Update README.md

README.md

@@ -1,3 +1,234 @@
----
-license: apache-2.0
----
+---
+license: gemma
+base_model: google/gemma-2-2b-it
+tags:
+- gemma
+- gemma2
+- medical
+- healthcare
+- medical-qa
+- fine-tuned
+- lora
+- instruct
+- 4bit
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+datasets:
+- lavita/ChatDoctor-HealthCareMagic-100k
+---
+
+# Gemma-2-2B Medical Q&A Assistant 🏥
+
+This is a fine-tuned version of [google/gemma-2-2b-it](https://huggingface.co/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](https://huggingface.co/datasets/lavita/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](https://ai.google.dev/gemma/terms) 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.