Instructions to use hf-internal-testing/tiny-random-UdopModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-UdopModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-UdopModel")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-UdopModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-UdopModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5c29fb106d5ccc6ff0500a2508f5407ef2928e3f0bcd84bf7dc7fce9a0eeeb80
- Size of remote file:
- 4.97 MB
- SHA256:
- d6855c6f9293a5aa76de12e9f3d563b57360d61cc9992bb9bcd300e4b7a8c2bc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.