LLM Fine-Tuning: A Guide for Domain-Specific Models

Large language models often struggle with specialized tasks, prompting the need for fine-tuning—a process that trains pre-existing models on custom datasets to create domain-specific experts with improved accuracy, customized tone, and lower inference costs than generic APIs. This comprehensive guide covers parameter-efficient techniques like LoRA and QLoRA that enable fine-tuning massive models on single GPUs while maintaining quality.