Home Glossary Low-Rank Adaptation (LoRA)

Low-Rank Adaptation (LoRA) - Page 11

Low-rank adaptation, known as LoRA, fine-tunes a model by training small added matrices while leaving the original weights frozen. The method assumes that useful task-specific changes can be represented with far fewer parameters than the full model contains. This lowers memory requirements, shortens training, and makes it practical to store separate adapters for different domains or styles. LoRA is common with large language and image models, although adapter quality still depends on data, rank, target layers, and evaluation. It does not update a model’s knowledge automatically or remove inherited risks, and combining several adapters can create interactions that require additional testing before deployment.

Andrew Tulloch Leaves $12B AI Startup to Join Meta After Turning Down $1.5B Offer
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AI & Machine Learning, Immersive Reality (AR, VR, MR, and XR), News, Startups & Investment

Andrew Tulloch Leaves $12B AI Startup to Join Meta After Turning Down $1.5B Offer

By • 3 mins read

Andrew Tulloch, co-founder of the $12 billion AI startup Thinking Machines Lab, has joined Meta after previously rejecting what reports described as a $1.5 billion offer — a figure Meta has since called ‘inaccurate and ridiculous.’