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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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🟣 2024 🟣 V. 11 🟣 Issue 4 🟣 Art. 202411411 🟣 Article 🟣 Опубликована новая статья 📜 Impact of calcium and copper co-doping on the oxygen transport of layered nickelates: a case study of Pr1.6Ca0.4Ni1–yCuyO4+δ and a comparative analysis 👩‍🎓👨‍🎓 V. Sadykov (https://orcid.org/0000-0003-2404-0325), N. Eremeev (https://orcid.org/0000-0002-3494-2771), E. Sadovskaya, T. Zhulanova (https://orcid.org/0000-0002-8009-4398), S. Pikalov (https://orcid.org/0000-0001-6292-0468), Y. Fedorova, E. Pikalova (https://orcid.org/0000-0001-8176-9417) 🏛 Federal Research Center Boreskov Institute of Catalysis SB RAS https://en.catalysis.ru 🏛 Institute of High-Temperature Electrochemistry UB RAS, https://ihte.ru 🏛 Ural Federal University, https://urfu.ru/en Institute of Metallurgy UB RAS, http://www.imeturan.ru 📚#SOFCs#SOECs#layered#nickelates#oxygen#transport#isotope#exchange 🔗https://doi.org/10.15826/chimtech.2024.11.4.11 https://journals.urfu.ru/index.php/chimtech/article/view/8073