| ---
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| license: apache-2.0
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| base_model: Qwen/Qwen3-1.7B
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| tags:
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| - medical
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| - mcq
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| - question-answering
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| - microbiology
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| - qwen3
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| - medmcq
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| - stravoris
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| pipeline_tag: text-generation
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| language:
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| - en
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| ---
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| # MedMCQ — Microbiology Answer Generator (Qwen3-1.7B)
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| A small fine-tuned Qwen3 model that **answers Microbiology medical multiple-choice questions (MCQs)**. Given a Microbiology topic, an MCQ stem, and four lettered options, it returns the correct option and a brief clinical explanation.
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| This is a **per-subject answer generator** — the third hop in the [MedMCQ three-hop pipeline](#the-medmcq-pipeline). It is reached only after the [subject classifier](https://huggingface.co/stravoris/medmcq-subject-classifier-qwen3-0.6b) has routed the MCQ to Microbiology and the [Microbiology topic classifier](https://huggingface.co/stravoris/medmcq-microbiology-classifier-qwen3-0.6b) has tagged it with a topic.
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| ## The MedMCQ pipeline
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| 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:
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| 1. **Subject routing** — the [subject classifier](https://huggingface.co/stravoris/medmcq-subject-classifier-qwen3-0.6b) picks the medical subject.
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| 2. **Topic classification** — the [Microbiology topic classifier](https://huggingface.co/stravoris/medmcq-microbiology-classifier-qwen3-0.6b) picks the topic within Microbiology.
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| 3. **Answer generation** — *this model.* Given the topic and the MCQ, return the correct option and an explanation.
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| Each hop is a separate, narrow model. They are all published under the [MedMCQ Medical Models](https://huggingface.co/collections/stravoris/medmcq-medical-models) collection.
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| ## Quick start
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| ```python
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| from transformers import AutoTokenizer, AutoModelForCausalLM
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| repo = "stravoris/medmcq-microbiology-qwen3-1.7b"
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| tokenizer = AutoTokenizer.from_pretrained(repo)
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| model = AutoModelForCausalLM.from_pretrained(repo)
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| prompt = """Answer the following medical question. Provide the correct option and a brief explanation.
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| Topic: <a Microbiology topic>
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| Question: <the MCQ stem>
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| Options:
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| A) <option A>
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| B) <option B>
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| C) <option C>
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| D) <option D>"""
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| inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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| outputs = model.generate(**inputs, max_new_tokens=256, do_sample=False)
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| print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
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| ```
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|
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| ## Prompt format
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| The model expects prompts in this exact form:
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| ```
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| Answer the following medical question. Provide the correct option and a brief explanation.
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| Topic: <topic name>
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| Question: <question stem>
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| Options:
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| A) <option A>
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| B) <option B>
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| C) <option C>
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| D) <option D>
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| ```
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| The model completes the prompt with the correct option and a short clinical explanation, in the form:
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| ```
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| <letter>) <correct option text>
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| <brief explanation>
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| ```
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|
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| ## What this model is not
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| This is a **sample model for demonstration**. It is not a production-grade medical AI system:
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| - It has not been formally evaluated against board-level benchmarks.
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| - It should not be used to make clinical decisions or provide medical advice.
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| - Returned answers and explanations may contain factual errors or outdated information. A clinician should review every output before any educational use.
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| - It is narrow: it only answers Microbiology MCQs and is brittle outside that domain or on prompts that deviate from the format above.
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| - It was trained on a curated educational dataset and inherits any biases or gaps in that data.
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| The MedMCQ project exists to explore small-model pipeline architectures for medical reasoning, not to ship a medical product.
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| ## Training data
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| Trained on the Microbiology subset of the [Stravoris Medical MCQ dataset](https://huggingface.co/datasets/stravoris/medical-mcq-dataset) — educational Microbiology MCQs with topic labels, stems, lettered options, the correct option, and a worked explanation.
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| ## Base model
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| Fine-tuned from [`Qwen/Qwen3-1.7B`](https://huggingface.co/Qwen/Qwen3-1.7B). The base model's license and usage terms also apply.
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| ## License
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| Apache 2.0. See [LICENSE](https://www.apache.org/licenses/LICENSE-2.0).
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| ## Collection
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| Part of the [MedMCQ Medical Models](https://huggingface.co/collections/stravoris/medmcq-medical-models) collection — all 31 models that make up the MedMCQ three-hop pipeline.
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