A small fine-tuned Qwen3 model that answers Radiology medical multiple-choice questions (MCQs). Given a Radiology topic, an MCQ stem, and four lettered options, it returns the correct option and a brief clinical explanation.

This is a per-subject answer generator — the third hop in the MedMCQ three-hop pipeline. It is reached only after the subject classifier has routed the MCQ to Radiology and the Radiology topic classifier has tagged it with a topic.

The MedMCQ pipeline

The MedMCQ project explores small, specialized models for medical MCQs. Instead of using one large model for everything, it splits the task across three hops:

Subject routing — the subject classifier picks the medical subject.
Topic classification — the Radiology topic classifier picks the topic within Radiology.
Answer generation — this model. Given the topic and the MCQ, return the correct option and an explanation.

Each hop is a separate, narrow model. They are all published under the MedMCQ Medical Models collection.

Quick start

python

from transformers import AutoTokenizer, AutoModelForCausalLM

repo = "stravoris/medmcq-radiology-qwen3-1.7b"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo)

prompt = """Answer the following medical question. Provide the correct option and a brief explanation.

Topic: <a Radiology topic>

Question: <the MCQ stem>

Options:
A) <option A>
B) <option B>
C) <option C>
D) <option D>"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, do_sample=False)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Prompt format

The model expects prompts in this exact form:

Answer the following medical question. Provide the correct option and a brief explanation.

Topic: <topic name>

Question: <question stem>

Options:
A) <option A>
B) <option B>
C) <option C>
D) <option D>

The model completes the prompt with the correct option and a short clinical explanation, in the form:

<letter>) <correct option text>

<brief explanation>

What this model is not

This is a sample model for demonstration. It is not a production-grade medical AI system:

It has not been formally evaluated against board-level benchmarks.
It should not be used to make clinical decisions or provide medical advice.
Returned answers and explanations may contain factual errors or outdated information. A clinician should review every output before any educational use.
It is narrow: it only answers Radiology MCQs and is brittle outside that domain or on prompts that deviate from the format above.
It was trained on a curated educational dataset and inherits any biases or gaps in that data.

The MedMCQ project exists to explore small-model pipeline architectures for medical reasoning, not to ship a medical product.

Training data

Trained on the Radiology subset of the Stravoris Medical MCQ dataset — educational Radiology MCQs with topic labels, stems, lettered options, the correct option, and a worked explanation.

Base model

Fine-tuned from Qwen/Qwen3-1.7B. The base model's license and usage terms also apply.

License

Apache 2.0. See LICENSE.

Collection

Part of the MedMCQ Medical Models collection — all 31 models that make up the MedMCQ three-hop pipeline.