Visual Question Answering
Transformers
PyTorch
English
vision-encoder-decoder
image-text-to-text
ui refexp
Instructions to use ivelin/donut-refexp-combined-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ivelin/donut-refexp-combined-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="ivelin/donut-refexp-combined-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("ivelin/donut-refexp-combined-v1") model = AutoModelForMultimodalLM.from_pretrained("ivelin/donut-refexp-combined-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload model
Browse files- config.json +1 -1
- pytorch_model.bin +1 -1
config.json
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"_name_or_path": "ivelin/donut-refexp-combined-v1",
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"_commit_hash": "da953c0146f3a5f7bc7af510487f772180a5019c",
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"_name_or_path": "ivelin/donut-refexp-combined-v1",
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pytorch_model.bin
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