Tag: fine-tuning

This article explores the advanced techniques of fine-tuning large language models (LLMs) for domain adaptation, focusing on training strategies, scaling, model merging, and synergistic capabilities. It provides a technical tutorial for adapting LLMs to specific domains, enhancing their performance and utility.

This tutorial provides a step-by-step guide on fine-tuning the Audio Spectrogram Transformer (AST) model for audio classification tasks using the Hugging Face ecosystem. It covers data loading, preprocessing, audio augmentation, model configuration, and training, enabling users to adapt pre-trained AST models to their specific datasets for improved performance and efficiency.