This study evaluates the effectiveness of an impact-based parameter selection criterion for quantizing large language models (LLMs). Through rigorous experiments on LLaMA models, researchers showed that this new method consistently outperformed the commonly used magnitude-based approach, demonstrating its ability to better identify critical parameters. The findings underscore the phenomenon of parameter heterogeneity and how it affects model performance during quantization. The research introduces CherryQ, a unified mixed-precision optimization technique, established as the state-of-the-art for both base and chat LLMs, offering improved efficiency in parameter selection.
Our proposed impact-based selection criterion for parameters demonstrated its effectiveness through rigorous comparison with the traditional magnitude-based criterion, consistently achieving superior results across various settings.
The experimental results reveal that the impact-based criterion not only outperforms the magnitude-based criterion, but also justifies the importance of accounting for parameter heterogeneity during quantization.
Through the study on LLaMA models, we showcased how CherryQ, a unified mixed-precision optimization strategy, excels in identifying critical cherry parameters, leading to enhanced performance.
The findings of this research highlight the significant influence of parameter selection methods on model performance, paving the way for more efficient architectures for LLMs.
#parameter-selection #mixed-precision-training #large-language-models #quantization #model-performance
Collection
[
|
...
]