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:
- 54bd363878852048b4e5bc4d5d29d55cadfa5c59f54f0da781a930d1bc74049a
- Size of remote file:
- 5.37 kB
- SHA256:
- 4df3bfd3890cd052142834bffd1755dde28b659cc07a0baef54188dc46b2bfa6
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