Symbol r
r is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.
Read this term in its guide →Published equation contexts
Only B and A are trained; never moves during fine-tuning [ 5 ] . The effect on trainable parameter count for that one matrix is to fall from dk to r(d + k) , which is small whenever the chosen rank r is small relative to d and k — and because BA can be merged back into after training, LoRA adds no extra inference latency once deployed. Hu and colleagues report, for their comparison against full fine-tuning of GPT-3 175B, up to a 10,000-fold reduction in trainable parameters and a threefold reduction in GPU memory requirement, with quality on par with or better than full fine-tuning on the benchmarks they tested [ 5 ] . Those figures are the paper’s own reported comparison for a…
r is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.
Read this term in its guide →d is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.
Read this term in its guide →k is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.
Read this term in its guide →Read this expression with the definitions, units, and assumptions supplied by the article.
A symbol can carry a different meaning in another article. Each occurrence keeps its own guide and term definitions.
Equation 7 · Open Models
This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text.
Only B and A are trained; never moves during fine-tuning [ 5 ] . The effect on trainable parameter count for that one matrix is to fall from dk to r(d + k) , which is small whenever the chosen rank r is small relative to d and k — and because BA can be merged back into after training, LoRA adds no extra inference latency once deployed. Hu and colleagues report, for their comparison against full fine-tuning of GPT-3 175B, up to a 10,000-fold reduction in trainable parameters and a threefold reduction in GPU memory requirement, with quality on par with or better than full fine-tuning on the benchmarks they tested [ 5 ] . Those figures are the paper’s own reported comparison for a…
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