Jackrong/Qwen3.5-2B-Claude-4.6-Opus-Reasoning-Distilled
Jackrong/Qwen3.5-2B-Claude-4.6-Opus-Reasoning-Distilled is a 2.3 billion parameter language model built on the Qwen3.5-2B architecture, fine-tuned for advanced reasoning. 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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Qwen3.5-2B-Claude-4.6-Opus-Reasoning-Distilled Overview
This model is a 2.3 billion parameter language model, built upon the Qwen3.5-2B base, and specifically fine-tuned for enhanced reasoning capabilities. It incorporates advanced Chain-of-Thought (CoT) distillation techniques, primarily sourced from Claude-4.6 Opus interactions, to foster structured, step-by-step problem-solving.
Key Enhancements & Capabilities
- Reasoning Distillation: Further enhanced with high-quality reasoning trajectories distilled from Qwen3.5-27B, improving performance in science, instruction-following, and mathematics.
- Structured Thinking: Employs a streamlined reasoning paradigm, adopting an efficient structured thinking pattern (e.g., "Let me analyze this request carefully: 1..2..3...") to reduce redundant cognitive loops.
- SFT with Unsloth: Utilizes Supervised Fine-Tuning (SFT) with Unsloth for memory and compute optimization, focusing on training the model to generate internal
<think>sequences before producing final answers. - Extended Context: Supports an extended context window of 16,384 tokens, allowing for complex multi-step reasoning within memory limits.
- Dataset Integration: Trained on curated datasets like nohurry/Opus-4.6-Reasoning-3000x-filtered and Jackrong/Qwen3.5-reasoning-700x to strengthen structured problem-solving and reasoning diversity.
Intended Use Cases
This model is best suited for offline analytical tasks, coding, mathematical problem-solving, and other logic-dependent prompting scenarios where transparent, step-by-step internal logic is beneficial. It is a test version for academic research and technical exploration.