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:
# 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
- Xet hash:
- e29af0cd70def0db0afed77ef0a0f51e6a8104ed5e1ab4256024b2e7f2fee696
- Size of remote file:
- 33 MB
- SHA256:
- 6eaf2e2414fa09845f9619ba7d226fbe228f22c2a66638017471244044a90386
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.