MedMCQ is an experiment in answering medical multiple-choice questions with a collection of small, narrow models instead of one large generalist. A raw MCQ is routed by a subject classifier, then refined by a per-subject topic classifier, then answered by a per-subject generator that returns the correct option and a brief clinical explanation. The full pipeline is 31 fine-tuned Qwen3 models — 1 router, 15 topic classifiers, 15 answer generators — all published under the stravoris org and grouped into the MedMCQ Medical Models collection. This repository is the landing page and runnable usage guide for that pipeline; it ships only a README and no weights.
Dataset — stravoris/medical-mcq-dataset (the single educational MCQ dataset every model was fine-tuned on; subject/topic slices feed the corresponding classifier and generator).
Hardware
Every model in the pipeline runs on CPU; no GPU is required.
Model class
Base
Approx. RAM
Subject classifier, topic classifiers
Qwen3-0.6B
~2 GB
Answer generators
Qwen3-1.7B
~4 GB
A full three-hop call loads one 0.6B router, one 0.6B topic classifier, and one 1.7B generator — comfortably handled on a typical laptop. You can keep them resident in memory across calls, or load lazily based on the routed subject if RAM is tight.
What this pipeline is not
These are sample models for demonstration, released as a public reference for the three-hop architecture. They have not been formally benchmarked and must not be used to make clinical decisions or provide medical advice. Returned answers and explanations may contain factual errors. A clinician should review every output before any educational use.
License
Apache 2.0 across all 31 models and this guide. Base models (Qwen/Qwen3-0.6B and Qwen/Qwen3-1.7B from Alibaba's Qwen team) retain their original licenses too.
