Several key concepts and techniques essential for working with large language models (LLMs). It begins by explaining sampling, the probabilistic method for generating diverse text, and contrasts it with fine-tuning, which adapts pre-trained models for specific tasks. The text then discusses sharding, a method for distributing large models, and the role of a tokenizer in preparing text for processing. Furthermore, it covers parameter-efficient fine-tuning methods like LoRA and general PEFT, which allow for efficient model adaptation, and concludes by explaining checkpoints as mechanisms for saving and resuming training progress.