Instructions to use Eveready/tmp_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Eveready/tmp_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Eveready/tmp_trainer", device_map="auto") 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("Eveready/tmp_trainer") model = AutoModelForImageClassification.from_pretrained("Eveready/tmp_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b701c5b981c0e8955f4d41d4302602e232bf1a4b6acfdd8da2d9695cf61d7675
- Size of remote file:
- 9.09 MB
- SHA256:
- 163623f6aba336353e4789d7798d2f2852c6861d43545beaf50eaa2cb4f865a7
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