Comparison
Fine-tuning vs LoRA
Fine-tuning and LoRA 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 LoRA
When full fine-tuning is too expensive or you want swappable specialized adapters.
Fine-tuning Llama-3-8B for a domain on a single A100 with LoRA.
Frequently asked
What is the difference between Fine-tuning and LoRA?
Fine-tuning: Fine-tuning continues training a pretrained model on a smaller, task-specific dataset, adjusting its weights to specialize behavior or knowledge. LoRA: LoRA is a parameter-efficient fine-tuning method that freezes a model's original weights and learns small low-rank update matrices alongside them. Cheap fine-tuning on a single GPU.
When should I use Fine-tuning vs LoRA?
After you've exhausted prompting and retrieval, and you have a few hundred to thousands of clean labeled examples. When full fine-tuning is too expensive or you want swappable specialized adapters.
Are Fine-tuning and LoRA the same thing?
No. Fine-tuning is training; LoRA is training. They are related but address different parts of the AI stack.