The article discusses various quantization strategies for large language models (LLMs), emphasizing methods to reduce weight and activation precision while ensuring acceptable accuracy. Key techniques include post-training quantization, which directly quantizes pre-trained models, and quantization-aware training, which integrates quantization into the training loop. The article also delves into the modeling of parameter outliers, exploring magnitude and activation perspectives, and highlighting several quantitative methods developed for optimal outlier management, aiming to enhance model efficiency without significantly compromising performance.
Quantization strategies for large language models (LLMs) encompass techniques for optimizing weight and activation precision while balancing accuracy and efficiency, particularly post-training and quantization-aware training.
The research highlights the management of parameter outliers in LLM quantization, discussing approaches that assume Gaussian distributions and strategies to maintain higher precision for extreme outliers.
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