Concept
Prefix-tuning
Prefix-tuning is a parameter-efficient fine-tuning method that steers a frozen language model by prepending a small set of trainable vectors to the input at every transformer layer. The base model's weights never change.
The rest of “Prefix-tuning” is a premium feature: every concept in the library gets a precise, practitioner-focused write-up like this one, cross-linked straight from the paper summaries that use it.
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