How does rotary positional embedding improve generative model performance

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Can i know How does rotary positional embedding improve generative model performance?
1 day ago in Generative AI by Ashutosh
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You can use Rotary Positional Embedding (RoPE) to encode relative positions directly in attention scores, improving generalization to longer sequences.

Here is the code snippet below:

In the above code, we are using the following key points:

  • Frequency-based angle generation for encoding relative positions

  • Element-wise rotation of input embeddings using sine and cosine

  • Applied only to a specific portion of the embedding (rotary dimension)

Hence, rotary embeddings boost generative model robustness on longer context inputs.
answered 20 hours ago by andrew

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