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Source channel @githubtrending · Post #15141 · Sep 13

#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

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@githubtrending · Post #15257 · 11/01/2025, 01:00 PM

#python#docker#docker_compose#music#pdm#python#vue#xiaoai#xiaoai_speaker#xiaomi#xiaomusic XiaoMusic lets you play any music on your XiaoAI speaker using simple voice commands. You can install it easily with Docker or pip, then connect it to your Xiaomi account through a web interface. It downloads songs automatically and supports many formats. This means you can enjoy unlimited music, create playlists, and control playback hands-free, making your speaker much more powerful and flexible. https://github.com/hanxi/xiaomusic

Ultimora.net - POLITICS

@UltimoraPOlitics · Post #39184 · 05/10/2022, 04:45 PM

#Sondaggi#Moldavia Sondaggio di IDIS-CBS Research-IPRI: #PAS|EPP: 40% (+4) #PSRM|Sinistra russofila: 31% #PȘ|ECR: 11% (-2) #PCRM|LEFT: 7% #MAN|Sinistra: 3% (+1) #DA|EPP: 2% #PN|Grande tenda euroscettica russofila: 2% (-1) #PDM|S&D: 1,5% (+0,5) #AUR|Destra romena: 1% #PS|Anti-corruzione: 1% #PL|RE: 0,3% #PUN|Centro-destra unionista: 0,1% Data rilevazione: 27 aprile-6 maggio +/-: 25 gennaio-5 febbraio Intervistati: 1055 @UltimoraPolitics