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:
- 66a15dd2933e37e23db52504fd00c5491dffc8a6f393aea2a7c9982499ddfd9c
- Size of remote file:
- 33 MB
- SHA256:
- b8d50db1b0cc449c4a673207b9fcbb53390242b86ab0090f8bef0db58d79fd75
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.