2024年10月30日 · To address this problem, we propose gradient-aware weight quantization (GWQ), the first quantization approach for low-bit weight quantization that leverages gradients to localize outliers, requiring only a minimal amount of calibration data for outlier detection.

GWQ is the first accurate first-order gradient-aware post-training weight quantization method for pre-trained LLMs, requiring only a minimal quantity of calibration data to identify outliers efficiently. GWQ outshines the current state-of-the-arts method SPQR on the wikitext and C4 datasets.

To address this problem, we propose gradient-aware weight quantization (GWQ), the first quantization approach for low- bit weight quantization that leverages gradients to localize outliers, requiring only a minimal amount of calibration data for outlier detection.

加权量化和（WQS）回归是一种统计 模型，用于环境暴露、表观/基因组学和代谢组学研究等常见的高维数据集的多变量回归。 该模型构建了一个加权指数，估计所有预测变量对结果的混合效应，然后可以在带有相关协变量的 回归模型 中使用，以检验该指数与因变量或结果的关联。 然后，每个单独的预测因素对整体指数效应的贡献可以通过模型分配给每个变量的权重的相对强度来评估。 gWQS包将WQS回归扩展到具有连续和分类结果的应用中，并实现了随机子集WQS和重 …

2024年10月30日 · To address this problem, we propose gradient-aware weight quantization (GWQ), the first quantization approach for low-bit weight quantization that leverages gradients to localize outliers, requiring only a minimal amount of calibration data for outlier detection.

In this paper, we propose a Group-Wise Quantization framework, called GWQ, to reduce computational consumption during the activation data pass process by allowing multiple layers share one scale factor in SFC operations. Specifically, in the GWQ framework, we propose two algorithms for network layers grouping and model training.

In this paper, we propose a Group-Wise Quantization framework, called GWQ, to reduce computational consumption during the activation data pass process by allowing multiple layers share one scale factor in SFC operations. Specifically, in the GWQ framework, we propose two algorithms for network layers grouping and model training.

2024年10月30日 · To address this problem, we propose gradient-aware weight quantization (GWQ), the first quantization approach for low-bit weight quantization that leverages gradients to...

2024年12月27日 · 为了解决这个问题，我们提出了梯度感知权重量化（GWQ），这是第一个低位权重量化的量化方法，它利用梯度来定位异常值，只需要最少量的校准数据即可进行 异常值检测。 GWQ 优先以 FP16 精度保留前 1% 异常值对应的权重，而其余非异常值权重以低位格式存储。 GWQ通过实验发现，与在Hessian矩阵定位模型中使用敏感权重相比，在梯度定位模型中使用敏感权重更加科学。 与当前的量化方法相比，GWQ 可以应用于多种语言模型，并在 WikiText2 和 …

To address this problem, we propose gradient-aware weight quantization (GWQ), the first quantization approach for low-bit weight quantization that leverages gradients to localize outliers, requiring only a minimal amount of calibration data for outlier detection.