Full Fine-Tuning vs. Parameter-Efficient Fine-Tuning (PEFT): Key Principles of Dataset Curation

In the adaptation of large language models (LLMs), both Full Fine-Tuning and Parameter-Efficient Fine-Tuning (PEFT) demonstrate significant performance improvements. When choosing a fine-tuning strategy, factors such as computational resources, task performance, dataset quality, and diversity should be considered. This article explores the importance of dataset curation and best practices, and discusses...

Read more →