Instructions to use hf-internal-testing/tiny-random-ConvNextModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ConvNextModel 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-ConvNextModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-ConvNextModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-ConvNextModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-internal-testing/tiny-random-ConvNextModel: direct link, hf CLI and curl.
- Browser
- Download file 320 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-ConvNextModel/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-ConvNextModel@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-ConvNextModel/resolve/refs%2Fpr%2F1/model.safetensors
320 kB
- Xet hash:
- dea6acf1e47a777eaaa1fa80007ee52b573cdd7bc4cdcc62ef3d363dca0776dc
- Size of remote file:
- 320 kB
- SHA256:
- 72fe746a8741d22cb4699df44f68c9cae345c3ada47127d76fedbb8eeebce0a4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.