#python#large_language_models#machine_learning_systems#natural_language_processing
Flash Linear Attention (FLA) is a fast, memory-efficient library for advanced linear attention models used in transformers, written in PyTorch and Triton, and compatible with NVIDIA, AMD, and Intel GPUs. It offers many state-of-the-art linear attention models and fused modules that speed up training and reduce memory use. You can easily replace standard attention layers in your models with FLA’s efficient versions, improving training and inference speed, especially for long sequences. FLA supports hybrid models mixing linear and standard attention, and integrates with Hugging Face Transformers for easy use and evaluation. This helps you train and run large language models faster and with less memory, making your AI projects more efficient and scalable.
https://github.com/fla-org/flash-linear-attention
#result
Ko'pgina õquvchilar aniq bir sohani tanlay olisha olmaydi. Tanlashganda ham juda kõp ikkilanishlar bõladi. Ular bilan õzlari xohlagan yõnalishni tanlab, shu yõnalish bo'yicha aniq maqsad sari harakat qilish esa mashaqqatli ish hisoblanadi.
Õquvchilarning universitetlarga ketma-ket kirayotganligi haqidagi xabarlar kelganida esa ushbu mashaqqatlar esdan chiqib, yuzda ajib tabassum hosil bõladi.
Ilm olishda va ilm ulashishda Allohning õzi bardavom qilsin 🤲
Yana ham kõproq natijalarni bilish uchun bizni instagram tarmog'ida kuzatib boring.
#competition#result
👍17-noyabr kuni Oʻzbekiston stol tennisi federatsiyasi bilan hamkorlikda tashkil etilgan “IT Park Table Tennis Cup-2024” turniri oʻz yakuniga yetdi!
🏓 Turnirda 42 ta jamoa ishtirok etdi va kuchli bellashuvlar oʻtkazildi.
🥇 “PAYNET”
🥈 “BRB TECH”
🥉 “UZINFOCOM”, “YUSOLVE”
🏆 Gʻoliblarni samimiy tabriklaymiz! Musobaqada qatnashgan barcha jamoalarga minnatdorchilik bildiramiz. Bu turnir nafaqat sport, balki doʻstlik va hamjihatlikning haqiqiy bayrami boʻldi!
_______
👍The IT Park Table Tennis Cup 2024, organized in collaboration with the Table Tennis Federation of Uzbekistan, successfully concluded on November 17!
A total of 42 teams participated, demonstrating exceptional skill and commitment to victory.
🥇 “PAYNET”
🥈 “BRB TECH”
🥉 “UZINFOCOM”, “YUSOLVE”
🏆 Congratulations to the winners! We extend our gratitude to all teams for their involvement and outstanding performances. This tournament was a true celebration of sportsmanship, friendship, and unity.
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