Instructions to use vikp/donut-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vikp/donut-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="vikp/donut-encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("vikp/donut-encoder") model = AutoModel.from_pretrained("vikp/donut-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from vikp/donut-encoder: direct link, hf CLI and curl.
- Browser
- Download file 298 MB
-
https://huggingface.co/vikp/donut-encoder/resolve/main/model.safetensors
- Command line
-
hf download hf://vikp/donut-encoder/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/vikp/donut-encoder/resolve/main/model.safetensors
298 MB
- Xet hash:
- 462739a67e9df18457b30bc89496ac0af4ecad2dc8fc1e704e272fa6539b3979
- Size of remote file:
- 298 MB
- SHA256:
- ad687912b504b57f22d53ccac97088326f06ec52b4044b19bb4f447b49096cda
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