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Slim attention: cut your context memory in half without loss of accuracy -- K-cache is all you need for MHA
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Topic 33: Slim Attention, KArAt, XAttention and Multi-Token Attention Explained – What’s Really Changing in Transformers?
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Flash normalization: fast RMSNorm for LLMs
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rasbt/llama-3.2-from-scratch
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