This work studies post-training parameter quantization in large language models (LLMs).
We introduce quantization with incoherence processing (QuIP), a new method based on the …

Large language models (LLMs) have revolutionized natural language processing tasks.
However, their practical deployment is hindered by their immense memory and computation …

Generative Large Language Models (LLMs) have demonstrated remarkable results for a
wide range of tasks. However, deploying these models for inference has been a significant …

Large models based on the Transformer architecture play increasingly vital roles in artificial
intelligence, particularly within the realms of natural language processing (NLP) and …

In the rapidly evolving landscape of artificial intelligence (AI), generative large language
models (LLMs) stand at the forefront, revolutionizing how we interact with our data. However …

The rapid growth of Large Language Models (LLMs) has been a driving force in
transforming various domains, reshaping the artificial general intelligence landscape …

Abstract Large Language Models (LLMs) have demonstrated remarkable capabilities in
important tasks such as natural language understanding, language generation, and …

Since the advent of personal computing devices, intelligent personal assistants (IPAs) have
been one of the key technologies that researchers and engineers have focused on, aiming …

As the size of large language models (LLMs) continues to grow, model compression without
sacrificing accuracy has become a crucial challenge for deployment. In this paper, we …

Large Language Models (LLMs) excel in NLP, but their demands hinder their widespread
deployment. While Quantization-Aware Training (QAT) offers a solution, its extensive …