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Found 4 papers, 2 papers with code
E-Sparse: Boosting the Large Language Model Inference through Entropy-based N:M Sparsity
Traditional pruning methods are known to be challenging to work in Large Language Models (LLMs) for Generative AI because of their unaffordable training process and large computational demands.
Improving Post-Training Quantization on Object Detection with Task Loss-Guided Lp Metric
PTQ optimizes the quantization parameters by different metrics to minimize the perturbation of quantization.
RPTQ: Reorder-based Post-training Quantization for Large Language Models
In this paper, we identify that the challenge in quantizing activations in LLMs arises from varying ranges across channels, rather than solely the presence of outliers.
PD-Quant: Post-Training Quantization based on Prediction Difference Metric
It determines the quantization parameters by using the information of differences between network prediction before and after quantization.
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