Instructions to use MariaK/vilt_finetuned_200 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MariaK/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="MariaK/vilt_finetuned_200")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering processor = AutoProcessor.from_pretrained("MariaK/vilt_finetuned_200") model = AutoModelForVisualQuestionAnswering.from_pretrained("MariaK/vilt_finetuned_200", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from MariaK/vilt_finetuned_200: direct link, hf CLI and curl.
- Browser
- Download file 3.96 kB
-
https://huggingface.co/MariaK/vilt_finetuned_200/resolve/main/training_args.bin
- Command line
-
hf download hf://MariaK/vilt_finetuned_200/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/MariaK/vilt_finetuned_200/resolve/main/training_args.bin
3.96 kB
- Xet hash:
- aefd3f23fed92088066f2274b20a09b3ee26ce5b61644e8077e8f1590c14f47e
- Size of remote file:
- 3.96 kB
- SHA256:
- 277146f937dd54698843246a5813cf30032f30768ba78774b63e426d583ea560
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