Instructions to use nvidia/RADIO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/RADIO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="nvidia/RADIO", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/RADIO", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload model
Browse files- hf_model.py +2 -0
hf_model.py
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@@ -45,6 +45,8 @@ class RADIOConfig(PretrainedConfig):
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class RADIOModel(PreTrainedModel):
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"""Pretrained Hugging Face model for RADIO."""
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def __init__(self, config):
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super().__init__(config)
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class RADIOModel(PreTrainedModel):
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"""Pretrained Hugging Face model for RADIO."""
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config_class = RADIOConfig
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def __init__(self, config):
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super().__init__(config)
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