Thank you for open sourcing these models!

I mentioned that the sizes of the models are relatively small (13B max). Is it an inherent limitation, or training a bigger model is possible, just has not been done in this exercise?

Someone else can answer this better than I, so I'll probably end up deleting this in an hour or two. But I think the purpose of this research was not to create an excellent GPT model. I believe it was to explore the scaling effects on Cerebras hardware and determine a helpful framework for compute-optimal training regimes so that customers who might use Cerebras hardware can be confident that:

1) Standard AI/ML scaling assumptions still apply on this hardware.

2) They have a starting point for hyper-parameter estimation and can get better results sooner.

> But I think the purpose of this research was not to create an excellent GPT model.

Yes, understood. I feel that this phrase is a response to the other commenter that suggested that Cerebras should release a ChatGPT-competitive model. I don't think it's easy and I don't think it's a focus for a hardware maker, such as Cerebras.

> I believe it was to explore the scaling effects on Cerebras hardware and determine a helpful framework for compute-optimal training regimes so that customers who might use Cerebras hardware can be confident that:

> 1) Standard AI/ML scaling assumptions still apply on this hardware.

This is my point. Is it possible to train a 100B model on Cerebras hardware? 500B? In this respect, the quality is secondary to the capability for the purpose of demonstration of capabilities.

> Maximal Update Parameterization (μP)

The use of μ (mu) as a sort of… pun acronym thing is pretty clever, nice one.

Thanks for publishing this. I quickly skimmed the paper, I saw the impressive linear scaling as you scaled to 16 nodes. How long did it take to train the various models in wall clock time?