WebSmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models Guangxuan Xiao* , Ji Lin* , Mickael Seznec , Julien Demouth , Song Han , arXiv / Code Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models Muyang Li , Ji Lin , Chenlin Meng , Stefano Ermon , Song Han , Jun-Yan Zhu WebZeRO技术. 解决数据并行中存在的内存冗余的问题. 在DeepSpeed中，上述分别对应ZeRO-1,ZeRO-2,ZeRO-3. > 前两者的通信量和传统的数据并行相同，最后一种方法会增加通信量. …

Artificial Intelligence & Deep Learning **SmoothQuant: Accurate …

WebLarge language models (LLMs) show excellent performance but are compute- and memory-intensive. Quantization can reduce memory and accelerate inference. However, for LLMs … WebSmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models Guangxuan Xiao*, Ji Lin*, Mickael Seznec, Julien Demouth, Song Han arXiv Sparse … irish queens in history

ChatGPT等大模型的模型量化：平滑量化法 - 代码天地

WebFigure 1: SmoothQuant’s intuition: the activation X is hard to quantize because outliers stretch the quantization range, leaving few effective bits for most values. We migrate the … Web22 Nov 2024 · Reading the SmoothQuant paper ( arxiv.org/abs/2211.10438 ), which is quite ingenious and wanted to share. Since matmul, A*B=C, is linear, we can shift information in A or B around. As such, we can balance the quantization difficulty across both matrices leading to great performance! 5:18 PM · Nov 22, 2024 13 Retweets 2 Quote Tweets 122 … Web[R] SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models - Massachusetts Institute of Technology and NVIDIA Guangxuan Xiao et al - … irish quiz league