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:
- 5933b0572a958ad2f733e2a4cedf21ce7570cde8b9c8624a31d76a4069f730bf
- Size of remote file:
- 437 kB
- SHA256:
- b40d9065de51d893996ae8a1c170a86f585de5e71135964568c4c73e7afd6ce0
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