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Related Questions
- What are the trade-offs between increasing model capacity and training data size when fine-tuning a pre-trained LLM?
- How do computational resources impact the fine-tuning process, and what are the optimal resource allocations for different model architectures?
- What are the implications of overfitting and underfitting on fine-tuned LLM performance, and how can they be mitigated?
- What are the key hyperparameters to tune when fine-tuning a pre-trained LLM, and how do they interact with each other?
- How can transfer learning be leveraged to adapt pre-trained LLMs to new tasks and domains, and what are the key considerations?
- What are the effects of catastrophic forgetting on fine-tuned LLMs, and how can it be addressed through techniques like knowledge distillation?
- What are the strategies for selecting the optimal subset of pre-trained weights to fine-tune when adapting to a new task or domain?
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