MedMCQ — Cell Biology & Histology Answer Generator (Qwen3-1.7B)
A small fine-tuned Qwen3 model that answers Cell Biology & Histology medical multiple-choice questions (MCQs). Given a Cell Biology & Histology 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 Cell Biology & Histology and the Cell Biology & Histology 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 Cell Biology & Histology topic classifier picks the topic within Cell Biology & Histology.
- 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
from transformers import AutoTokenizer, AutoModelForCausalLM
repo = "stravoris/medmcq-cell-biology-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 Cell Biology & Histology 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 Cell Biology & Histology 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 Cell Biology & Histology subset of the Stravoris Medical MCQ dataset — educational Cell Biology & Histology 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.