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sweatSmile/
Gemma-2-2B-MedicalQA-Assistant

Name: Gemma-2-2B-MedicalQA-Assistant
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Gemma-2-2B Medical Q&A Assistant 🏥

This is a fine-tuned version of google/gemma-2-2b-it specialized for medical question answering. The model has been trained on real patient-doctor conversations to provide helpful, accurate medical information with appropriate safety disclaimers.

🎯 Model Details

Base Model: Gemma-2-2B-Instruct (2B parameters)
Fine-tuning Method: LoRA (Low-Rank Adaptation)
Quantization: 4-bit (nf4) for efficient training and inference
Dataset: ChatDoctor-HealthCareMagic (1,500 curated samples)
Domain: Medical Q&A, Healthcare Information
Use Case: Medical information assistant, health education, preliminary medical guidance

🔧 Training Configuration

LoRA Parameters

Rank (r): 8
Alpha: 16
Dropout: 0.1
Target Modules: All linear layers

Training Hyperparameters

Epochs: 3
Learning Rate: 2e-5
Batch Size: 4 per device
Max Sequence Length: 1024 tokens (for detailed medical responses)
Scheduler: Cosine with 10% warmup
Optimizer: AdamW with weight decay (0.01)
Precision: BF16
Gradient Clipping: 1.0

Training Infrastructure

Hardware: Single GPU (T4/V100/A100)
Training Time: ~30-45 minutes
Framework: TRL + Transformers + PEFT

📊 Dataset

The model was fine-tuned on a curated subset of the ChatDoctor-HealthCareMagic-100k dataset, which contains:

Real patient questions from HealthCareMagic platform
Professional doctor responses
Diverse medical topics (symptoms, treatments, medications, preventive care)
Privacy-preserved and ethically sourced data

Training Samples: 1,500 high-quality medical Q&A pairs

💻 Usage

Basic Inference

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "sweatSmile/Gemma-2-2B-MedicalQA-Assistant"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Format your medical query
prompt = """<start_of_turn>user
I've been experiencing persistent headaches for the past week. What could be causing this?<end_of_turn>
<start_of_turn>model
"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

4-bit Quantized Inference (Recommended)

python

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16
)

model = AutoModelForCausalLM.from_pretrained(
    "sweatSmile/Gemma-2-2B-MedicalQA-Assistant",
    quantization_config=quantization_config,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("sweatSmile/Gemma-2-2B-MedicalQA-Assistant")

🩺 Example Outputs

Query: "What are the common symptoms of diabetes?"

Response:

Common symptoms of Type 2 diabetes include frequent urination, increased thirst, unexplained weight loss, fatigue, blurred vision, slow-healing wounds, and tingling or numbness in hands or feet. If you're experiencing multiple symptoms, it's important to get your blood sugar levels checked. Early detection and management are crucial.

⚕️ Disclaimer: This information is for educational purposes only. Please consult a qualified healthcare professional for medical advice.

Query: "How can I manage high blood pressure naturally?"

Response:

Natural management of high blood pressure includes: regular exercise (30 minutes daily), reducing sodium intake, maintaining healthy weight, limiting alcohol, managing stress through meditation or yoga, eating potassium-rich foods (bananas, leafy greens), and ensuring adequate sleep. However, these should complement, not replace, prescribed medications.

⚕️ Disclaimer: This information is for educational purposes only. Please consult a qualified healthcare professional for medical advice.

⚠️ Important Limitations & Safety

Medical Disclaimers

🚨 NOT A REPLACEMENT FOR PROFESSIONAL MEDICAL ADVICE: This model provides general health information only
🚨 Always consult a qualified healthcare provider for diagnosis and treatment
🚨 Emergency situations: Call emergency services immediately for urgent medical conditions
🚨 Individual variation: Medical advice must be personalized to your specific situation

Model Limitations

Trained on 1,500 samples - may not cover all medical scenarios
Limited to English language medical terminology
May not reflect the most recent medical research (knowledge cutoff dependent on base model)
Cannot perform physical examinations or order diagnostic tests
May occasionally generate plausible-sounding but incorrect information (hallucinations)
Not validated against clinical benchmarks

Appropriate Use Cases ✅

General health education and information
Understanding common medical terms and conditions
Preliminary research before doctor's appointments
Health literacy improvement
Medical training assistance (with supervision)

Inappropriate Use Cases ❌

Self-diagnosis or self-treatment
Emergency medical situations
Replacing professional medical consultations
Making critical healthcare decisions
Prescribing medications or treatments
Mental health crisis intervention

🎓 Intended Use

This model is designed for:

Educational purposes: Teaching and learning about common health conditions
Health information access: Providing accessible medical knowledge
Research: Medical AI and NLP research
Prototyping: Building healthcare chatbot prototypes
Medical training: Supplementary tool for medical students (with instructor oversight)

📈 Performance Notes

Strengths: Common conditions, preventive care, general wellness advice, medical terminology
Best performance on: Questions similar to training distribution (patient-doctor Q&A format)
Quantization: 4-bit quantization maintains ~95% of full precision performance with significant memory savings

🔬 Technical Specifications

Specification	Value
Base Architecture	Gemma 2 (Google)
Model Size	2B parameters
Quantization	4-bit (nf4)
Context Window	8,192 tokens
Training Tokens	~1.5M medical tokens
LoRA Rank	8
LoRA Alpha	16
Trainable Parameters	~4.2M (0.2% of base)

📝 Citation

bibtex

@misc{gemma2-medical-qa-2025,
  author = {sweatSmile},
  title = {Gemma-2-2B Medical Q&A Assistant},
  year = {2025},
  publisher = {HuggingFace},
  journal = {HuggingFace Model Hub},
  howpublished = {\url{https://huggingface.co/sweatSmile/Gemma-2-2B-MedicalQA-Assistant}}
}

🙏 Acknowledgments

Base Model: Google's Gemma team for the excellent Gemma-2-2B-Instruct model
Dataset: ChatDoctor team and HealthCareMagic for the medical Q&A dataset
Framework: HuggingFace TRL, Transformers, and PEFT libraries

📜 License

This model inherits the Gemma License from the base model. The fine-tuned weights are released under the same terms.

Usage Restrictions: Please review Google's Gemma Terms of Use, particularly regarding healthcare applications.

⚖️ Ethical Considerations

Model outputs should always include medical disclaimers
Designed to encourage users to seek professional medical advice
Training data sourced ethically from publicly available patient-doctor interactions
No personally identifiable information (PII) in training data
Built with safety alignment from base Gemma-2 model

🔄 Version History

v1.0 (Current): Initial release with 1.5k samples, LoRA fine-tuning

Disclaimer: This model is a research prototype and educational tool. It is not intended for clinical use or as a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of your physician or other qualified health provider with any questions you may have regarding a medical condition.