How Good Are Low-bit Quantized LLaMA3 Models? An Empirical Study
Meta's LLaMA family has become one of the most powerful open-source Large
Language Model (LLM) series. Notably, LLaMA3 models have recently been released
and achieve impressive performance across various with super-large scale
pre-training on over 15T tokens of data. Given the wide application of low-bit
quantization for LLMs in resource-limited scenarios, we explore LLaMA3's
capabilities when quantized to low bit-width. This exploration holds the
potential to unveil new insights and challenges for low-bit quantization of
LLaMA3 and other forthcoming LLMs, especially in addressing performance
degradation problems that suffer in LLM compression. Specifically, we evaluate
the 10 existing post-training quantization and LoRA-finetuning methods of
LLaMA3 on 1-8 bits and diverse datasets to comprehensively reveal LLaMA3's
low-bit quantization performance. Our experiment results indicate that LLaMA3
still suffers non-negligent degradation in these scenarios, especially in
ultra-low bit-width. This highlights the significant performance gap under low
bit-width that needs to be bridged in future developments. We expect that this
empirical study will prove valuable in advancing future models, pushing the
LLMs to lower bit-width with higher accuracy for being practical. Our project
is released on https://github.com/Macaronlin/LLaMA3-Quantization and quantized
LLaMA3 models are released in https://huggingface.co/LLMQ.
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Wei Huang
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Wei Huang
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Xudong Ma
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Haotong Qin
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Haotong Qin
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Xingyu Zheng
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Xingyu Zheng
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Xingyu-Zheng
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Chengtao Lv
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吕呈滔
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lvchengtao
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Hong Chen
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ChenHong
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chhhhhhh
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Jie Luo
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Xiaojuan Qi
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xiaojuan qi
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Xianglong Liu
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Michele Magno
How Good Are Low-bit Quantized LLaMA3 Models? An Empirical Study
Meta's LLaMA family has become one of the most powerful open-source Large
Language Model (LLM) series. Notably, LLaMA3 models have recently been released
and achieve impressive performance across various with super-large scale
pre-training on over 15T tokens of data. Given the wide application of low-bit
quantization for LLMs in resource-limited scenarios, we explore LLaMA3's
capabilities when quantized to low bit-width. This exploration holds the
potential to unveil new insights and challenges for low-bit quantization of
LLaMA3 and other forthcoming LLMs, especially in addressing performance
degradation problems that suffer in LLM compression. Specifically, we evaluate
the 10 existing post-training quantization and LoRA-finetuning methods of
LLaMA3 on 1-8 bits and diverse datasets to comprehensively reveal LLaMA3's
low-bit quantization performance. Our experiment results indicate that LLaMA3
still suffers non-negligent degradation in these scenarios, especially in
ultra-low bit-width. This highlights the significant performance gap under low
bit-width that needs to be bridged in future developments. We expect that this
empirical study will prove valuable in advancing future models, pushing the
LLMs to lower bit-width with higher accuracy for being practical. Our project
is released on https://github.com/Macaronlin/LLaMA3-Quantization and quantized
LLaMA3 models are released in https://huggingface.co/LLMQ.
LLaMA3 performance degrades significantly when quantized to low bit-width, highlighting challenges in low-bit quantization for large language models.
Large Language Model LLaMA LLaMA3 low-bit quantization resource-limited post-training quantization LoRA-finetuning ultra-low bit-width performance degradation LLM compression