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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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Crypto M - Crypto News

@CryptoM · Post #65305 · 04/12/2026, 06:10 PM

🚀 CMDSS Employee Accused of Misappropriating $46 Million in Seized Digital Assets A federal indictment has been filed against John Daghita, an employee of CMDSS, accusing him of transferring approximately $46 million in seized digital assets from U.S. Marshals Service (USMS) wallets to wallets under his control. According to NS3.AI, Daghita was apprehended by French Gendarmerie officers on March 4 in Saint Martin. Prosecutors allege that these unauthorized transfers took place in December and January. The case has reignited concerns previously raised by the Department of Justice (DOJ) inspector general and industry participants regarding the vulnerabilities in the USMS's crypto custody controls, procurement processes, and audit systems. These issues highlight the need for improved security measures and oversight in handling digital assets. #CMDSS#JohnDaghita#USMS#digitalassets#crypto#seizedassets#fraud#misappropriation#federalindictment#FrenchGendarmerie#NS3AI#DOJ#cryptosecurity#vulnerabilities#audit#procurement#oversight