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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 #419 · 11/29/2023, 04:29 AM

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Опубликована новая работа 🟠 2023 🟠 V. 10 🟠 Issue 4 🟠 Art. 202310409 🟠 Article 🟠 📜 Features of forming a low-temperature cubic Li7La3Zr2O12 film by tape casting 👩‍🎓👨‍🎓 E. Lyalin (http://orcid.org/0000-0002-0539-506X), L. Pershina (http://orcid.org/0000-0003-1759-5234), E. Il’ina, K. Druzhinin (http://orcid.org/0000-0002-8947-8208), S. Belyakov (https://orcid.org/0000-0001-9237-8307) 🏛 Institute of High Temperature Electrochemistry, UB RAS, https://ihte.ru 🏛 Ural Federal University, https://urfu.ru/en 📚#Li7La3Zr2O12#LIB#lithium#conductivity#films#tapecasting#allSSB 🔗https://doi.org/10.15826/chimtech.2023.10.4.09 https://www.chimicatechnoacta.ru/index.php/chimtech/article/view/7161

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