Browse all content tagged with Fine-Tuning
Model fine-tuning adapts pre-trained models for new tasks by making minor adjustments, reducing data and resource needs. Learn how fine-tuning leverages transfer learning, different techniques, best practices, and evaluation metrics to efficiently improve model performance in NLP, computer vision, and more.
Instruction tuning is a technique in AI that fine-tunes large language models (LLMs) on instruction-response pairs, enhancing their ability to follow human instructions and perform specific tasks.
Parameter-Efficient Fine-Tuning (PEFT) is an innovative approach in AI and NLP that enables adapting large pre-trained models to specific tasks by updating only a small subset of their parameters, reducing computational costs and training time for efficient deployment.
