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Related Questions
- What are some techniques for pruning neural networks to reduce model size while preserving performance in LLMs?
- How can knowledge distillation be used to transfer knowledge from a large teacher model to a smaller student model?
- What are some strategies for using quantization to reduce the precision of model weights and activations, resulting in smaller model sizes?
- What is the concept of 'model compression' and how can it be applied to LLMs to reduce model size without sacrificing performance?
- How can attention mechanisms be optimized to reduce the computational cost of LLMs while maintaining performance?
- What are some techniques for using transfer learning to adapt pre-trained LLMs to new tasks or domains, potentially reducing the need for large models?
- How can model parallelization be used to distribute the computation of LLMs across multiple GPUs or machines, potentially reducing the need for large models?
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