From Fine-Tuning to Prompt Engineering: Theory and Practice for Efficient Transformer Adaptation
The Challenge of Fine-Tuning Large Transformer Models Self-attention enables transformer models to capture long-range dependencies in text, which is crucial for comprehending complex language patterns. These models work efficiently with massive datasets and achieve remarkable performance without needing task-specific structures. As a result, they are widely applied across industries, including software development, education, and content […] The post From Fine-Tuning to Prompt Engineering: Theory and Practice for Efficient Transformer Adaptation appeared first on MarkTechPost. read more