Comparison
Fine-tuning vs QLoRA
Fine-tuning and QLoRA are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Fine-tuning
After you've exhausted prompting and retrieval, and you have a few hundred to thousands of clean labeled examples.
Fine-tuning Llama 3 on medical Q&A for a clinical assistant.
When you would reach for QLoRA
When you want to fine-tune a frontier-sized open model on a single GPU.
Fine-tuning Llama-3-70B on a domain corpus on a single A100.
Frequently asked
What is the difference between Fine-tuning and QLoRA?
Fine-tuning: Fine-tuning continues training a pretrained model on a smaller, task-specific dataset, adjusting its weights to specialize behavior or knowledge. QLoRA: QLoRA fine-tunes a 4-bit quantized base model with LoRA adapters, letting you train 70B-class models on a single 48 GB GPU at near-full fine-tuning quality.
When should I use Fine-tuning vs QLoRA?
After you've exhausted prompting and retrieval, and you have a few hundred to thousands of clean labeled examples. When you want to fine-tune a frontier-sized open model on a single GPU.
Are Fine-tuning and QLoRA the same thing?
No. Fine-tuning is training; QLoRA is training. They are related but address different parts of the AI stack.