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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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Опубликована новая статья 🟣 2024 🟣 V. 11 🟣 Issue 2 🟣 Art. 202411207 🟣 Letter 🟣 📜 Features of electronic states in the vicinity of band gap and atomic structure of Ta- and Nb-doped Li7La3Zr2O12 👩‍🎓👨‍🎓 M. I. Vlasov (http://orcid.org/0000-0002-7814-7489), E.A. Surzhikov (http://orcid.org/0009-0005-3466-6374), A.Yu. Germov (http://orcid.org/0000-0001-6091-1250), E.A. Il'ina (http://orcid.org/0000-0003-1759-5234), I.A. Weinstein (http://orcid.org/0000-0002-5573-7128) 🏛 Institute of High Temperature Electrochemistry of the Ural Branch of the Russian Academy of Sciences, https://ihte.ru/?page_id=3106 🏛 Ural Federal University, https://urfu.ru/en 🏛 M.N. Mikheev Institute of Metal Physics of the Ural Branch of the Russian Academy of Sciences, https://www.imp.uran.ru/?q=en 🏛 Institute of Metallurgy of the Ural Branch of the Russian Academy of Sciences, http://www.imet-uran.ru 📚#Li7La3Zr2O12#Ta#Nb#doping#bandgap#oxygen#vacancies 🔗https://doi.org/10.15826/chimtech.2024.11.2.07 https://journals.urfu.ru/index.php/chimtech/article/view/7692