Instructions to use hf-internal-testing/tiny-random-PvtV2Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-PvtV2Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-PvtV2Model")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-PvtV2Model") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-PvtV2Model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3df6fce24ebbe0b53d2c7a3fc2fb9a583aab8551c13f731e20419dda1d9438a6
- Size of remote file:
- 3.11 MB
- SHA256:
- 415832d33e3c303975b5c8449983b5eae8ad51b7bfee2abfd8a48f4d498b694f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.