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Related Questions
- What are the key considerations when pruning pre-trained models to reduce their size while maintaining performance?
- How can knowledge distillation be used to transfer knowledge from a large pre-trained model to a smaller one?
- What are some techniques for fine-tuning pre-trained models on smaller datasets to adapt them to a specific task?
- What is the difference between model compression and model pruning, and how do they impact model performance?
- How can model parallelization be used to speed up the inference process of large pre-trained models?
- What is the role of quantization in reducing the size of pre-trained models without significant loss in accuracy?
- What are some strategies for adapting pre-trained models to new languages or domains, and what are their limitations?
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