How does LoRA make fine-tuning efficient (fewer parameters)? This animation is to show you. Pay attention to the number of red cells. On the left, a regular fine-tune would involve 10x10=100 trainable parameters to update the original weight matrix.. On the right, LoRA reduces to two smaller (low-rank) matrices, A and B, each has only 20 trainable parameters, together 40 trainable parameters, a 60% reduction. When weight matrices are larger in real-world applications, this reduction is even more
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