Instructions to use smyu/customFineTunedAutoGrader with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smyu/customFineTunedAutoGrader with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="smyu/customFineTunedAutoGrader") 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("smyu/customFineTunedAutoGrader") model = AutoModelForImageClassification.from_pretrained("smyu/customFineTunedAutoGrader", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from smyu/customFineTunedAutoGrader: direct link, hf CLI and curl.
- Browser
- Download file 687 MB
-
https://huggingface.co/smyu/customFineTunedAutoGrader/resolve/refs%2Fpr%2F1/optimizer.pt
- Command line
-
hf download hf://smyu/customFineTunedAutoGrader@refs/pr/1/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/smyu/customFineTunedAutoGrader/resolve/refs%2Fpr%2F1/optimizer.pt
687 MB
- Xet hash:
- 25f54addc00e468c0ba80d67f4d482be63844943bdba91adb9706d7c63879ad8
- Size of remote file:
- 687 MB
- SHA256:
- d82abf7a2d71346cb612147f70e5ce2a52077c8b25ce2fd916a03f850465e346
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