Instructions to use javierrf91/streamlit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use javierrf91/streamlit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="javierrf91/streamlit") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("javierrf91/streamlit") model = AutoModelForImageClassification.from_pretrained("javierrf91/streamlit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from javierrf91/streamlit: direct link, hf CLI and curl.
- Browser
- Download file 3.45 kB
-
https://huggingface.co/javierrf91/streamlit/resolve/main/training_args.bin
- Command line
-
hf download hf://javierrf91/streamlit/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/javierrf91/streamlit/resolve/main/training_args.bin
3.45 kB
- Xet hash:
- bd6bc9e4ff699c916938a47957c437f6a77c6337e1ae67141c0fedd9b3c4d86a
- Size of remote file:
- 3.45 kB
- SHA256:
- ec550fb1ffc43d6f9242cb285687225265d43be19c0a6f238f6966b8f32263c1
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