Fined-Tuning-LLM-Medical-Chatbox

Model Save Link: https://huggingface.co/LxyMia1129/LLM_Medical_ChatBox/tree/main

This project leverages LLaMA-2, a powerful and versatile language model, as the foundation to create a fine-tuned Large Language Model (LLM) for a medical AI chatbot using a dataset of 5k+ medical questions and answers involves several steps, each crucial for ensuring the model's accuracy, relevance, and safety.

Here’s a step-by-step guide to fine-tuning this model for medical purposes:

1. Preparing the Dataset

• Begin with a robust dataset of 5k+ medical questions and answers. Ensure the data is clean, high-quality, and representative of the variety of medical queries the chatbot is expected to handle.

• Structure the dataset appropriately for training. Each entry paired a medical question with its corresponding answer.

2. Setting Up the Environment

• Select Vast.ai for training. Training LLMs is resource-intensive, often requiring powerful GPUs or TPUs. Cloud computing resources can be a flexible and scalable option.

• Set up Python (PyTorch & Transformers) for data preprocessing and model evaluation.

3. Model Fine-Tuning

• Start with the pre-trained LLaMA-2 model. Configure the model’s hyperparameters for fine-tuning, such as learning rate, batch size, and the number of epochs.

• Use the prepared dataset to fine-tune LLaMA-2. This involves training the model to adjust its weights specifically to the medical questions and answers domain.