Mayank-22
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Mayank-AI

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-library_name: transformers
-tags: []
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-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
 ### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 - **Developed by:** Mayank Malviya
-- **Model type:** AI Model based on Supervised Learning
-- **Language(s) (NLP):**
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-## Uses
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-## How to Get Started with the Model
-Use the code below to get started with the model.
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+# Mayank-AI: Medical AI Assistant Model
+[![Hugging Face](https://img.shields.io/badge/🤗%20Hugging%20Face-Model-blue)](https://huggingface.co/Mayank-22/Mayank-AI)
+[![License](https://img.shields.io/badge/License-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
+[![Medical AI](https://img.shields.io/badge/Domain-Medical%20AI-red)](https://huggingface.co/Mayank-22/Mayank-AI)
+## 📋 Model Overview
+Mayank-AI is a specialized artificial intelligence model designed for Indian pharmaceutical and medical applications, trained on comprehensive Indian medicines datasets. This model leverages supervised learning techniques built on GPT-2 transformer architecture to provide accurate and contextually relevant information about Indian medicines, their compounds, uses, and related medical information.
+## 🔍 Model Details
 ### Model Description
 - **Developed by:** Mayank Malviya
+- **Model Type:** GPT-2 based Transformer for Indian Medical/Pharmaceutical Applications
+- **Language(s):** English (with Indian medical terminology and drug names)
+- **License:** Apache 2.0
+- **Domain:** Indian Pharmaceuticals & Medicine Information
+- **Primary Use:** Indian medicine information, drug compound analysis, symptom mapping, prescription guidance
+### Key Features
+- ✅ Indian medicines database knowledge
+- ✅ Drug compound information and analysis
+- ✅ Symptom-to-medicine mapping
+- ✅ Prescription guidance and recommendations
+- ✅ Disease diagnosis assistance
+- ✅ Indian pharmaceutical market insights
+- ✅ Medicine availability and alternatives
+## 🚀 Quick Start
+### Installation
+```bash
+pip install transformers torch
+```
+### Basic Usage
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+# Load model and tokenizer
+model_name = "Mayank-22/Mayank-AI"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+# Example queries for Indian medicines
+query1 = "What is the composition of Crocin tablet?"
+query2 = "Which medicine is used for fever and headache?"
+query3 = "What are the side effects of Paracetamol?"
+query4 = "Medicines available for diabetes in India"
+# Process query
+inputs = tokenizer.encode(query1, return_tensors="pt")
+# Generate response
+with torch.no_grad():
+    outputs = model.generate(
+        inputs,
+        max_length=512,
+        num_return_sequences=1,
+        temperature=0.7,
+        do_sample=True,
+        pad_token_id=tokenizer.eos_token_id
+    )
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+### Advanced Usage
+```python
+# For more controlled generation about Indian medicines
+def generate_medicine_response(question, max_length=256):
+    prompt = f"Indian Medicine Query: {question}\nResponse:"
+    inputs = tokenizer.encode(prompt, return_tensors="pt")
+    outputs = model.generate(
+        inputs,
+        max_length=max_length,
+        num_return_sequences=1,
+        temperature=0.6,
+        do_sample=True,
+        top_p=0.9,
+        repetition_penalty=1.1
+    )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response.split("Response:")[-1].strip()
+# Example usage
+question = "What are the uses of Azithromycin tablets available in India?"
+answer = generate_medicine_response(question)
+print(answer)
+```
+## 📊 Performance & Capabilities
+### Supported Medical Areas
+- **Indian Pharmaceuticals:** Comprehensive database of medicines available in India
+- **Drug Compounds:** Active ingredients, chemical compositions, formulations
+- **Symptom Analysis:** Symptom-to-medicine mapping and recommendations
+- **Disease Information:** Common diseases and their standard treatments in India
+- **Prescription Guidance:** Dosage, administration, and usage instructions
+- **Drug Interactions:** Side effects and contraindications
+- **Medicine Alternatives:** Generic and branded medicine alternatives
+### Performance Metrics
+- **Training Data:** Indian medicines dataset with comprehensive drug information
+- **Specialization:** Focused on Indian pharmaceutical market and medicine availability
+- **Coverage:** Extensive database of Indian medicines, their compounds, and uses
+- **Accuracy:** High precision in Indian medicine information and drug compound details
+## ⚠️ Important Medical Disclaimer
+> **CRITICAL NOTICE:** This model is for informational and educational purposes only. It should NOT be used as a substitute for professional medical advice, diagnosis, or treatment. Always consult with qualified healthcare providers for medical concerns.
+### Limitations & Risks
+- **Not a replacement for medical professionals**
+- **May contain inaccuracies or outdated information**
+- **Should not be used for emergency medical situations**
+- **Requires human oversight for clinical applications**
+- **May have biases present in training data**
+## 🎯 Intended Use Cases
+### ✅ Appropriate Uses
+- Indian pharmaceutical research and education
+- Medicine information lookup and comparison
+- Drug compound analysis and research
+- Symptom-to-medicine mapping assistance
+- Prescription guidance and dosage information
+- Medicine availability and alternatives research
+- Healthcare app development and integration
+### ❌ Inappropriate Uses
+- Direct patient diagnosis
+- Emergency medical decisions
+- Prescription or treatment recommendations without medical supervision
+- Replacement for clinical judgment
+- Use without proper medical context
+## 🔧 Technical Specifications
+### Model Architecture
+- **Base Architecture:** GPT-2 Transformer model
+- **Fine-tuning:** Supervised learning on Indian medicines dataset
+- **Context Length:** Standard GPT-2 context window
+- **Training Approach:** Domain-specific fine-tuning on pharmaceutical data
+### Training Details
+- **Training Data:** Indian medicines dataset including:
+  - Medicine names and brand information
+  - Drug compounds and chemical compositions
+  - Symptom-medicine mappings
+  - Prescription guidelines and dosages
+  - Disease-treatment associations
+  - Side effects and contraindications
+- **Training Regime:** Supervised fine-tuning on GPT-2 with pharmaceutical domain adaptation
+- **Optimization:** Adam optimizer with learning rate scheduling
+- **Data Focus:** Indian pharmaceutical market and medicine availability
+## 📚 Datasets & Training
+### Training Data Sources
+- Comprehensive Indian medicines database
+- Drug compound and chemical composition data
+- Symptom-medicine relationship mappings
+- Prescription guidelines and dosage information
+- Disease-treatment associations
+- Medicine availability and market data
+### Data Preprocessing
+- Medicine name normalization and standardization
+- Drug compound data structure optimization
+- Symptom-medicine relationship mapping
+- Quality filtering and validation of pharmaceutical data
+- Indian market-specific data curation
+## 🧪 Evaluation & Validation
+### Evaluation Metrics
+- **Medicine Information Accuracy:** Correctness of drug compound and usage information
+- **Symptom Mapping Precision:** Accuracy of symptom-to-medicine recommendations
+- **Indian Market Relevance:** Appropriateness for Indian pharmaceutical context
+- **Safety Assessment:** Risk evaluation for medicine information provision
+### Benchmark Performance
+- **Indian Medicine Database:** Comprehensive coverage of medicines available in India
+- **Drug Compound Accuracy:** High precision in chemical composition information
+- **Symptom-Medicine Mapping:** Effective symptom-to-treatment recommendations
+## 🔄 Updates & Maintenance
+This model is maintained and updated with:
+- Latest Indian medicine information
+- New drug approvals and market entries
+- Updated compound and formulation data
+- Enhanced symptom-medicine mappings
+## 📖 Citation
+If you use this model in your research, please cite:
+```bibtex
+@misc{mayank2024indianmedicines,
+  title={Mayank-AI: Indian Medicines Information Model},
+  author={Malviya, Mayank},
+  year={2024},
+  url={https://huggingface.co/Mayank-22/Mayank-AI},
+  note={GPT-2 based model for Indian pharmaceutical information}
+}
+```
+## 🤝 Contributing
+Contributions to improve the model are welcome! Please:
+- Report issues with medicine information accuracy
+- Suggest new Indian medicines to include
+- Share feedback on drug compound data
+- Contribute to symptom-medicine mapping improvements
+## 📞 Contact & Support
+- **Model Author:** Mayank Malviya
+- **Repository:** [Mayank-22/Mayank-AI](https://huggingface.co/Mayank-22/Mayank-AI)
+- **Issues:** Please report issues through the Hugging Face repository
+## 📄 License
+This model is released under the Apache 2.0 License. See the LICENSE file for details.
+## 🙏 Acknowledgments
+Special thanks to the Indian pharmaceutical community, healthcare professionals, and medical researchers who contributed to the development and validation of this specialized model for Indian medicines.
+---
+**Remember:** This AI model is a tool to assist, not replace, medical professionals. Always prioritize patient safety and seek professional medical advice for healthcare decisions.