You can combine data parallelism with model parallelism to train LLMs."Is this true or false?1 pointTrueFalse

Answer

This statement is True.

Explanation: In training large language models (LLMs), data parallelism and model parallelism can be effectively combined to optimize resource usage and improve training efficiency.

• Data Parallelism involves distributing the training data across multiple processors, allowing each to compute gradients independently based on a subset of the data. This is particularly beneficial when the data set is large, as it helps speed up training by processing more samples simultaneously.

• Model Parallelism, on the other hand, splits the model itself across different processors. This is necessary when the model is too large to fit into the memory of a single device. By distributing the different layers or parts of the model across different devices, the training can utilize the combined memory and computational power of those devices.

Combining these two methods allows for efficient utilization of computational resources, as data parallelism can handle large datasets while model parallelism can handle large, complex model architectures, making it feasible to train extremely large language models effectively.