使用自投機解碼（Self-Speculative Decoding）加速文本生成：Meta 推出 LayerSkip 技術
Original: Faster Text Generation with Self-Speculative Decoding
The slow autoregressive generation speed of large language models (LLMs) has long been a major bottleneck in real-world deployment. While…
Hugging Face 介紹了 Meta 的 LayerSkip 技術，該技術透過「自投機解碼（Self-Speculative Decoding）」來加速 LLM 推理。傳統投機解碼需要額外的草稿模型，而 LayerSkip 讓單一模型在推理時自我預測與驗證。透過在訓練時加入層丟棄與早期退出損失，模型能用前幾層快速生成草稿，再由完整模型驗證，顯著降低記憶體佔用並提升速度。
The slow autoregressive generation speed of large language models (LLMs) has long been a major bottleneck in real-world deployment. While "speculative decoding" can effectively accelerate inference — by using a smaller "draft model" to pre-generate multiple tokens, which are then verified in a single pass by the "target model" — this approach requires loading two models simultaneously in GPU memory, increasing hardware overhead, and demands coordination between the vocabularies and architectures of two different models, making deployment more complex.
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