Complete Guide to LLM Fine-Tuning: LoRA, VRAM, Learning Rates and More

A comprehensive guide published on DEV Community outlines the full lifecycle of training large language models, from data preparation and tokenizer selection to deployment monitoring. The guide emphasizes that successful AI model development requires a clear objective, clean and legally usable data, an appropriate architecture, and rigorous evaluation. It explains core concepts such as loss functions, backpropagation, and optimization algorithms like AdamW, which are applicable beyond LLMs to computer vision and speech models. A key focus is helping developers choose the right training approach — such as LoRA for efficient fine-tuning versus costly from-scratch pretraining — based on their specific use case. The guide warns that selecting the wrong method early can waste millions of training examples and significant computational resources.