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Related Questions
- What are the primary challenges in training large language models and how do model-parallelization and data-parallelization address them?
- How does model-parallelization work and what are its advantages and disadvantages in the context of large language model training?
- Can you explain the concept of data-parallelization and its role in distributed training of large language models?
- What are some common techniques used for model-parallelization and data-parallelization in large language model training?
- How do model-parallelization and data-parallelization affect the training time and computational resources required for large language model training?
- Are there any trade-offs between model-parallelization and data-parallelization, and how do they impact the overall performance of the model?
- Can you discuss some real-world applications and use cases where model-parallelization and data-parallelization have been successfully applied to large language model training?
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