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Uncover the hidden truth behind softmax functions with @jedmaczan 🔢 👀 Watch the full recording to dive into the computational insights: Thanks to our ML Math program leads @KatrinaL2899 and @neel_ghoshal for hosting this session!
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literally was having a discussing about selective memory allocation today, in terms of kv cache though. softmax attention query dependent retrieval cant be replicated by a fixed linear map because different queries need to extract different information. raven's solution is to keep the softmax readout but over a compressed set of M slots rather than N tokens.
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This is the best way to learn how LLMs work.Interactive. 3D. Step-by-step. Covers: → Embedding → Layer Norm → Self-Attention → MLP → Transformer layers → Softmax → Output Stop reading papers. Start seeing.Link in comments.Save this immediately.
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