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 4.16 kB
-
https://huggingface.co/Atul8827/vilt_finetuned_200/resolve/34af9cd92c9e76cc5002d1f8269f83bcaed8eff5/training_args.bin
- Command line
-
hf download hf://Atul8827/vilt_finetuned_200@34af9cd92c9e76cc5002d1f8269f83bcaed8eff5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Atul8827/vilt_finetuned_200/resolve/34af9cd92c9e76cc5002d1f8269f83bcaed8eff5/training_args.bin
4.16 kB
- Xet hash:
- 66ffa331e1cf5e0e33d9ecf4df7e417be4b63171db375b15fd256e8d1e84f40a
- Size of remote file:
- 4.16 kB
- SHA256:
- bcae464a8901a1634ba8e7346d41abd55d313e892acb44511ddb04e050233444
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