Large Language Models (LLMs) have demonstrated remarkable capabilities across a myriad of natural language processing tasks, from content generation to complex reasoning. However, achieving peak performance and ensuring alignment with specific user needs and safety guidelines remains a significant challenge. Traditional fine-tuning approaches often fall short in capturing the nuanced preferences and implicit knowledge that […]
Distributed training is a critical technique for handling large-scale machine learning models, especially when dealing with large language models (LLMs) that require significant computational resources. Amazon SageMaker, in combination with Hugging Face, provides a powerful platform for distributed training. This article will guide you through the process of fine-tuning a large language model using model […]
