Instructions to use hf-internal-testing/tiny-random-ConvNextV2Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ConvNextV2Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-internal-testing/tiny-random-ConvNextV2Model")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-ConvNextV2Model") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-ConvNextV2Model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e0150bdde0afa93a29bb02e4a269fb9b631c68c65f20643d60cff2e57e7fe5b4
- Size of remote file:
- 327 kB
- SHA256:
- 7b7a0dcfeb5a5117483a0fb500a9bd298ab5e6b0bc17b3cdb1e59bcb5819b9a0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.