Instructions to use hf-internal-testing/tiny-random-DistilBertModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-DistilBertModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-DistilBertModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-DistilBertModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-DistilBertModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9f3b6da07039e1c2b4a6aedc828600ce1fbd5ec1a39bf42afa1ec41a56056d90
- Size of remote file:
- 354 kB
- SHA256:
- 1840e234641e2ca03eefbf9267b1b6adf62b62759cb0b070c959336ac26bdedf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.