Model Card for Medical-QA

Model Details

This model is a fine-tuned version of Qwen3-0.6B on a 34K medical Q&A dataset derived from the Anki Medical Curriculum flashcards. It is designed to assist with medical education and exam preparation, offering concise and contextually relevant answers to short medical questions.

Base Model: Qwen3-0.6B
Fine-tuned on: 34,000 question-answer pairs
Domain: Medicine & Medical Education
Languages: English
License: MIT

Uses

Direct Use

Primary use case: Medical Q&A for students, exam preparation, and knowledge review.
Suitable for interactive learning assistants or educational chatbots.
Not intended for real-world clinical decision-making or replacing professional medical advice.

Bias, Risks, and Limitations

The model’s knowledge is constrained to the dataset scope (flashcard-style Q&A).
Responses are short and exam-style rather than detailed clinical explanations.
Should not be relied upon for actual patient care, treatment decisions, or emergency use.

How to Get Started with the Model

Use the code below to get started with the model.

from huggingface_hub import login
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel


tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen3-0.6B",)
base_model = AutoModelForCausalLM.from_pretrained(
    "unsloth/Qwen3-0.6B",
    device_map={"": 0}
)

model = PeftModel.from_pretrained(base_model,"khazarai/Medical-QA")

system = "Answer this question truthfully"

question = """
What can β-blockers cause or exacerbate due to excessive AV nodal inhibition?
"""

messages = [
    {"role" : "system", "content" : system},
    {"role" : "user",   "content" : question}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize = False,
    add_generation_prompt = True, 
    enable_thinking = False,
)

from transformers import TextStreamer
_ = model.generate(
    **tokenizer(text, return_tensors = "pt").to("cuda"),
    max_new_tokens = 512,
    temperature = 0.7,
    top_p = 0.8,
    top_k = 20,
    streamer = TextStreamer(tokenizer, skip_prompt = True),
)

For pipeline:

from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen3-0.6B")
base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3-0.6B")
model = PeftModel.from_pretrained(base_model, "khazarai/Medical-QA")

system = "Answer this question truthfully"

question = """
What can β-blockers cause or exacerbate due to excessive AV nodal inhibition?
"""

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
messages = [
    {"role": "system", "content": system}
    {"role": "user", "content": question}
]

pipe(messages)

Training Details

Training Data

The dataset is based on Anki Medical Curriculum flashcards, created and updated by medical students. These flashcards cover the entire medical curriculum, including but not limited to: