Qwen3-8B fine-tuned for stronger step-by-step medical reasoning, built to retain its native thinking mode and its existing tool-calling ability.
This is a full standalone model. No PEFT, no Unsloth, no extra libraries required — load it exactly like any other transformers causal LM.
Base model
Qwen/Qwen3-8B — 8.2B parameters, hybrid thinking / non-thinking model with native agentic tool-calling support.
Training data
MedReason (5,000 examples) — knowledge-graph grounded medical reasoning, for clinical reasoning quality.
xLAM Function-Calling (681 examples) — general tool-calling data, mixed in during training to help preserve agentic behavior.
Method
QLoRA fine-tuning (4-bit, rank 16, alpha 32) on a single T4 GPU, using Unsloth purely as a training-time tool. The resulting adapter was merged into the base model and released here as a full fp16 checkpoint — Unsloth is not needed to use this model, only transformers.
Result
Improved step-by-step reasoning inside Qwen3-8B's native <think>...</think> mode on medical/clinical questions, while keeping thinking mode, non-thinking mode, and tool-calling all functional.
Set enable_thinking=False for direct, non-reasoning answers.
License
Apache 2.0, matching the base model.
Disclaimer
Research and educational project. Not validated for clinical use — not a substitute for professional medical advice.
GGUF
An 8-bit quantized GGUF version (Qwen3-8B-MedReasonPath-Q8_0.gguf, ~8.7GB) is also available in this repo, for use with llama.cpp, Ollama, or LM Studio.
