DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU
DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU is a 27 billion parameter Qwen 3.8-based language model developed by DavidAU, fine-tuned for enhanced reasoning, reduced 'thinking tokens,' and uncensored output. It demonstrates significantly improved benchmark scores, with ARC-C 141 points above the base Qwen 3.8 27B, and maintains strong performance even in 4-bit quantization. This model is optimized for detailed, creative, and analytical text generation, particularly in scenarios requiring nuanced understanding and uncensored responses.
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Model Overview
DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU is a 27 billion parameter model built upon the Qwen 3.8 architecture, developed by DavidAU. This release represents the culmination of multiple fine-tuning stages, including "Heretic'ing" for uncensored output and subsequent "healing" training to restore and enhance core metrics. The model aims to deliver superior performance compared to other Qwen 27B and 35B models, focusing on reasoning, reduced verbosity in internal thought processes, and creative generation.
Key Capabilities
- Enhanced Reasoning: Demonstrates a significant uplift in reasoning benchmarks, with ARC-C scores 141 points higher than the base Qwen 3.8 27B. This is achieved through specialized training branches.
- Reduced "Thinking Tokens": Features a strong reduction in internal thought processes (1/2 to 1/10 of normal Qwen size) while maintaining or improving detail levels, with auto-variable thinking sizes based on prompt complexity.
- Uncensored Output: Undergoes a "Heretic'ing" process to remove safety alignments, balanced with low KLD (Kullback-Leibler Divergence) to minimize performance damage.
- High Performance at Low Quantization: Achieves 4-bit benchmark scores that are at or near 99% of its 8-bit performance, ensuring stable and accurate operation even with reduced precision.
- Creative and Analytical Generation: Specific branches are tuned for creative enhancements and detailed analytical output, as evidenced by example generations.
Good For
- Applications requiring uncensored and highly creative text generation.
- Tasks demanding strong reasoning and analytical capabilities with efficient output.
- Use cases where detailed narrative and character development are crucial, as shown in provided examples.
- Environments where resource-efficient deployment is necessary, given its strong 4-bit performance.