Pre-Publication V2. Custom Model: Lora LoRA Model
Low-Rank Adaptation (LoRA) Model is a parameter-efficient A fine-tuning technique designed used to adapt large pre-trained, or foundational, models to perform for specific tasks with minimal computational and memory overhead and without modifying the foundational model’s core weights. Instead of rewriting the entire model, a LoRA adds a small learned layer (“adapter”) that influences how the underlying model behaves.
Deliberation Summary:
Language to increase clarity for unfamiliar readers/spelling; add a cross-reference to the separate entry on model weights.
Pre-Publication V1. Low-Rank Adaptation (LoRA) Model is a parameter-efficient fine-tuning technique used to adapt large pre-trained, or foundational, models for specific tasks with minimal computational and memory overhead and without modifying the foundational model’s core weights. Instead of rewriting the entire model, a LoRA adds a small learned layer (“adapter”) that influences how the underlying model behaves.