What is Low-Rank Adaptation (LoRA) | explained by the inventor | Matt Johnson
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🚀 LoRA (Low-Rank Adaptation) is one of those ideas that redefined efficiency in model fine-tuning. Instead of updating every parameter of a massive pre-trained model, LoRA injects small trainable matrices into specific layers — learning low-rank updates that approximate the full weight changes. This animation by Tom Yeh captures that process visually: You can almost see how LoRA decomposes the adaptation into a compact subspace, focusing only on the dimensions that truly matter for the new task
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🚀 LoRA (Low-Rank Adaptation) is one of those ideas that redefined efficiency in model fine-tuning. Instead of updating every parameter of a massive pre-trained model, LoRA injects small trainable matrices into specific layers — learning low-rank updates that approximate the full weight changes. This animation by Tom Yeh captures that process visually: You can almost see how LoRA decomposes the adaptation into a compact subspace, focusing only on the dimensions that truly matter for the new task
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