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):
