Instructions to use hf-internal-testing/tiny-random-UMT5Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-UMT5Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-UMT5Model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-UMT5Model") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-UMT5Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 581508f527f2fd9be86157780922c88b866b5c8a22551628cc101e28184ce5db
- Size of remote file:
- 33.2 MB
- SHA256:
- 8ef8383c73aa0d9959c372a679f5a175ff1198e5a739a8f8550350ccc6b7eb4f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.