Instructions to use hf-internal-testing/tiny-random-SEWDModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SEWDModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-SEWDModel")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-SEWDModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-SEWDModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f7cbc7957c38f42e8b1e1971bd083b257be33987744e977ba50701130d25481b
- Size of remote file:
- 287 kB
- SHA256:
- 1403871f8806731573ba9b95c5670a893b84da1a8f4b17041ec8652f2a54f8d7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.