Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1
Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1 is a 9 billion parameter Qwen3.5-based model, distilled from GLM-5.1 reasoning data using Supervised Fine-Tuning (SFT) with Unsloth. This model is specifically optimized to enhance structured reasoning ability, improve instruction-following consistency, and activate latent knowledge through better reasoning structures. It excels in tasks requiring transparent internal logic, such as offline analytical tasks, coding, and mathematics, by learning a task-first and structure-driven reasoning pattern.
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Model Overview
Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1 is a 9 billion parameter model built upon the Qwen3.5-9B base, fine-tuned using Supervised Fine-Tuning (SFT) and distillation techniques with Unsloth. Its core innovation lies in distilling high-quality reasoning data from a GLM-5.1 teacher model, specifically the Jackrong/GLM-5.1-Reasoning-1M-Cleaned dataset. This process aims to transfer a robust reasoning structure and problem-solving style, rather than merely imitating outputs.
Key Capabilities & Improvements
- Enhanced Structured Reasoning: The model learns a task-first, structure-driven reasoning scaffold, emphasizing task decomposition, constraint extraction, and step-by-step logic, distinct from previous Claude-style scaffolds.
- Improved Instruction Following: Demonstrates better consistency in adhering to instructions, leading to more stable and predictable outputs.
- Clearer Output Organization: Produces more structured and readable responses, particularly beneficial for complex problems.
- Domain-Aware Reasoning: Benefits from a training dataset with wide domain coverage and strong problem decomposition patterns, supporting multilingual reasoning.
Intended Use Cases
- Complex Problem Solving: Designed for tasks requiring multi-step reasoning, such as coding, mathematics, and STEM problems.
- Analytical Tasks: Ideal for offline analytical tasks where transparent internal logic and structured outputs are crucial.
- Learning & Research: Serves as an experimental model for academic research and technical exploration, focusing on the transfer of reasoning procedures through distillation.
While gains are incremental for a 9B model, the primary benefit is improved stability, structure, and consistency in complex reasoning tasks, rather than a dramatic jump in raw capability. The distillation philosophy focuses on enabling the student model to better utilize and activate its existing knowledge through structured reasoning.