What is LLM fine-tuning?

This post provides a comprehensive overview of LLM fine-tuning, explaining its benefits (cost, efficiency, customization), common use cases (structured output, style adherence, domain-specific instruction), and where to fine-tune (OpenAI, Predibase, Modal, Colab, SageMaker). It also lists top frameworks (HuggingFace transformers, TRL, Axolotl) and outlines the steps for fine-tuning on Modal: choosing a base model, preparing the dataset (including prompt creation and optional special tokens), and using advanced strategies. The post links to a Modal tutorial for practical implementation.