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Related Questions
- How does pre-training on large datasets impact the performance of large language models on downstream NLP tasks?
- What are the key differences between pre-training and fine-tuning in the context of NLP, and how do they affect model performance?
- Can you explain the concept of 'transfer learning' in the context of Llama and Qwen, and how it relates to pre-training and fine-tuning?
- How does the size of the pre-training dataset affect the performance of Llama and Qwen on NLP tasks such as language translation and text classification?
- What are some common challenges associated with fine-tuning large language models like Llama and Qwen, and how can they be addressed?
- Can you discuss the role of hyperparameters in fine-tuning Llama and Qwen for NLP tasks, and how they can be optimized for better performance?
- How do the pre-training and fine-tuning processes affect the interpretability of the models, and what are the implications for understanding the decision-making processes of Llama and Qwen?
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