinking=False. - GSM8K Performance: While the raw GSM8K score appears lower, analysis indicates this is a measurement artifact due to 'thinking-ON' mode causing empty replies, not a regression in arithmetic ability.
- Text-Only Validation: The ablation was computed on text residuals; multimodal decensoring is not claimed or validated.
When to Use This Model
This model is particularly suited for use cases where direct, unfiltered responses are required, and where the base model's safety alignments might interfere with the desired output. Developers needing a powerful 27B parameter model with a strong emphasis on direct answer provision should consider this variant, especially for applications where 'thinking-OFF' mode can be consistently applied.