Instructions to use lprat/test_manual_import with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lprat/test_manual_import with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="lprat/test_manual_import") 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("lprat/test_manual_import") model = AutoModelForImageClassification.from_pretrained("lprat/test_manual_import", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from lprat/test_manual_import: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/lprat/test_manual_import/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://lprat/test_manual_import/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/lprat/test_manual_import/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- eb7caf8b10746b129e1174a4c29fc3e4253c31cffb3da85d8033ef3ab56372b8
- Size of remote file:
- 343 MB
- SHA256:
- bd90de3ff75fa960f59ef2c2c663e6c0caba1653a57190bd75a1cb68e010a656
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.