Jackrong/Qwen3.5-27B-GLM5.1-Distill-v1
Jackrong/Qwen3.5-27B-GLM5.1-Distill-v1 is a 27 billion parameter language model developed by Jackrong, distilled from Qwen3.5-27B and fine-tuned on high-quality reasoning data derived from GLM-5.1. This model is specifically designed to enhance structured reasoning ability, improve instruction-following consistency, and activate latent knowledge through a refined reasoning structure. It excels in tasks requiring multi-step logic, problem decomposition, and organized output, making it suitable for analytical tasks, coding, and complex problem-solving.
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Overview
Jackrong/Qwen3.5-27B-GLM5.1-Distill-v1 is a 27 billion parameter model, a distilled variant of Qwen3.5-27B. It was fine-tuned using Unsloth and LoRA on high-quality reasoning data derived from a GLM-5.1 teacher model, specifically Jackrong/GLM-5.1-Reasoning-1M-Cleaned. The core philosophy behind this distillation is to transfer a stronger reasoning structure and problem-solving style, rather than mere output imitation.
Key Capabilities
- Enhanced Structured Reasoning: Learns a task-first, structure-driven reasoning pattern from GLM-5.1, focusing on problem decomposition and step-by-step logic.
- Improved Instruction Following: Demonstrates better consistency in adhering to instructions and producing organized responses.
- Stable Multi-step Reasoning: Aims for incremental but meaningful improvements in the stability and clarity of complex, multi-step reasoning tasks.
- High-Quality Data Advantage: Benefits from a training dataset featuring strong chain-of-thought structures, wide domain coverage, and consistent instruction-reasoning-answer alignment.
Good For
- Offline Analytical Tasks: Ideal for scenarios requiring transparent, step-by-step internal logic.
- Coding and Math: Suited for tasks demanding heavy logic and structured problem-solving.
- Complex Problem Solving: Provides more structured and readable outputs for intricate challenges.
Limitations
- Hallucination Risk: As an autoregressive LLM, it may occasionally hallucinate external facts within its reasoning sequences.
- Experimental Nature: This is an independent project, and the reasoning chain (CoT) may sometimes exhibit instability or logic loops due to resource constraints.