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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 #15601 · 04/05/2026, 11:30 AM

#yara#awesome_list#blueteam#blueteam_tools#cti#detection#detection_engineering#dfir#hacktools#incident_response#ioc#iocs#ir#ransomware#redteam#rmm#security#siem#soc#threat_hunting#threat_intelligence You can access comprehensive security detection lists and threat hunting resources that help identify malicious activity across your infrastructure. These curated collections include indicators like suspicious file hashes, domain names, IP addresses, and behavioral patterns organized by threat type—from ransomware and phishing to command-and-control servers and vulnerable drivers. By integrating these lists into your security tools like SIEM platforms and endpoint detection systems, you gain immediate visibility into known threats while learning detection methodologies through guides and YARA rules. This accelerates your ability to hunt for compromises, validate security controls, and stay current with emerging attack techniques without building detection logic from scratch. https://github.com/mthcht/awesome-lists