Instructions to use Master-AI-Lab/Lumi-Transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Master-AI-Lab/Lumi-Transformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Master-AI-Lab/Lumi-Transformer") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Master-AI-Lab/Lumi-Transformer", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dd8eb59fdd1de44af9877bff92c68af40a2b76143986c1fec90922c4ad23ca4f
- Size of remote file:
- 781 MB
- SHA256:
- 792490d69394275f19d1c9de024d6bd22aa5b88dd693e82564d563d5f5e601f9
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