Stacking transformer layers to create large models results in better accuracies, few-shot learning capabilities, and even near-human emergent abilities on a wide range of language tasks. These foundation models are expensive to train, and they can be memory- and compute-intensive during inference (a recurring cost). The most popular large language models (LLMs) today can reach tens to hundreds of��
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