#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
EVAA: Introducing Loop APY for LP Pool Interface
#Loop#EVAA
EVAA introduces a new Loop APY feature in its LP Pool Interface, enabling users to deposit LP tokens from StormTrade or DeDust as collateral, borrow TON or USDT, and utilize a liquidity looping strategy to potentially enhance annual returns. This strategy combines third-party yields, EVAA rates, and compounding effects.
Source: link
@tonlines
For operatori
Umuman olganda kod yozayotganingizda bir xil hisoblash jarayonini qayta-qayta yozish qimmatli vaqtingizni o'g'irlab sizni bezor qilishi mumkin, masalan siz “Salom, Dunyo!” jumlasini 100 marta yozishingiz zarur bo’lib qoldi.Siz uni qayta qayta yozib chiqgan bo’larmidingiz, yo’q albatta.
👉Batafsil
👨🏫 Mentor: Suxrob Xayitmurodov
#csharp#for#loop#starter
.NET Uzbekistan Community
__________
Telegram | Instagram | Youtube