Jongsim/Qwen3.5-27B-heretic-v2-Opus-4.6-Distilled
VISIONConcurrency Cost:2Model Size:27BQuant:FP8Ctx Length:32kTool Calling:SupportedPublished:Apr 10, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
Jongsim/Qwen3.5-27B-heretic-v2-Opus-4.6-Distilled is a 27 billion parameter Qwen3.5-based causal language model fine-tuned by Jongsim. It was distilled from Claude Opus 4.6 reasoning traces, specifically optimized for complex multi-step reasoning, logic, and coding tasks. This model leverages a high-quality dataset of 12,822 Claude Opus 4.6 generated examples featuring structured chain-of-thought reasoning.
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Model Overview
Jongsim/Qwen3.5-27B-heretic-v2-Opus-4.6-Distilled is a 27 billion parameter language model built upon the Qwen3.5 architecture. It is a fine-tuned version of llmfan46/Qwen3.5-27B-ultra-uncensored-heretic-v2, specifically enhanced through distillation from Claude Opus 4.6 reasoning traces.
Key Capabilities & Features
- Advanced Reasoning: Fine-tuned on 12,822 high-quality English reasoning examples from Claude Opus 4.6, including complex multi-step reasoning with chain-of-thought structures.
- Diverse Domains: Excels in tasks across various domains such as mathematics, logic, coding, analysis, and creative writing, benefiting from the diverse nature of its training data.
- Efficient Training: Utilizes LoRA fine-tuning with Unsloth, resulting in strong convergence and consistent loss reduction over 3 epochs, with a final overall training loss of 0.1954.
- Uncensored Base: Inherits the uncensored characteristics from its base model,
llmfan46/Qwen3.5-27B-ultra-uncensored-heretic-v2.
Ideal Use Cases
- Complex Problem Solving: Suited for applications requiring detailed, step-by-step logical deduction and problem-solving.
- Code Generation & Analysis: Benefits from reasoning traces that include coding examples, making it suitable for programming-related tasks.
- Content Generation with Structure: Can generate creative and analytical content that follows coherent reasoning chains, thanks to its training on structured reasoning data.