Papers by Insu Han

1 papers
Mamba Drafters for Speculative Decoding (2025.findings-emnlp)

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Challenge: Existing drafters that use external drafters suffer from slower drafting while self-speculation methods use drafters tailored to the target model but require re-training.
Approach: They propose a drafter based on a state space model, Mamba, as a solution that combines the best aspects of both approaches.
Outcome: The proposed drafters outperform existing drafters while using less memory and maintaining their cross-model adaptability.

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