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Related Questions
- What are some techniques for pruning or quantizing large models to reduce their size and computational requirements?
- How can model architecture design influence the trade-off between model size and computational requirements?
- What are some strategies for using knowledge distillation to transfer knowledge from a large teacher model to a smaller student model?
- Can you discuss the impact of hyperparameter tuning on the balance between model size and computational requirements?
- What are some methods for compressing or compressing models using techniques such as Huffman coding or arithmetic coding?
- How can distributed training and parallelization strategies affect the balance between model size and computational requirements?
- What are some strategies for using transfer learning to leverage pre-trained models and reduce the need for large, computationally expensive models?
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