Instructions to use schubertcarvalho/vilt_finetuned_200 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use schubertcarvalho/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="schubertcarvalho/vilt_finetuned_200")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering processor = AutoProcessor.from_pretrained("schubertcarvalho/vilt_finetuned_200") model = AutoModelForVisualQuestionAnswering.from_pretrained("schubertcarvalho/vilt_finetuned_200", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from schubertcarvalho/vilt_finetuned_200: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/schubertcarvalho/vilt_finetuned_200/resolve/main/training_args.bin
- Command line
-
hf download hf://schubertcarvalho/vilt_finetuned_200/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/schubertcarvalho/vilt_finetuned_200/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- e4c2eabff1abe55b7410a521d52fb685332c63c23213dc696384402b5c22b0c3
- Size of remote file:
- 4.54 kB
- SHA256:
- 7606a6cc9236a429e9723935614a1b1a0f451759894ce5c69c2fb0e51459bb4d
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