Step-by-Step QLoRA Fine-Tuning of Llama-2-13B for Domain-Specific Q&A on a Single GPU
Key Takeaways You can fine-tune massive language models like Llama-2-13B on a single consumer GPU (e.g., RTX 3090/4090) with at least 24GB of VRAM. The key is QLoRA , a technique that combines 4-bit quantization to shrink the model's memory footprint with Low-Rank Adapters (LoRA) to train only a tiny fraction of the parameters. This guide provides a complete, practical workflow: preparing a Q&A dataset, writing the Python script, launching the training job, and testing your new specialized model. Just a few years ago, the idea of fine-tuning a massive 13-billion-parameter language model would have required a small fortune and a dedicated server room humming with A100s. If you told me you were doing it at home, I’d assume you were either a certified genius with a secret NVIDIA sponsorship or completely delusional. Well, the landscape has radically shifted. I’m about to show you how to take a powerhouse model like Llama-2-13B, which normally wouldn’t even fit on a consu...
