Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled

Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled

This 27 billion parameter model, developed by Jackrong, is a specialized fine-tune of the Qwen3.5 architecture, focusing on advanced reasoning capabilities. It incorporates Chain-of-Thought (CoT) distillation from Claude-4.6 Opus interactions, enabling it to efficiently break down complex problems and formulate structured solutions. The model utilizes a streamlined reasoning paradigm, adopting an efficient "Let me analyze this request carefully: 1..2..3..." thinking pattern to reduce redundant cognitive loops while maintaining deep analytical capacity.

Key Capabilities & Differentiators

• Enhanced Reasoning: Excels in structured problem-solving by planning methodologies within <think> tags, delivering precise and nuanced solutions.
• Improved Agent Autonomy: Fixes issues with the official Qwen3.5 model, providing native support for the "developer" role and preserving thinking mode, leading to significantly improved autonomy and stability in coding agent environments like Claude Code and OpenCode.
• Efficient Fine-tuning: Developed using Unsloth for memory and compute optimization, with Supervised Fine-Tuning (SFT) focusing on high-density reasoning logic.
• Stable Tool-Calling: Demonstrates stable performance in tool-calling benchmarks, particularly the 27B version distilled with Claude Opus reasoning.

Ideal Use Cases

• Offline Analytical Tasks: Suited for scenarios requiring transparent internal logic and step-by-step problem decomposition.
• Coding and Math: Highly effective for programming tasks, mathematical problem-solving, and other logic-dependent prompting.
• Coding Agent Environments: Designed to be plug-and-play in modern local coding agents, offering an experience close to Claude Opus in smoothness and usability.