Update all references to canonical models (#29001)
* Script & Manual edition * Update
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@@ -42,7 +42,7 @@ You can control the maximum size before sharding with the `max_shard_size` param
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```py
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from transformers import AutoModel
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model = AutoModel.from_pretrained("bert-base-cased")
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model = AutoModel.from_pretrained("google-bert/bert-base-cased")
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```
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If you save it using [`~PreTrainedModel.save_pretrained`], you will get a new folder with two files: the config of the model and its weights:
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