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Related Questions
- How does the self-attention mechanism in Llama's architecture handle long-range dependencies in contextual understanding?
- What are the limitations of Llama's architecture in capturing long-range dependencies and how do they impact overall performance?
- Can you explain how the use of multi-head attention in Llama's architecture affects its ability to capture long-range dependencies?
- How does the size of the input representation in Llama's architecture impact its ability to capture long-range dependencies?
- What are the implications of Llama's architecture for its ability to handle nested long-range dependencies?
- How does the use of layers in Llama's architecture impact its ability to capture long-range dependencies?
- Can you describe the trade-offs between attention-based and recurrent-based methods for handling long-range dependencies in Llama's architecture?
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