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For folks wondering what Sliding Window Attention is, there's a method for it on Papers with Code Sliding Window Attention (SWA): A local attention pattern that restricts each token to attending only within a fixed-size neighborhood instead of the full sequence. This reduces attention and KV-cache memory for long-context models, while periodic global-attention layers can preserve broader context. Find it here: https://t.co/K1MhZVasL8
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"text": "For folks wondering what Sliding Window Attention is, there's a method for it on Papers with Code\n\nSliding Window Attention (SWA): A local attention pattern that restricts each token to attending only within a fixed-size neighborhood instead of the full sequence. This reduces attention and KV-cache memory for long-context models, while periodic global-attention layers can preserve broader context.\n\nFind it here: https://t.co/K1MhZVasL8",
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"text": "Simple beats complicated:\n\nWe show that switching to a sliding-window attention mask with attention sinks (at no cost) beats linear attention post-training. \n\nHuge thanks to my collaborators @RheaSukthanker, @CameronPashmina, and @Emy_Aze.\n\nPaper: https://t.co/h8DIc223Su",
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