What to know about low-rank adaptation (LoRA)
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The success of recently released open-source large language models has sparked interest and growing activity in the field.
Some of these efforts focus on making the fine-tuning of LLMs more cost-efficient. One of the techniques that helps reduce the costs of fine-tuning enormously is “low-rank adaptation” (LoRA). With LoRA, you can fine-tune LLMs at a fraction of the cost it would normally take.
Key findings:
The weights of pre-trained LLMs form a matrix that models their huge training dataset
Research shows you can fine-tune a pre-trained model for a downstream task on a much smaller vector space
LoRA takes advantage of this characteristic to create a very small set of learnable weights for fine-tuning LLMs
The smaller set of LoRA weights makes it much faster and less costly to fine-tune LLMs
The benefits of LoRA go beyond cutting the cost of fine-tuning models
Read the full article on TechTalks.
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