Poznámky k epizodě
This technical paper proposes a novel technique called Low-Rank Adaptation (LoRA) for adapting large language models (LLMs) to specific downstream tasks. LoRA addresses the challenge of fine-tuning LLMs, which requires updating all model parameters, by injecting low-rank decomposition matrices into each layer of the Transformer architecture. This significantly reduces the number of trainable parameters, resulting in a substantial decrease in storage requirements, memory usage, and training time. The paper shows that LoRA performs comparably or even better than fine-tuning on various tasks…
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