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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 52594f3c21d243af941210d89e147b7406a73acc74c7d053c8010c34b3b6ab92
- Size of remote file:
- 437 kB
- SHA256:
- 78617964e43c9acdba6cbac6852d496cd47e7d6acf1c57aac42a9c5674d69270
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.