The Ultimate Guide to LoRA: How to Fine-Tune LLMs Correctly, Part 2

A deep dive into the mathematics, memory architecture, structural consequences, and deployment decisions behind low-rank adaptation Most engineers who fine-tune language models treat the process as a configuration exercise. Set a rank, pick target modules from a blog post, run the job, and hope the loss curve looks right. That approach works until it does not. Until the model learns nothing despite a clean loss curve, or collapses on day two of