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)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/RADIO", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Fix bug in make_preprocessor_external (#3)
Browse files- Fix bug in make_preprocessor_external (95f4b113ba701856b055ea95e9b432018813bd36)
- hf_model.py +1 -0
hf_model.py
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@@ -16,6 +16,7 @@ from typing import Callable, Optional, List, Union
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from timm.models import VisionTransformer
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import torch
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from transformers import PretrainedConfig, PreTrainedModel
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from timm.models import VisionTransformer
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import torch
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from torch import nn
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from transformers import PretrainedConfig, PreTrainedModel
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