vectors_file = np.load('vectors.npy')
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While attention scores are learned indices into the rows of the residual stream, subspace scores are learned “coefficients” that provide a soft index into the “column dimension” of the residual stream. The model is able to do this because the W_QK and W_OV matrices are low-rank: d_head is conventionally much smaller than d_model. This allows for low-dimensional subspaces to be used for different purposes. Each component that reads from the residual stream learns to read from a distinct linear combination of subspaces.。关于这个话题,Line下载提供了深入分析
cumulative weight += weights[i]
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