LoRA vs Full Fine-Tuning: Cost-Accuracy Trade-offs

#lora #finetuning #llm #interview

When Your Interview Project Budget Says LoRA but Your Resume Needs SOTA

You've got 72 hours before the final interview. They want a fine-tuned model demo. Your GPU budget is $50. Full fine-tuning a 7B model will cost you $200 and 18 hours. LoRA promises 90% of the performance at 10% of the cost.

But here's the part most tutorials skip: that 10% accuracy gap? On some tasks it's negligible. On others it'll torpedo your entire demo.

I've burned through three interview projects learning this the hard way. Let me save you the pain.

Arduino and LoRa components set up on a breadboard for a DIY project. — Photo by Bmonster Lab on Pexels

The Math Everyone Glosses Over

Full fine-tuning updates every parameter in your model. For a 7B parameter LLM, that's 7 billion gradient updates per backward pass. LoRA (Low-Rank Adaptation) injects trainable rank decomposition matrices into each layer while freezing the original weights.

The core idea: instead of updating $W \in \mathbb{R}^{d \times k}$ directly, LoRA learns:

$$W' = W + \Delta W = W + BA$$

Continue reading the full article on TildAlice