Implementation

Setup

For this project, I set up a fine-tuning pipeline using a specialized medical dataset and a powerful language model:

• Dataset: I used the gamino/wiki_medical_terms dataset from Hugging Face. This dataset contains a wide range of medical terms and definitions, making it ideal for training models to understand and generate medical language.
• Model: The base model for fine-tuning was the Llama-3.1 8B, a state-of-the-art large language model.
• Fine-tuning API: Fine-tuning was performed using the Thinking Machine Tinker API, which streamlines the process of adapting large models to domain-specific datasets.
• Technique: The fine-tuning process used the LoRA (Low-Rank Adaptation) technique with a rank of 32, enabling efficient adaptation of the large model while reducing computational requirements.

This implementation allows the Llama-3.1 8B model to better understand and generate text related to medical terminology, improving its performance on healthcare and biomedical tasks.