Instructions to use TalentoTechIA/Wilmer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TalentoTechIA/Wilmer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="TalentoTechIA/Wilmer") 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("TalentoTechIA/Wilmer") model = AutoModelForImageClassification.from_pretrained("TalentoTechIA/Wilmer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 26344ff3485f75408f70d27e1ad475a69bc8f483eff48230d9c46ffadb837736
- Size of remote file:
- 343 MB
- SHA256:
- ac993d88ea1a55e6ed72b92ccd0b491bd6a6a208466f611a1a2f8d9d20847314
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