[Docs] Readme in longeval #389
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@tonysy LGTM |
kennymckormick
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gaotongxiao
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docs/en/advanced_guides/longeval.md
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| Though RoPE and other alternatives to the original sinusoidal position method(like T5 bias) have improved extrapolation, they are considerably slower than the sinusoidal approach and use extra memory and parameter. Therefore, Attention with Linear Biases (ALiBi) is introduced to facilitate efficient extrapolation. | ||
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| For an input subsequence of length L, the attention sublayer computes the attention scores for the ith query $q\_{i} \\in R^{1\\times d}, (1\\leq i\\leq L)$ in each head, given the first i keys $K \\in R^{i\\times d}$, where d is the head dimension. | ||
| $$softmax(q\_{i}K^{T})$$ |
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gaotongxiao
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* [Docs] Readme in longeval * [Docs] Readme in longeval * [Docs] Readme in longeval * [Docs] Readme in longeval * [Docs] Readme in longeval * [Docs] Readme in longeval * [Docs] Readme in longeval
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* [Docs] Readme in longeval * [Docs] Readme in longeval * [Docs] Readme in longeval * [Docs] Readme in longeval * [Docs] Readme in longeval * [Docs] Readme in longeval * [Docs] Readme in longeval
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