[docs] Update offline mode docs (#26478)

update
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Steven Liu
2023-09-29 00:42:21 -07:00
committed by GitHub
parent 211f93aab9
commit 7bb1c0c147

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@@ -169,28 +169,28 @@ Pretrained models are downloaded and locally cached at: `~/.cache/huggingface/hu
## Offline mode ## Offline mode
🤗 Transformers is able to run in a firewalled or offline environment by only using local files. Set the environment variable `TRANSFORMERS_OFFLINE=1` to enable this behavior. Run 🤗 Transformers in a firewalled or offline environment with locally cached files by setting the environment variable `TRANSFORMERS_OFFLINE=1`.
<Tip> <Tip>
Add [🤗 Datasets](https://huggingface.co/docs/datasets/) to your offline training workflow by setting the environment variable `HF_DATASETS_OFFLINE=1`. Add [🤗 Datasets](https://huggingface.co/docs/datasets/) to your offline training workflow with the environment variable `HF_DATASETS_OFFLINE=1`.
</Tip> </Tip>
For example, you would typically run a program on a normal network firewalled to external instances with the following command:
```bash
python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en ...
```
Run this same program in an offline instance with:
```bash ```bash
HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \ HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \
python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en ... python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en ...
``` ```
The script should now run without hanging or waiting to timeout because it knows it should only look for local files. This script should run without hanging or waiting to timeout because it won't attempt to download the model from the Hub.
You can also bypass loading a model from the Hub from each [`~PreTrainedModel.from_pretrained`] call with the [`local_files_only`] parameter. When set to `True`, only local files are loaded:
```py
from transformers import T5Model
model = T5Model.from_pretrained("./path/to/local/directory", local_files_only=True)
```
### Fetch models and tokenizers to use offline ### Fetch models and tokenizers to use offline