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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 #64776 · 04/09/2026, 10:11 PM

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🚀 CFTC and DOJ Legal Actions Could Influence Future of Prediction Markets The Commodity Futures Trading Commission (CFTC) and the Department of Justice (DOJ) have initiated legal proceedings against the states of Arizona, Connecticut, and Illinois concerning their actions against CFTC-registered prediction markets. According to NS3.AI, a recent 2-1 ruling by the Third Circuit Court upheld an injunction preventing New Jersey from applying its gambling laws to Kalshi, a prediction market platform. The court determined that Kalshi's contracts related to sports events are classified as swaps under the Commodity Exchange Act, thereby falling under the exclusive jurisdiction of the CFTC. This legal dispute has the potential to significantly influence the regulatory framework for decentralized prediction markets and crypto-native derivatives across the United States. #CFTC#DOJ#PredictionMarkets#Kalshi#CommodityExchangeAct#Regulation#CryptoDerivatives#USLaw#LegalActions#DecentralizedFinance

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