DavidAU/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-NEO-IMATRIX-MAX-MTP
DavidAU/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-NEO-IMATRIX-MAX-MTP is a 9 billion parameter Qwen 3.5-based model developed by DavidAU and Nightmedia. This multi-stage fine-tuned and merged model significantly enhances general intelligence and instruction following, exceeding 7 critical benchmarks of the Qwen 3.5 27B model. It is fully uncensored, designed to follow instructions without refusal, and features a compacted, stronger reasoning block. The model is optimized for superior instruction following and general intelligence in a compact package, supporting a 256k context length and vision capabilities.
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Model Overview
DavidAU/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-NEO-IMATRIX-MAX-MTP is a 9 billion parameter model built upon the Qwen 3.5 architecture, developed through a multi-stage fine-tuning and merging process by DavidAU and Nightmedia. This model prioritizes enhanced general intelligence and superior instruction following, demonstrating performance that exceeds 7 critical benchmarks of the larger Qwen 3.5 27B model, and in some cases, even matching Qwen 3.6 27B.
Key Capabilities and Features
- Exceptional Performance: Achieves 0.649 ARC-C in bf16, outperforming base Qwen 3.5 9B, Qwen 3.5 27B, and Qwen 3.6 35B-A3B, and nearly matching Qwen 3.6 27B.
- Uncensored & Heretic: Designed to follow user instructions without refusal, offering maximum flexibility.
- Enhanced Reasoning: Features a compacted and significantly strengthened thinking/reasoning block.
- Optimized Quantization: Utilizes NEO IMATRIX GGUFs for improved accuracy (2-4% over normal GGUFs) and long context performance, with the output tensor modified to 16-bit full precision.
- Multi-Token Prediction (MTP): Includes MTP GGUFs for potential speed increases (up to 185 T/S on Q4_K_S) under specific settings (temp <= 1, rep pen = 1).
- Vision Capable: Supports vision inputs, requiring a separate 'mmproj' file.
- Extended Context: Offers a 256k context window.
Ideal Use Cases
- Applications requiring a highly intelligent and compliant model for complex instruction following.
- Scenarios where uncensored content generation is necessary.
- Tasks benefiting from strong reasoning capabilities in a smaller parameter count.
- Users seeking optimized performance with GGUF quantizations, including faster inference with MTP variants for suitable workloads.