Instructions to use Avelardo/my_test_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Avelardo/my_test_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Avelardo/my_test_model") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Avelardo/my_test_model") model = AutoModelForImageClassification.from_pretrained("Avelardo/my_test_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Avelardo/my_test_model: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/Avelardo/my_test_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Avelardo/my_test_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Avelardo/my_test_model/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- a46179ddeb6b9246dd104df87bd67ad4300cdc852b5db792cf213818de66bb85
- Size of remote file:
- 343 MB
- SHA256:
- fea7d1ed07661aab97c3b9f62f3f51deade9ed60b52334198ae6651f593421c1
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