Instructions to use TGrote11/Math_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TGrote11/Math_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="TGrote11/Math_Classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import CNN model = CNN.from_pretrained("TGrote11/Math_Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from TGrote11/Math_Classification: direct link, hf CLI and curl.
- Browser
- Download file 29.2 MB
-
https://huggingface.co/TGrote11/Math_Classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://TGrote11/Math_Classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/TGrote11/Math_Classification/resolve/main/pytorch_model.bin
29.2 MB
- Xet hash:
- 88954ef62313642e6eee6443d066347e89b51917ed558a11489c9f37bf383884
- Size of remote file:
- 29.2 MB
- SHA256:
- d439b9ca0fa069596da0bcf5e5a2d43126f579e0528f767f0e20be493a01abd2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.