RCNN Ticker
@RCNNticker · Post #315 · 12/19/2017, 04:21 PM
全球第五个单人六重蓝竹笋 也是 呆瓜 @DuangWB (原 @DuangW) 的第二个单六 于 12 月 10 日 清晨 06:49 (中国 04:49) 耗时 5h18min 再次在澳大利亚布里斯班完成 距第四个单六竹笋被呆瓜于 11 月 26 日 耗时 8h48min 完成 仅过两周 #Homogeneous
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Source channel @githubtrending · Post #14747 · May 25
#python#deep_learning#intel#machine_learning#neural_network#pytorch#quantization Intel Extension for PyTorch boosts the speed of PyTorch on Intel hardware, including both CPUs and GPUs, by using special features like AVX-512, AMX, and XMX for faster calculations[5][2][4]. It supports many popular large language models (LLMs) such as Llama, Qwen, Phi, and DeepSeek, offering optimizations for different data types and easy GPU acceleration. This means you can run advanced AI models much faster and more efficiently on your Intel computer, with simple setup and support for both ready-made and custom models. https://github.com/intel/intel-extension-for-pytorch
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ФГБУН Институт высокотемпературной электрохимии УрО РАН
@ihteubras · Post #329 · 07/18/2023, 05:16 AM
В журнале Electrochemical Materials and Technologies вышла обзорная работа "H/D exchange studies of methane activation mechanisms in heterogeneous catalysis" 🔗https://doi.org/10.15826/elmattech.2023.2.014 🔗https://journals.urfu.ru/index.php/elmattech/article/view/6883 В данном обзоре подробно рассматривается механизм конверсии метана и анализируются существующие теоретические и экспериментальные подходы к изотопному обмену H/D между метаном и каталитическими системами: #CH4#methane#conversion#isotope#catalyst#bonds#homogeneous#exchange #