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 pytorch_model.bin from javierrf91/streamlit: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/javierrf91/streamlit/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://javierrf91/streamlit/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/javierrf91/streamlit/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- f3517f554ab7e0f83d6d76c0efa0b2822ee84bdfa081a4b81ab736352b81ac9f
- Size of remote file:
- 343 MB
- SHA256:
- d170d27390f5a34bd1f149e421a95f983555c9d925f854322ef4368a2c37b029
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