<ul><li>GuidedQuant is a novel quantization approach that integrates gradient information from the end loss into the quantization objective while preserving cross-weight dependencies within output channels.</li><li>This approach enhances the performance of quantization methods across weight-only scalar, weight-only vector, and weight-and-activation quantization.</li><li>A novel non-uniform scalar quantization algorithm introduced alongside GuidedQuant is proven to monotonically decrease the quantization objective value, outperforming existing methods in this category.</li><li>The code for GuidedQuant is available at https://github.com/snu-mllab/GuidedQuant.</li></ul>

GuidedQuant: Large Language Model Quantization via Exploiting End Loss Guidance

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