Hey guys,
I’m looking for some guidance on a particular issue.
I’m not using HF’s trainer, but my own pytorch implementation, the workflow is like:
best_score = 0
for epoch in epochs:
# Training
# ...
if metric > best_score:
best_model = deepcopy(model.state_dict)
And then, to evaluate the best model:
model.load_state_dict(best_model)
This approach works perfectly when model
is defined as AutoModelForSequenceClassification.from_pretrained(modelcp, labels).
However, when I define model
as AutoModelForSequenceClassification.from_pretrained(modelcp, labels, load_in_8bit=True),
I encounter the following error:
RuntimeError: Loading a quantized checkpoint into non-quantized Linear8bitLt is not supported. Please call module.cuda() before module.load_state_dict()
Anyone could have any insights on how to resolve this issue? Cheers!