Instructions to use kimdesok/vilt_finetuned_200 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kimdesok/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="kimdesok/vilt_finetuned_200")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering processor = AutoProcessor.from_pretrained("kimdesok/vilt_finetuned_200") model = AutoModelForVisualQuestionAnswering.from_pretrained("kimdesok/vilt_finetuned_200", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from kimdesok/vilt_finetuned_200: direct link, hf CLI and curl.
- Browser
- Download file 452 MB
-
https://huggingface.co/kimdesok/vilt_finetuned_200/resolve/9659719c419b1a6be30a3083a21f64504596d52e/model.safetensors
- Command line
-
hf download hf://kimdesok/vilt_finetuned_200@9659719c419b1a6be30a3083a21f64504596d52e/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/kimdesok/vilt_finetuned_200/resolve/9659719c419b1a6be30a3083a21f64504596d52e/model.safetensors
452 MB
- Xet hash:
- 0243ce474dcdb97b77efabdf4f3d75af3cd5300c2691f59394ed8eec4320f026
- Size of remote file:
- 452 MB
- SHA256:
- 35aa4f60876664aec8512f2056a2c47e396a5142fabe0be31d57b6fce752fa74
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