Instructions to use SixAILab/nepa-large-patch14-224-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SixAILab/nepa-large-patch14-224-sft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SixAILab/nepa-large-patch14-224-sft") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import ViTNepaForImageClassification model = ViTNepaForImageClassification.from_pretrained("SixAILab/nepa-large-patch14-224-sft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# NEPA: Next-Embedding Prediction Makes Strong Vision Learners
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[](https://arxiv.org/abs/2512.16922)
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[](https://sihanxu.
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[](https://huggingface.co/collections/SixAILab/nepa)
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This is a PyTorch/GPU re-implementation of Next-Embedding Prediction Makes Strong Vision Learners.
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# NEPA: Next-Embedding Prediction Makes Strong Vision Learners
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[](https://arxiv.org/abs/2512.16922)
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[](https://sihanxu.me/nepa)
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[](https://github.com/SihanXU/nepa)
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This is a PyTorch/GPU re-implementation of Next-Embedding Prediction Makes Strong Vision Learners.
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