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Related Questions
- What are the trade-offs between increasing the number of attention heads and model capacity?
- How does the attention head size affect the model's ability to capture long-range dependencies?
- Can a smaller attention head size lead to better performance on specific tasks, and if so, what are the potential limitations?
- How does the choice of attention head size impact the computational requirements of the model, particularly in terms of memory and inference time?
- Can you explain the concept of 'head size' in the context of transformer models and how it relates to the model's ability to attend to different parts of the input sequence?
- How does the number of attention heads interact with the model's hidden state size and the input sequence length?
- Can you provide an example of a scenario where a larger attention head size might be beneficial, and another scenario where a smaller head size might be more suitable?
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