Hugging Face 推出 Assisted Generation：邁向低延遲文本生成的新方向
Original: Assisted Generation: a new direction toward low-latency text generation
Large language models (LLMs) typically generate text using an "autoregressive" mechanism, meaning the model must generate one token at a…
Hugging Face 發表「輔助生成（Assisted Generation）」技術（即投機解碼），旨在解決 LLM 自迴歸生成速度慢的痛點。該技術透過一個體積小、速度快的「輔助模型」預先生成候選 Token，再由「目標大模型」進行單次並行驗證。此方法在不降低輸出品質的前提下，能將生成速度提升高達 2 至 3 倍，為低延遲文本生成開闢了新路徑。
Large language models (LLMs) typically generate text using an "autoregressive" mechanism, meaning the model must generate one token at a time. Each generation step requires loading the entire model into GPU memory and executing a full forward pass. This mode of operation is memory-bandwidth bound, resulting in slow generation speeds and high latency.
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