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