Instructions to use Enthusiastic/Stars with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Enthusiastic/Stars with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Enthusiastic/Stars") 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("Enthusiastic/Stars") model = AutoModelForImageClassification.from_pretrained("Enthusiastic/Stars", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Enthusiastic/Stars: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/Enthusiastic/Stars/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Enthusiastic/Stars/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Enthusiastic/Stars/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- e8d045f0d9917304da4441a69fb8ac90d9e3eec44a2f67c76987048cfebee901
- Size of remote file:
- 343 MB
- SHA256:
- c5cd0d91cf627ddb47e3f8b77ba38233650d4b5a69bab99bc2d055a0257af2c1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.