LLM Concepts Explained: Sampling, Fine-tuning, Sharding, LoRA

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.