MUXQ uses low-rank outlier decomposition to redistribute activation outliers, allowing mixed-to-uniform INT8 quantization of LLMs with lower perplexity than naive methods on GPT-2 models.
A survey of quan- tization methods for efficient neural network inference,
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MUXQ: Mixed-to-Uniform Precision MatriX Quantization via Low-Rank Outlier Decomposition
MUXQ uses low-rank outlier decomposition to redistribute activation outliers, allowing mixed-to-uniform INT8 quantization of LLMs with lower perplexity than naive methods on GPT-2 models.