Seeking Speed without Loss in Large Language Models? Meet EAGLE: A Machine Learning Framework Setting New Standards for Lossless Acceleration

For LLMs, auto-regressive decoding is now considered the gold standard. Because LLMs generate output tokens individually, the procedure is time-consuming and expensive. Methods based on speculative sampling provide an answer to this problem. In the first, called the “draft” phase, LLMs are hypothesized at little cost; in the second, called the “verification” phase, all of […]

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