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Basil Hosmer

2 accepted papers

2024

CHAI: Clustered Head Attention for Efficient LLM Inference

ICML 2024poster

Large Language Models (LLMs) with hundreds of billions of parameters have transformed the field of machine learning. However, serving these models at inference time is both compute and memory intensive, where a single request can require multiple GPUs and tens of Gigabytes of memory. Multi-head atte…

2024

LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding

ACL 2024long

We present LayerSkip, an end-to-end solution to speed-up inference of large language models (LLMs). First, during training we apply layer dropout, with low dropout rates for earlier layers and higher dropout rates for later layers, and an early exit loss where all transformer layers share the same e…