Implementing Linear Scaling by Modifying Embedding Model Input
The linear scaling of position ranges in position interpolation can be practically realized by directly modifying the input to the positional embedding model. This adjusts how the sequence positions are processed without changing the core embedding architecture.
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Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Ch.2 Generative Models - Foundations of Large Language Models
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Implementing Linear Scaling by Modifying Embedding Model Input
A language model was originally developed to process text sequences with a maximum length of 2048 positions. To enable it to handle a longer input sequence of 8192 positions, a technique is applied that linearly scales down the new position indices to fit within the model's original learned range. Given this scenario, what would be the scaled-down position index that corresponds to the token at position 6144 in the new, longer sequence?
Adapting a Language Model for Longer Documents
Calculating Scaled Positional Indices
Learn After
Debugging Context Length Extension
A large language model was originally trained with a maximum context window of 2048 tokens. You are now tasked with enabling it to process a sequence of 4096 tokens using a technique that scales the position indices of the longer sequence to fit within the model's original learned range. How should the position index for the token at position 3072 in the 4096-token sequence be handled before being passed to the embedding layer?
Implementing Position Scaling in a Language Model