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Related Questions
- What are the key differences in model architecture between pre-training and fine-tuning in large language models?
- Can you explain the role of pre-training in initializing the model weights and how it affects fine-tuning?
- How does the model architecture change when transitioning from pre-training to fine-tuning in a transformer-based model?
- What are the implications of pre-training on the model's ability to generalize to new tasks during fine-tuning?
- How does the pre-training objective affect the model's architecture and its performance on downstream tasks?
- Can you discuss the relationship between pre-training and fine-tuning in terms of model capacity and the need for additional parameters?
- What are the architectural differences between pre-trained and fine-tuned models in terms of layer configurations and parameter sizes?
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