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Related Questions
- What are the primary differences between pre-training and fine-tuning in the context of large language models?
- How does model size impact the trade-offs between pre-training and fine-tuning?
- What are the typical computational resources required for pre-training and fine-tuning large language models?
- How does training time vary between pre-training and fine-tuning, and what are the key factors that influence this difference?
- Can you provide examples of scenarios where pre-training and fine-tuning are used separately or together?
- How do the trade-offs between pre-training and fine-tuning impact the overall performance of a large language model?
- Are there any best practices or guidelines for determining when to use pre-training versus fine-tuning in a given project?
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