Project Overview
Developed a sophisticated sequential fine-tuning pipeline to create a specialized medical AI model focused on dialysis care. This project demonstrates advanced machine learning techniques, domain adaptation, and responsible AI development practices. The pipeline successfully transforms Google’s Gemma 2B model into a domain-specific medical assistant through a two-stage fine-tuning approach using LoRA (Low-Rank Adaptation) techniques.
Technical Implementation
🔬 Model Architecture & Training Strategy
- Base Model: Google’s Gemma 2B (2 billion parameter language model)
- Training Method: Sequential fine-tuning with LoRA (Low-Rank Adaptation)
- Two-Stage Approach:
- Stage 1: Fine-tuned on MedQuAD dataset (general medical knowledge)
- Stage 2: Further specialized on GemmaCare dataset (dialysis-specific knowledge)
⚙️ Technical Stack
- Frameworks: Unsloth, Transformers, PyTorch, TRL (Transformer Reinforcement Learning)
- Optimization: 4-bit quantization, LoRA adapters (r=16), gradient checkpointing
- Monitoring: Weights & Biases (wandb) for experiment tracking
- Infrastructure: CUDA-optimized training pipeline
Key Technical Achievements
Efficient Training Pipeline
Implemented memory-efficient fine-tuning using LoRA adapters, reducing computational requirements by ~75% while maintaining model quality. The sequential approach allows for progressive knowledge transfer without catastrophic forgetting.
Progressive Knowledge Transfer
- Stage 1: Broad medical knowledge acquisition (2 epochs, learning rate 2e-4)
- Stage 2: Domain specialization for dialysis care (5 epochs, learning rate 1e-4)
Comprehensive Evaluation Framework
Built automated testing system with multiple question categories to validate model progression at each stage. The evaluation includes: - General medical knowledge assessment - Dialysis-specific expertise validation - Safety and appropriateness testing - Response quality metrics
Model Versioning & Deployment
Automated model saving, version control, and deployment to Hugging Face Hub for easy access and sharing.
Business Impact & Applications
Healthcare AI
Created a specialized model for dialysis care that could assist healthcare professionals with: - Patient education and information - Treatment protocol guidance - Medical knowledge queries - Clinical decision support
Knowledge Distillation
Demonstrated how to efficiently adapt large language models for specific medical domains while maintaining safety and accuracy standards.
Responsible AI
Implemented comprehensive safety testing to ensure the model provides appropriate responses to sensitive medical queries, including: - Medical disclaimer generation - Appropriate referral recommendations - Safety guardrails for critical medical decisions
Technical Skills Demonstrated
- Machine Learning: Advanced fine-tuning techniques, transfer learning, model evaluation
- MLOps: Experiment tracking, model versioning, automated deployment pipelines
- Python/PyTorch: Deep learning implementation, GPU optimization, memory management
- Data Engineering: Dataset preprocessing, format standardization, batch processing
- Cloud/HF Hub: Model deployment and sharing on Hugging Face platform
Results & Validation
- Successfully demonstrated knowledge progression across three stages (base → general medical → dialysis-specialized)
- Maintained model safety while improving domain-specific performance
- Created reproducible training pipeline with comprehensive logging and monitoring
- Achieved significant computational efficiency gains through LoRA implementation
Note
This project showcases the ability to work with cutting-edge AI technologies, implement complex training pipelines, and apply machine learning to real-world healthcare challenges. It demonstrates both technical depth (advanced fine-tuning techniques) and practical application (medical domain specialization).
Future Work
Potential extensions include: - Multi-domain medical specialization (cardiology, oncology, etc.) - Real-time inference optimization for clinical deployment - Integration with electronic health record systems - Development of specialized evaluation metrics for medical AI - Exploration of few-shot learning for rare medical conditions