When would you choose LoRA instead of full fine-tuning?

Instruction: Compare adaptation choices using task requirements, data, training resources, and deployment constraints.

Context: Discuss low-rank adapters and full fine-tuning without assuming either always gives better results.

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I’d try LoRA when I need task adaptation with fewer trainable parameters and less training memory than updating the whole model...

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