Instructions to use Docty/solacies with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Docty/solacies with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Docty/solacies") 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("Docty/solacies") model = AutoModelForImageClassification.from_pretrained("Docty/solacies", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Docty/solacies: direct link, hf CLI and curl.
- Browser
- Download file 940 Bytes
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https://huggingface.co/Docty/solacies/resolve/main/README.md
- Command line
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hf download hf://Docty/solacies/README.md
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curl -L -o README.md https://huggingface.co/Docty/solacies/resolve/main/README.md
940 Bytes
| base_model: google/vit-base-patch16-224-in21k | |
| library_name: transformers | |
| license: creativeml-openrail-m | |
| inference: true | |
| tags: | |
| - image-classification | |
| <!-- This model card has been generated automatically according to the information the training script had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # Image Classification | |
| This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the Docty/solaices dataset. | |
| You can find some example images in the following. | |
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| ## Intended uses & limitations | |
| #### How to use | |
| ```python | |
| # TODO: add an example code snippet for running this diffusion pipeline | |
| ``` | |
| #### Limitations and bias | |
| [TODO: provide examples of latent issues and potential remediations] | |
| ## Training details | |
| [TODO: describe the data used to train the model] |