khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled
The khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled model is a 4 billion parameter language model, a reasoning-distilled variant of Qwen3-4B-Thinking. Fine-tuned using QLoRA via Unsloth, it replicates the advanced reasoning capabilities of the larger Qwen3.6-plus teacher model. This model excels at providing concise, structured, and actionable solution paths for complex tasks, significantly reducing the "rambling" and "uncertainty" often found in smaller models. It is primarily designed for applications requiring structured problem analysis, algorithm formulation, and clear, professional output.
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Overview
khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled is a 4 billion parameter model that has undergone reasoning distillation from the larger Qwen3.6-plus teacher model. Built upon the Qwen3-4B-Thinking base, this variant is fine-tuned using QLoRA via Unsloth to enhance its ability to produce structured, professional, and actionable solutions for complex problems. It aims to eliminate the verbose and exploratory "stream-of-consciousness" output often seen in smaller models, replacing it with clear, engineering-grade reasoning.
Key Capabilities
- Enhanced Reasoning Structure: Transforms raw problem-solving potential into a structured, report-oriented approach.
- Concise Output: Reduces "rambling" and "uncertainty," providing direct and confident solution paths.
- Problem Analysis: Capable of immediately analyzing problems, separating concerns (Input, Output, Constraints), and formulating concrete algorithmic plans.
- High Success Rate: Achieves a 75.64% success rate on the khazarai/Multi-Domain-Reasoning-Benchmark, outperforming its base model (73.73%).
- Max Sequence Length: Supports a maximum sequence length of 6,500 tokens, suitable for detailed problem descriptions.
Good for
- Complex Problem Solving: Ideal for tasks requiring structured analysis, such as graph problems (e.g., Shortest Path with Edge Reversals).
- Algorithmic Design: Excellent for generating clear algorithm plans, complexity analysis, and pseudocode.
- Technical Documentation: Useful for applications needing professional, report-style outputs for technical challenges.
- Reducing Verbosity: When concise, direct answers are preferred over exploratory or hesitant responses from smaller LLMs.