4 papers
TapOut: A Bandit-Based Approach to Dynamic Speculative Decoding
Aditya Sridhar, Nish Sinnadurai, Sean Lie +1
Speculative decoding accelerates LLMs by using a lightweight draft model to generate tokens autoregressively before verifying them in parallel with a larger target model. However,…
SD: Self-Distilled Sparse Drafters
Mike Lasby, Nish Sinnadurai, Valavan Manohararajah +3
Speculative decoding is a powerful technique for reducing the latency of Large Language Models (LLMs), offering a fault-tolerant framework that enables the use of highly compressed…
MASSV: Multimodal Adaptation and Self-Data Distillation for Speculative Decoding of Vision-Language Models
Mugilan Ganesan, Shane Segal, Ankur Aggarwal +3
Speculative decoding significantly accelerates language model inference by enabling a lightweight draft model to propose multiple tokens that a larger target model verifies simulta…
Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment
Abhinav Agarwalla, Abhay Gupta, Alexandre Marques +9
Large language models (LLMs) have revolutionized Natural Language Processing (NLP), but their size creates computational bottlenecks. We introduce a novel approach to create accura…