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Related Questions
- What are the primary factors that contribute to the increased computational requirements of large language models compared to traditional machine learning models?
- How do the dimensions of the input and output spaces affect the computational resources required for large language models?
- What are some strategies for reducing the computational requirements of large language models, such as model pruning, knowledge distillation, or quantization?
- Can you explain the role of embedding layers and their impact on the computational resources required for large language models?
- How do the requirements for memory and parallelization differ between large language models and traditional machine learning models?
- What are the implications of the increased computational requirements of large language models on deployment and inference in real-world applications?
- Can you discuss the trade-offs between model size, accuracy, and computational efficiency in large language models?
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