Fine-Tuning Llama 3.2 3B on Medical QA: Week 1 Setup and Baseline Inference
A developer is undertaking a project to fine-tune Meta's Llama 3.2 3B Instruct model for medical question answering. The goal is to address the unreliability of general-purpose LLMs in healthcare by training the model on the MedQuAD dataset, which is sourced from USMLE board exam questions. The project will document the entire fine-tuning pipeline, from data preparation and LoRA training to evaluation and deployment via a public API, aiming to create a reproducible and domain-agnostic process. AI
IMPACT Demonstrates a practical approach to specializing LLMs for high-stakes domains like healthcare, improving reliability beyond general-purpose models.