Inside LLMs: Unraveling the Architecture, Training, and Real-World Use of Large Language Models

The book begins by exploring the fundamental concepts behind LLMs, including their architectural components, such as transformers and attention mechanisms. It delves into the intricacies of self-attention, positional encoding, and multi-head attention, highlighting how these elements work together to create powerful language models.

In the training section, the book covers essential strategies for pre-training and fine-tuning LLMs, including various paradigms like masked language modeling and next sentence prediction. It also addresses advanced topics such as domain-specific fine-tuning, transfer learning, and continual adaptation, providing practical insights into optimizing model performance for specialized tasks.