Instructions to use bramschipper/test_pytorch_benchmark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bramschipper/test_pytorch_benchmark with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="bramschipper/test_pytorch_benchmark") 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("bramschipper/test_pytorch_benchmark") model = AutoModelForImageClassification.from_pretrained("bramschipper/test_pytorch_benchmark", device_map="auto") - Notebooks
- Google Colab
- Kaggle