wwhoadie/Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled
wwhoadie/Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled is a 9 billion parameter language model based on the Qwen3.5 architecture, fine-tuned for advanced reasoning tasks. It leverages Chain-of-Thought (CoT) distillation from Claude-4.6 Opus interactions, focusing on structured step-by-step problem-solving within a 32768 token context window. This model excels at breaking down complex problems, planning methodologies, and delivering precise solutions, making it ideal for analytical tasks, coding, and mathematics.
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Model Overview
wwhoadie/Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled is a 9 billion parameter model built upon the Qwen3.5-9B dense architecture, specifically fine-tuned for enhanced reasoning capabilities. It incorporates state-of-the-art Chain-of-Thought (CoT) distillation, primarily sourced from Claude-4.6 Opus interactions, to improve structured problem-solving.
Key Capabilities
- Structured Reasoning: The model is trained to break down complex problems and plan step-by-step methodologies using a strict
<think>tag format, ensuring transparent internal logic. - Efficient Thinking: It adopts a streamlined reasoning paradigm, reducing redundant cognitive loops while maintaining deep analytical capacity, inspired by Claude-4.6 Opus's structured thinking patterns.
- Extended Context: Fine-tuned to support a 16,384 token context window, allowing for complex multi-step reasoning traces within memory limits.
- Distilled Knowledge: Enhanced with additional reasoning data distilled from Qwen3.5-27B, including high-quality reasoning trajectories across science, instruction-following, and mathematics.
Training Details
The model underwent Supervised Fine-Tuning (SFT) using Unsloth for memory and compute optimization. A train_on_responses_only strategy was employed, masking instructions so loss was calculated purely over the generation of <think> sequences and subsequent solutions. Training data included high-quality, filtered reasoning distillation datasets such as nohurry/Opus-4.6-Reasoning-3000x-filtered and Jackrong/Qwen3.5-reasoning-700x.
Intended Use Cases
This model is best suited for:
- Offline Analytical Tasks: Where transparent, step-by-step reasoning is crucial.
- Coding and Mathematics: Excelling in logic-dependent prompting.
- Complex Problem Solving: Requiring a structured approach to derive nuanced solutions.
Limitations
- Hallucination Risk: As an autoregressive LLM, it may occasionally hallucinate external facts during its thinking sequence.