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Related Questions
- What is the optimal number of warm-up steps for a large language model in a specific downstream task?
- How does the choice of warm-up steps affect the model's ability to converge to a solution?
- Can you explain the relationship between warm-up steps and model performance in terms of convergence speed and accuracy?
- What happens to the model's performance if the number of warm-up steps is too low or too high?
- How does the type of warm-up steps (e.g., random, sequence, or task-specific) impact the model's performance?
- Can you provide examples of how different warm-up step strategies have been used in various NLP tasks, such as language translation or text classification?
- What are the computational costs associated with increasing the number of warm-up steps, and how do they impact the overall training process?
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