How to Fine-Tune Llama 3 for Customer Service

Fine-tuning a large language model (LLM) is the process of taking a pre-trained base LLM and further training it on a specialized dataset for a specific task or knowledge domain. This allows organizations to leverage existing AI development work and create personalized LLMs without having to train one from scratch, saving time and resources. Fine-tuning an LLM can be beneficial in various ways, including increased task or domain specificity, customization, and reduced costs. One potential use case for fine-tuned LLMs is customer service, where they can power chatbots, perform sentiment analysis, and generate content such as call summaries and key insights. Fine-tuning an LLM involves installing libraries, downloading a base model, preparing fine-tuning data, setting hyperparameters, establishing evaluation metrics, and fine-tuning the base model. Common pitfalls when fine-tuning an LLM include catastrophic forgetting, overfitting, underfitting, difficulty sourcing data, time requirements, and increasing costs.