Instructions to use dacorvo/mnist-mlp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dacorvo/mnist-mlp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dacorvo/mnist-mlp", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dacorvo/mnist-mlp", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c28514f911cf5e0939304feb5283bf24cc627b72812407e03e9c3572b5d0c45b
- Size of remote file:
- 1.08 MB
- SHA256:
- 26300c9e4b676ba94c7773ee82de3bb2831b9fd45cc1202b65bc94a6e4db4fa5
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