Jul 25, 2024 · Early-exit models, a variant of LLMs, improves the inference efficiency of LLMs by skipping rest layers and directly generate output tokens when ...

Jul 25, 2024 · In this work, we solves two key challenges in building efficient inference framework for early-exit models: (1) batch inference at iteration-level granularity; ...

Sep 8, 2024 · Early-exit models, a variant of LLMs, improves the inference efficiency of LLMs by skipping rest layers and directly generate output tokens when ...

Jul 29, 2024 · We present EE-LLM, a framework for large-scale training and inference of early-exit large language models (LLMs).

EE-LLM is a framework for large-scale training and inference of early-exit (EE) large language models (LLMs), which is built upon Megatron-LM and compatible ...

Early-exit models, a variant of LLMs, improves the inference efficiency of LLMs by skipping rest layers and directly generate output tokens when they are ...

Dec 31, 2023 · This paper proposes RAEE, a robust Retrieval-Augmented Early Exiting framework for efficient inference.

This variant, termed ENGINE (Early), facilitates dynamic early exit of node embedding encoding, accelerating inference and mitigating the risk of overthink- ing ...

Sep 25, 2024 · Summary: This paper aims to enhancing the efficiency of large language model inference by adaptively exiting the model at earlier layers. The ...

We present EE-LLM, a framework for large-scale training and inference of early-exit large language models (LLMs). While recent works have shown preliminary ...