The challenges of training language models
Training large language models is a complex and resource-intensive task that poses several challenges. Here are some of the key issues:
- Computational resources: The training of large language models requires substantial computational resources. These models have billions of parameters that need to be updated during training, which involves performing a large amount of computation over an extensive dataset. This computation is usually carried out on high-performance GPUs or tensor processing units (TPUs), and the costs associated can be prohibitive.
- Memory limitations: As the size of the model increases, the amount of memory required to store the model parameters, intermediate activations, and gradients during training also increases. This can lead to memory issues on even the most advanced hardware. Techniques such as model parallelism, gradient checkpointing, and offloading can be used to mitigate these issues, but they add complexity...
