Comments (2)
What is the base model you use? Can you provide some information about the training log?
The NaN issue is probably caused by dividing by 0 in -torch.sum(x * mask.view(-1), dim=0) / torch.sum(mask.view(-1), dim=0)
. You can check whether there exists any full-zero row in mask
.
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That was indeed the issue, looks like my tokenizer was truncating the inputs early. Thanks!
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