AI for predictive differential diagnosis
Using efficiently tuned large language models
Overview
This collection of tutorials demonstrates how to build AI systems for predictive differential diagnosis using large language models. We explore both traditional transfer learning approaches and modern efficient fine-tuning techniques, all applied to medical datasets including one derived from the Uganda Clinical Guidelines.
Blog Posts & Tutorials
- Text Transfer Learning with ULMFit - Medical LLM V1 - Using fast.ai’s text transfer learning to build a language model
- Efficient Fine-tuning of Llama 3 with FSDP QDora - Medical LLM V3 - Efficient finetuning of Llama 3 70B with FSDP QDora on the Uganda Clinical Guidelines using consumer GPUs
What You’ll Learn
This comprehensive guide covers:
- Traditional Transfer Learning (ULMFit)
- Text classification for medical diagnosis
- Fast.ai’s ULMFit architecture
- Language model pre-training and fine-tuning
- Medical text preprocessing techniques
- Modern Efficient Fine-tuning (FSDP QDora)
- Quantization techniques and 4-bit precision benefits
- LoRA (Low Rank Adaptation) fundamentals
- QLoRA innovations for memory efficiency
- FSDP (Fully Sharded Data Parallel) for multi-GPU training
- DoRA improvements over standard LoRA
- Technical Implementation
- Setting up training environments
- Dataset preparation (Uganda Clinical Guidelines)
- Training configuration and execution
- Model inference and testing
- Deployment strategies
- Practical Results
- Training large models on consumer hardware
- Medical question-answering capabilities
- Performance comparisons between approaches
- Memory efficiency analysis
Key Technologies
- ULMFit - Universal Language Model Fine-tuning for text classification
- FSDP QDora - Combines sharding, quantization, and efficient adaptation
- Fast.ai - Practical deep learning framework
- Meta Llama 3 70B - State-of-the-art base model
- Uganda Clinical Guidelines - Real-world medical dataset
- Consumer Hardware - Accessible GPU requirements
- HuggingFace Integration - Easy model sharing and deployment
Hardware Requirements
For ULMFit Tutorial: - Minimum: Single GPU with 8GB+ memory - Recommended: RTX 3080/4080 or similar - Memory: ~16GB total GPU memory
For FSDP QDora Tutorial: - Minimum: 2x 24GB GPUs (RTX 3090 or similar) - Recommended: Multiple high-memory GPUs for faster training - Memory: ~48GB total GPU memory for 70B model training
Dataset: Uganda Clinical Guidelines
The Uganda Clinical Guidelines contain over 1000 pages of medical information including: - Clinical features and symptoms - Diagnostic procedures - Treatment protocols - Prevention strategies - Common health conditions in Uganda
This makes it an ideal dataset for training medical AI assistants that can provide evidence-based clinical guidance for predictive differential diagnosis.
Why These Approaches Matter
Traditional ML Challenges: - Limited context understanding - Poor generalization to unseen medical conditions - Requires extensive feature engineering - Difficulty handling complex medical language
Modern LLM Advantages: - Contextual Understanding - Grasp complex medical relationships - Few-shot Learning - Adapt to new conditions with minimal data - Natural Language Processing - Handle unstructured medical text - Scalable Training - Efficient techniques for large models
Comparison: ULMFit vs FSDP QDora
| Aspect | ULMFit | FSDP QDora |
|---|---|---|
| Model Size | ~100M parameters | 70B parameters |
| Training Time | Hours | Days |
| Hardware Needs | Single GPU | Multiple GPUs |
| Performance | Good for classification | Excellent for generation |
| Use Case | Specific diagnostic tasks | General medical AI |
Getting Started
Choose your learning path based on your goals and hardware:
Start with ULMFit if you: - Have limited GPU resources - Want to learn transfer learning fundamentals - Need a classification-focused approach - Prefer faster training cycles
Begin with FSDP QDora if you: - Have access to multiple high-end GPUs - Want state-of-the-art performance - Need generative capabilities - Are building comprehensive medical AI systems
Tutorials
Text Transfer Learning with ULMFit - Medical LLM V1 - Learn how to use fast.ai’s text transfer learning to build a medical language model from scratch
Efficient Finetuning of Llama 3 with FSDP QDora - Medical LLM V3 - See how to efficiently finetune Llama 3 70B with FSDP QDora on the Uganda Clinical Guidelines using consumer GPUs
Prerequisites
For ULMFit Tutorial: - Basic Python and machine learning knowledge - Familiarity with fast.ai library - Understanding of text classification concepts
For FSDP QDora Tutorial: - Familiarity with PyTorch and transformers - Basic understanding of distributed training concepts - Access to multiple GPUs (24GB+ recommended) - Python environment with CUDA support
Begin your journey into AI for predictive differential diagnosis: - Start with Text Transfer Learning with ULMFit - Medical LLM V1 for a foundational approach - Advance to Efficient Finetuning of Llama 3 with FSDP QDora - Medical LLM V3 for cutting-edge techniques