When would you use LoRA vs full fine-tuning?
This tests your grasp of practical trade-offs in ML systems, specifically training cost versus model customization. A great answer explains that LoRA is a parameter-efficient method ideal for resource-constrained scenarios, reducing trainable parameters by 10,000x and GPU memory by 3x. Full fine-tuning is for high-budget projects requiring deep model changes. A red flag is vaguely saying LoRA is 'cheaper' without quantifying the resource savings or explaining the mechanism.
This question tests your understanding of practical ML systems design, focusing on the trade-offs between training cost, memory, inference latency, and model performance. A strong answer frames the choice as a cost-benefit analysis. You'd use LoRA when resources are limited, as it can reduce trainable parameters by 10,000x and GPU memory by 3x for a model like GPT-3. This also makes it ideal for deploying many specialized models, as each adapter is small. Full fine-tuning is reserved for scenarios with a large budget where you need to make deep, foundational changes to a model's behavior and can afford the high cost. The key red flag is failing to explain *why* LoRA is efficient (frozen weights, low-rank matrices) or quantify its benefits.
Read the original → arXiv
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- #lora
- #ml-systems
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