between $1.25 billion and $2.5 billion. This created a barrier where adapting such models to specific tasks through traditional fine-tuning became prohibitively expensive for most researchers and organizations.

Purpose

LoRA works by decomposing weight update matrices into lower-rank representations. Rather than updating all parameters in a neural network during fine-tuning, LoRA freezes the pre-trained model weights and injects trainable rank decomposition matrices into each

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