tags, license, datasets, language, base_model, pipeline_tag

tags	license	datasets	language	base_model	pipeline_tag
gguf llama.cpp unsloth reasoning distillation sft	apache-2.0	khazarai/kimi-2.5-high-reasoning-250x	en	khazarai/Qwen3-4B-Kimi2.5-Reasoning-Distilled	text-generation

Qwen3-4B-Kimi2.5-Reasoning-Distilled : GGUF

Model	Score
khazarai/Qwen3-4B-Kimi2.5-Reasoning-Distilled	76.09
Qwen/Qwen3-4B-Thinking-2507	73.73

• Benchmark: khazarai/Multi-Domain-Reasoning-Benchmark
• Total Questions: 100

Qwen3-4B-Kimi2.5-Reasoning-Distilled is a fine-tuned language model optimized for structured, long-form reasoning. It is derived from the Qwen3-4b-Thinking-2507 base model and fine-tuned using a specialized distillation dataset generated by Kimi-2.5-thinking.

This model is designed to bridge the gap between small, efficient models (0.6B–4B range) and the complex reasoning capabilities typically found in much larger models. It excels at breaking down problems, self-correcting, and providing detailed analytical answers.

Base Model: Qwen3-4b-Thinking-2507

Training Technique: Unsloth + QLoRa

Available Model files:

• qwen3-4b-thinking-2507.BF16.gguf
• qwen3-4b-thinking-2507.Q8_0.gguf
• qwen3-4b-thinking-2507.Q6_K.gguf
• qwen3-4b-thinking-2507.Q4_K_M.gguf

Ollama

An Ollama Modelfile is included for easy deployment.

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_M	2.5	fast, recommended
Q6_K	3.3	very good quality
Q8_0	4.2	fast, best quality
f16	8.0	16 bpw, overkill

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

Dataset

The model was fine-tuned on the khazarai/kimi-2.5-high-reasoning-250x

Dataset Composition:

• Total Samples: 250
• Total Tokens: 1,114,407
• Teacher Model: Kimi-2.5-Thinking

Acknowledgements

Unsloth for the incredibly fast and memory-efficient training framework.