LLM Concepts Explained: Sampling, Fine-tuning, Sharding, LoRA
An episode of the Build Wiz AI Show podcast, hosted by Build Wiz AI, titled "LLM Concepts Explained: Sampling, Fine-tuning, Sharding, LoRA" was published on March 20, 2025 and runs 17 minutes.
March 20, 2025 ·17m · Build Wiz AI Show
Summary
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.
Episode Description
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.
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