Medical LLM Project

A Language Model fine-tuned for medical applications, progressing from pretraining to instruction fine-tuning and Direct Preference Optimization (DPO).

Datasets

1. Pretraining: Medical Text Dataset (Kaggle)
2. Fine-tuning: PMC LLaMA Instructions (Hugging Face)

Project Stages

1. Pretraining

   • Custom GPT model on medical texts

2. Instruction Fine-tuning

   • Used LitGPT for LoRA fine-tuning on instruction dataset

3. Direct Preference Optimization (DPO)

   • Generated variants using fine-tuned model
   • Created preference pairs based on Levenshtein distance

Key Features

• Customized for medical domain
• Progression from general language model to instruction-following
• Experiment with preference optimization

Future Work

• Larger medical datasets
• Advanced DPO techniques
• Multi-task learning in medical domain
• Benchmark evaluation:
  • Compare against established medical NLP models
  • Evaluate on standardized medical QA datasets
  • Assess performance on clinical decision support tasks