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Related Questions
- How does knowledge distillation impact the performance and accuracy of a large language model?
- What are the trade-offs between model size and performance when applying knowledge distillation in LLM fine-tuning?
- Can you explain the concept of 'dark knowledge' in the context of knowledge distillation and its implications for model interpretability?
- In what situations is knowledge distillation more effective than other fine-tuning methods, and why?
- How does knowledge distillation affect the generalization performance of a pre-trained language model?
- What are the potential risks of over-distillation, and how can they be mitigated?
- Can you discuss the relationship between knowledge distillation and model capacity, and how they impact the final model performance?
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