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Related Questions
- What learning rate schedulers are effective for fine-tuning language models?
- Which optimizer types (e.g., Adam, SGD) are commonly used for tuning Llama and Qwen?
- How does the number of epochs, batch size, and hidden state size impact model performance?
- What weight decay values are typically effective for NLP tasks, and how do they vary across different models?
- Are there any popular techniques for learning rate adjustment, such as cosine annealing or cyclic learning rates, that have been shown to be effective for fine-tuning language models?
- Can you discuss the importance of gradient clipping in stabilizing the training of Llama and Qwen, and provide guidance on how to implement it?
- What are the key differences between warm restarts, cosine annealing, and step learning rates, and how do these impact the training of large language models?
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