DavidAU/Qwen3.5-4B-Deckard-HERETIC-UNCENSORED-Thinking
DavidAU/Qwen3.5-4B-Deckard-HERETIC-UNCENSORED-Thinking is a 4.5 billion parameter Qwen 3.5 model fine-tuned by DavidAU using Unsloth and proprietary Deckard datasets. It significantly improves reasoning and output generation, outperforming the base Qwen3.5-4B-Instruct model on various benchmarks. This model is designed to be uncensored and 'heretic,' providing direct responses without refusal, and supports vision capabilities.
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Model Overview
DavidAU/Qwen3.5-4B-Deckard-HERETIC-UNCENSORED-Thinking is a 4.5 billion parameter Qwen 3.5 model, fine-tuned by DavidAU using Unsloth and five in-house Deckard datasets. This fine-tuning process was designed to be "mild" to preserve the base model's strong benchmarks while enhancing reasoning and output generation. The model demonstrates improved performance over the root Qwen3.5-4B-Instruct model across various benchmarks, including ARC, BoolQ, HellaSwag, OBQA, PIQA, and WinoGrande.
Key Differentiators
- Uncensored and Heretic: Trained post-"Heretic'ing," this model is designed to provide direct responses without refusals, offering uncensored output.
- Enhanced Reasoning: Fine-tuning specifically targeted improvements in reasoning capabilities.
- Superior Benchmarks: Outperforms the base Qwen3.5-4B-Instruct model on multiple reasoning and general knowledge benchmarks.
- Vision-Capable: Supports image inputs, with vision capabilities tested and confirmed working after new training.
Ideal Use Cases
- Applications requiring uncensored and direct responses.
- Tasks demanding strong reasoning and output generation from a 4.5B parameter model.
- Multimodal applications leveraging both text and image inputs.