AI BriefingUAG Technology Unveiled for All Models
Key point
A technology has been developed that can speed up LLM inference using any small model, even if the tokenizer is different.
Details
Existing Assisted Generation (Speculative Decoding) had the constraint that the target model and the assistant model must share the same tokenizer. Because of this, certain model families (e.g., Gemma-2, Mixtral) had difficulty utilizing a sufficiently small and accurate assistant model suited for inference acceleration.
Universal Assisted Generation (UAG), jointly developed by Intel Labs and Hugging Face, supports using pairs of models with different tokenizers. For example, when targeting gemma-2-9b, you can use a tiny model from an entirely different family, such as vicuna-68m, as the assistant.
Key technology and features:
- 2-way Tokenizer Translation: Converts tokens generated by the assistant into text, then re-encodes them with the target model's tokenizer to align them.
- Inference acceleration: You can expect a 1.5x–2.0x speedup with any decoder or MoE model.
- Low overhead: There is almost no overhead incurred during the tokenizer-to-tokenizer conversion process.
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