Instructions to use hf-internal-testing/tiny-random-EfficientFormerForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-EfficientFormerForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-EfficientFormerForImageClassification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-EfficientFormerForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-EfficientFormerForImageClassification: direct link, hf CLI and curl.
- Browser
- Download file 1.84 MB
-
https://huggingface.co/hf-internal-testing/tiny-random-EfficientFormerForImageClassification/resolve/0e510f07c8f2e2052255090c441c7e04bb16b6a1/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-EfficientFormerForImageClassification@0e510f07c8f2e2052255090c441c7e04bb16b6a1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-EfficientFormerForImageClassification/resolve/0e510f07c8f2e2052255090c441c7e04bb16b6a1/pytorch_model.bin
1.84 MB
- Xet hash:
- 277627ce7bca28540b052c8df2ef6a3b3e45010dcb0a9cb05e380f125c156745
- Size of remote file:
- 1.84 MB
- SHA256:
- c0628e281b0a562ee483242a5ae2a872839d011acc123554cbfe0a9282c61fe1
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