@inproceedings{liu-etal-2024-llm,
title = "{LLM}-{QAT}: Data-Free Quantization Aware Training for Large Language Models",
author = "Liu, Zechun and
Oguz, Barlas and
Zhao, Changsheng and
Chang, Ernie and
Stock, Pierre and
Mehdad, Yashar and
Shi, Yangyang and
Krishnamoorthi, Raghuraman and
Chandra, Vikas",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.26",
pages = "467--484",
abstract = "Several post-training quantization methods have been applied to large language models (LLMs), and have been shown to perform well down to 8-bits. We find that these methods break down at lower bit precision, and investigate quantization-aware training for LLMs (LLM-QAT) to push quantization levels even further. We propose a data-free distillation method that leverages generations produced by the pre-trained model, which better preserves the original output distribution and allows quantizing any generative model independent of its training data, similar to post-training quantization methods. In addition to quantizing weights and activations, we also quantize the KV cache, which is critical for increasing throughput and supporting long sequence dependencies at current model sizes. We experiment with LLaMA models of sizes 7B, 13B, and 30B, at quantization levels down to 4-bits. We observe large improvements over training-free methods, especially in the low-bit settings.",
}
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<abstract>Several post-training quantization methods have been applied to large language models (LLMs), and have been shown to perform well down to 8-bits. We find that these methods break down at lower bit precision, and investigate quantization-aware training for LLMs (LLM-QAT) to push quantization levels even further. We propose a data-free distillation method that leverages generations produced by the pre-trained model, which better preserves the original output distribution and allows quantizing any generative model independent of its training data, similar to post-training quantization methods. In addition to quantizing weights and activations, we also quantize the KV cache, which is critical for increasing throughput and supporting long sequence dependencies at current model sizes. We experiment with LLaMA models of sizes 7B, 13B, and 30B, at quantization levels down to 4-bits. We observe large improvements over training-free methods, especially in the low-bit settings.</abstract>
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%0 Conference Proceedings
%T LLM-QAT: Data-Free Quantization Aware Training for Large Language Models
%A Liu, Zechun
%A Oguz, Barlas
%A Zhao, Changsheng
%A Chang, Ernie
%A Stock, Pierre
%A Mehdad, Yashar
%A Shi, Yangyang
%A Krishnamoorthi, Raghuraman
%A Chandra, Vikas
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F liu-etal-2024-llm
%X Several post-training quantization methods have been applied to large language models (LLMs), and have been shown to perform well down to 8-bits. We find that these methods break down at lower bit precision, and investigate quantization-aware training for LLMs (LLM-QAT) to push quantization levels even further. We propose a data-free distillation method that leverages generations produced by the pre-trained model, which better preserves the original output distribution and allows quantizing any generative model independent of its training data, similar to post-training quantization methods. In addition to quantizing weights and activations, we also quantize the KV cache, which is critical for increasing throughput and supporting long sequence dependencies at current model sizes. We experiment with LLaMA models of sizes 7B, 13B, and 30B, at quantization levels down to 4-bits. We observe large improvements over training-free methods, especially in the low-bit settings.
%U https://aclanthology.org/2024.findings-acl.26
%P 467-484
Markdown (Informal)
[LLM-QAT: Data-Free Quantization Aware Training for Large Language Models](https://aclanthology.org/2024.findings-acl.26) (Liu et al., Findings 2024)
ACL
- Zechun Liu, Barlas Oguz, Changsheng Zhao, Ernie Chang, Pierre Stock, Yashar Mehdad, Yangyang Shi, Raghuraman Krishnamoorthi, and Vikas Chandra. 2024. LLM-QAT: Data-Free Quantization Aware Training for Large Language Models. In Findings of the Association for Computational Linguistics ACL 2024, pages 467–484, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.