Instructions to use Atul8827/vilt_finetuned_200 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Atul8827/vilt_finetuned_200 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="Atul8827/vilt_finetuned_200")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering processor = AutoProcessor.from_pretrained("Atul8827/vilt_finetuned_200") model = AutoModelForVisualQuestionAnswering.from_pretrained("Atul8827/vilt_finetuned_200", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Atul8827/vilt_finetuned_200: direct link, hf CLI and curl.
- Browser
- Download file 4.79 kB
-
https://huggingface.co/Atul8827/vilt_finetuned_200/resolve/b1696d1cbc57147b2582ca8628268538c4caf807/training_args.bin
- Command line
-
hf download hf://Atul8827/vilt_finetuned_200@b1696d1cbc57147b2582ca8628268538c4caf807/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Atul8827/vilt_finetuned_200/resolve/b1696d1cbc57147b2582ca8628268538c4caf807/training_args.bin
4.79 kB
- Xet hash:
- 6bf78f42d6378676e6b8ba4e7bc4c78a553107c5a31f0a462e8452f0cb1712fb
- Size of remote file:
- 4.79 kB
- SHA256:
- 32c9cc309bdca2d8c1e37df598753fed24f4a67c561c77f85e32b12664bbc0a5
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