Visual Question Answering
Transformers
Safetensors
Vietnamese
vision-encoder-decoder
image-text-to-text
Instructions to use TeeA/DONUT-ViChart with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TeeA/DONUT-ViChart with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "visual-question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("visual-question-answering", model="TeeA/DONUT-ViChart")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("TeeA/DONUT-ViChart") model = AutoModelForMultimodalLM.from_pretrained("TeeA/DONUT-ViChart", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download ViChart-demo.png from TeeA/DONUT-ViChart: direct link, hf CLI and curl.
- Browser
- Download file 48.2 kB
-
https://huggingface.co/TeeA/DONUT-ViChart/resolve/d0c721bee67647dcdac7e1066a044fa80d019146/ViChart-demo.png
- Command line
-
hf download hf://TeeA/DONUT-ViChart@d0c721bee67647dcdac7e1066a044fa80d019146/ViChart-demo.png
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curl -L -o ViChart-demo.png https://huggingface.co/TeeA/DONUT-ViChart/resolve/d0c721bee67647dcdac7e1066a044fa80d019146/ViChart-demo.png
48.2 kB
