Instructions to use macroadster/starlight with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use macroadster/starlight with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="macroadster/starlight") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("macroadster/starlight", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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# Model Card: Starlight Unified Model 2025
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## Model Overview
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---
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license: apache-2.0
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tags:
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- steganography
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- image-classification
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- onnx
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- computer-vision
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library_name: transformers
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pipeline_tag: image-classification
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datasets:
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- custom
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metrics:
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- accuracy
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- f1
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- auc
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---
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# Model Card: Starlight Unified Model 2025
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## Model Overview
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