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Use weights_only for loading #540

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Use weights_only for loading #540

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2 changes: 1 addition & 1 deletion sample.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,7 +35,7 @@
if init_from == 'resume':
# init from a model saved in a specific directory
ckpt_path = os.path.join(out_dir, 'ckpt.pt')
checkpoint = torch.load(ckpt_path, map_location=device)
checkpoint = torch.load(ckpt_path, map_location=device, weights_only=True)
gptconf = GPTConfig(**checkpoint['model_args'])
model = GPT(gptconf)
state_dict = checkpoint['model']
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2 changes: 1 addition & 1 deletion train.py
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Expand Up @@ -159,7 +159,7 @@ def get_batch(split):
print(f"Resuming training from {out_dir}")
# resume training from a checkpoint.
ckpt_path = os.path.join(out_dir, 'ckpt.pt')
checkpoint = torch.load(ckpt_path, map_location=device)
checkpoint = torch.load(ckpt_path, map_location=device, weights_only=True)
checkpoint_model_args = checkpoint['model_args']
# force these config attributes to be equal otherwise we can't even resume training
# the rest of the attributes (e.g. dropout) can stay as desired from command line
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