A Step-by-Step Guide to Fine-Tuning Large Language Models (LLMs)

Embarking on the World of Large Language Models (LLMs)

Large Language Models (LLMs) represent a breakthrough in deep learning, specifically designed to comprehend the nuances of human-like text and execute diverse tasks, ranging from sentiment analysis to language modeling (next-word prediction), text generation, text summarization, and more. These models undergo extensive training on vast repositories of text data.

Little do we realize that we interact with applications powered by LLMs daily. Google, for instance, harnesses the power of BERT (Bidirectional Encoder Representations for Transformers) across various applications, enhancing query completion, contextual understanding of queries, delivering more relevant search results, language translation, and beyond.

Constructed on the foundations of deep learning principles, intricate neural networks, and sophisticated techniques like self-attention, these models delve deep into the language’s patterns, structures, and semantics during their extensive training.

Given the substantial resources and time investment required for training these models on colossal datasets, starting from scratch is often impractical. However, there are techniques that allow us to directly leverage…