Fix typo in MoE note. #27
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jacobaustin123
approved these changes
Feb 7, 2025
transformers.md
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| @@ -251,9 +251,9 @@ So the takeaway is that **dot-product attention FLOPs only become dominant durin | |||
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| ### Sparsity and Mixture-of-Experts | |||
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| We'd be remiss not to briefly discuss Mixture of Experts (MoE) models<d-cite key="moe"></d-cite>, which replace the single dense MLP blocks in a standard Transformer with a set of independent MLPs that can be dynamically routed between. To a first approximation, **an MoE is a dense model with E MLP blocks per layer**, instead of just one. Each token activates k of these experts, typically k=2. This increases the parameter count by O(E), while keeping the total number of activated parameters roughly the same as the dense model. | |||
| We'd be remiss not to briefly discuss Mixture of Experts (MoE) models<d-cite key="moe"></d-cite>, which replace the single dense MLP blocks in a standard Transformer with a set of independent MLPs that can be dynamically routed between. To a first approximation, **an MoE is a dense model with E MLP blocks per layer**, instead of just one. Each token activates $k$ of these experts, typically $k=2$. This increases the parameter count by $O(E)$, while multipling the total number of activated parameters per inference by $k$, compared with the dense version. | |||
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also "activated parameters per token"
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Also TeX'd some variables.