Allowing each quantization group to select among multiple 4-bit grids improves accuracy over single-grid FP4 for both post-training and pre-training of LLMs.
Piqa: Reasoning about physical commonsense in natural language
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cs.LG 2years
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Different calibration objectives produce distinct layer pruning patterns in LLMs, while search algorithms converge to similar solutions under a fixed objective.
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Grid Games: The Power of Multiple Grids for Quantizing Large Language Models
Allowing each quantization group to select among multiple 4-bit grids improves accuracy over single-grid FP4 for both post-training and pre-training of LLMs.
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Rethinking Layer Redundancy in Large Language Models: Calibration Objectives and Search for Depth Pruning
Different calibration objectives produce distinct layer pruning patterns in LLMs, while search algorithms converge to similar solutions under a fixed objective.