Universal Assisted Generation：支援任意輔助模型的通用輔助生成技術，大幅提升解碼速度
Original: Universal Assisted Generation: Faster Decoding with Any Assistant Model
In the deployment and inference of large language models (LLMs), reducing generation latency has always been a critical challenge. The…
Hugging Face 發表「通用輔助生成 (UAG)」技術，解決了傳統投機解碼（Speculative Decoding）必須要求大小模型使用相同 Tokenizer 的限制。UAG 透過跨 Tokenizer 的對齊機制，讓開發者能自由搭配任意輕量模型（如 Gemma-2B）來加速大型目標模型（如 Llama-3-70B）。此技術已整合至 Hugging Face 的 Transformers 庫中，能顯著降低推論延遲並節省運算成本。
In the deployment and inference of large language models (LLMs), reducing generation latency has always been a critical challenge. The traditional approach of "Assisted Generation" (also known as Speculative Decoding) is a common acceleration technique. It works by using a small, fast "draft model" to predict several tokens in advance, which are then verified in parallel by the "target model" in a single pass. When the verification passes, multiple tokens can be output at once, dramatically improving decoding speed. However, the traditional approach has one fatal limitation: the draft model and the target model must use exactly the same tokenizer and vocabulary, which severely restricts developers' choices when selecting a draft model.
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