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[docs] Increase visibility of torch_dtype="auto" (#35067)

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* auto-dtype

* feedback
This commit is contained in:
Steven Liu
2024-12-04 09:18:44 -08:00
committed by GitHub
parent baa3b22137
commit 1ed1de2fec
11 changed files with 49 additions and 35 deletions
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9
docs/source/en/autoclass_tutorial.md
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@@ -138,12 +138,15 @@ Load a processor with [`AutoProcessor.from_pretrained`]:
<frameworkcontent>
<pt>
The `AutoModelFor` classes let you load a pretrained model for a given task (see [here](model_doc/auto) for a complete list of available tasks). For example, load a model for sequence classification with [`AutoModelForSequenceClassification.from_pretrained`]:
The `AutoModelFor` classes let you load a pretrained model for a given task (see [here](model_doc/auto) for a complete list of available tasks). For example, load a model for sequence classification with [`AutoModelForSequenceClassification.from_pretrained`].
> [!WARNING]
> By default, the weights are loaded in full precision (torch.float32) regardless of the actual data type the weights are stored in such as torch.float16. Set `torch_dtype="auto"` to load the weights in the data type defined in a model's `config.json` file to automatically load the most memory-optimal data type.
```py
>>> from transformers import AutoModelForSequenceClassification
>>> model = AutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
>>> model = AutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased", torch_dtype="auto")
```
Easily reuse the same checkpoint to load an architecture for a different task:
@@ -151,7 +154,7 @@ Easily reuse the same checkpoint to load an architecture for a different task:
```py
>>> from transformers import AutoModelForTokenClassification
>>> model = AutoModelForTokenClassification.from_pretrained("distilbert/distilbert-base-uncased")
>>> model = AutoModelForTokenClassification.from_pretrained("distilbert/distilbert-base-uncased", torch_dtype="auto")
```
<Tip warning={true}>