Large Language Models: Scaling Laws, Transformers, and Fine-Tuning

Generative LLMs represent a paradigm shift in processing natural language. By utilizing the Transformer architecture, multi-head self-attention mechanisms capture long-range contextual relationships across tokens. This essay covers the pre-training methodologies, scaling laws, RLHF optimization, and Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA.