Instructions to use tcvrishank/fun with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tcvrishank/fun with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tcvrishank/fun") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("tcvrishank/fun") model = AutoModelForImageClassification.from_pretrained("tcvrishank/fun", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2a99d7fbdd401a91ae5edc496ad05b9a9f888e4b3a4849a193fa07cd31d398f7
- Size of remote file:
- 3.45 kB
- SHA256:
- ae80b08aa519ddc6d50dfa3410e71c93ff8c2d049887af373d5f98eee7e8a604
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.