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:
- e06581bf0f42f8b8815bb304cdaab8fd0e18b33d9153a78d4ddf7bb768153bf5
- Size of remote file:
- 33 MB
- SHA256:
- 2fd8017bcaf7daa1c5af06ecfbbc76d0519ceb309bdd25511c769c1bc46fa7d4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.