Instructions to use hf-internal-testing/tiny-random-UMT5EncoderModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-UMT5EncoderModel with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-UMT5EncoderModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-UMT5EncoderModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from hf-internal-testing/tiny-random-UMT5EncoderModel: direct link, hf CLI and curl.
- Browser
- Download file 16.8 MB
-
https://huggingface.co/hf-internal-testing/tiny-random-UMT5EncoderModel/resolve/5cd9b6ecac7fa84b619ff53142960ef131288f73/tokenizer.json
- Command line
-
hf download hf://hf-internal-testing/tiny-random-UMT5EncoderModel@5cd9b6ecac7fa84b619ff53142960ef131288f73/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/hf-internal-testing/tiny-random-UMT5EncoderModel/resolve/5cd9b6ecac7fa84b619ff53142960ef131288f73/tokenizer.json
16.8 MB
- Xet hash:
- 9ec432bdbf359181405fdcad8c81eb9d73703a3530ab6b9f1a6972244189d435
- Size of remote file:
- 16.8 MB
- SHA256:
- 8c423d8500263e0f8098ca1174fea42064b7b82a87ef4f757c75aab4c8310186
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