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Model: khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF
Source: Original Platform

README.md

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+---
+tags:
+- gguf
+- llama.cpp
+- unsloth
+license: apache-2.0
+datasets:
+- khazarai/qwen3.6-plus-high-reasoning-500x
+language:
+- en
+base_model:
+- khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled
+pipeline_tag: text-generation
+metrics:
+- accuracy
+---
+
+# Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled-GGUF : GGUF
+
+
+## Model: khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled
+
+![alt="General Benchmark Comparison Chart"](benchmark/evaluatedbyLLM.png)
+
+- **Success Rate**: 75.64%
+
+## Model: Qwen/Qwen3-4B-Thinking-2507
+
+![alt="General Benchmark Comparison Chart"](benchmark/BaseModel.png)
+
+- **Success Rate**: 73.73%
+
+- **Benchmark**: khazarai/Multi-Domain-Reasoning-Benchmark
+- **Total Questions**: 100
+
+  
+This is a reasoning-distilled variant of Qwen3-4B-Thinking, fine-tuned using LoRA via Unsloth to replicate the advanced reasoning capabilities of the larger Qwen3.6-plus teacher model.
+The distillation process focuses on reducing the "rambling" and "uncertainty" often found in smaller models during complex tasks, replacing them with concise, structured, and actionable solution paths.
+
+## Reasoning Comparison: Base vs. Distilled
+
+The primary improvement in this model is the qualitative leap in reasoning structure. Below is a summary of the differences observed when solving complex graph problems (e.g., Shortest Path with Edge Reversals):
+
+**Base Model (Qwen3-4B-Thinking)**:
+
+- Style: Stream-of-consciousness, exploratory, and verbose.
+- Behavior: The model often talks to itself ("Hmm, interesting", "Wait, no"), struggles to interpret problem constraints correctly on the first try, and enters loops of self-correction. It mimics a student trying to figure out the problem as they speak.
+- Output: Contains high noise-to-signal ratio; solution paths are often buried under paragraphs of hesitation.
+
+**Distilled Model (Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled)**:
+
+- Style: Structured, professional, and report-oriented.
+- Behavior: The model analyzes the problem immediately, separates concerns (Input, Output, Constraints), and formulates a concrete algorithm plan (e.g., State-Space Dijkstra). It proceeds with confidence, avoiding logical dead-ends.
+- Output: Provides a clean breakdown: Problem Analysis -> Intuition -> Algorithm -> Complexity Analysis -> Pseudocode.
+
+**Verdict**: The distilled model transforms the raw potential of the base model into an engineering-grade tool.
+
+
+## Model Specifications
+
+- **Base Model**: Qwen/Qwen3-4B-Thinking-2507
+- **Model Type**: Reasoning Distillation (QLoRA)
+- **Framework**: Unsloth
+- **Fine-tuning Method**: QLoRA (PEFT)
+- **Teacher Model**: Qwen3.6-plus
+- **Distillation Dataset**: khazarai/qwen3.6-plus-high-reasoning-500x
+  - Total Tokens: 1,739,249
+  - Max Sequence Length: 6,500 tokens
+
+## Provided Quants
+
+(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
+
+| Type | Size/GB | Notes |
+|:-----|--------:|:------|
+| Q4_K_1 | 2.3 |  |
+| Q6_K | 3.3 | very good quality |
+| Q8_0 | 4.2 | fast, best quality |
+| bf16 | 8.0 | 16 bpw, overkill |
+
+Here is a handy graph by ikawrakow comparing some lower-quality quant
+types (lower is better):
+
+![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)
+
+And here are Artefact2's thoughts on the matter:
+https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9