Instructions to use Farideh/dataset_model3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Farideh/dataset_model3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Farideh/dataset_model3") 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("Farideh/dataset_model3") model = AutoModelForImageClassification.from_pretrained("Farideh/dataset_model3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Farideh/dataset_model3: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/Farideh/dataset_model3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Farideh/dataset_model3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Farideh/dataset_model3/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 1e808ecff16b3b94843415f28c0e7bb5d1012ffcf708395ce2ecbfde298b62d3
- Size of remote file:
- 343 MB
- SHA256:
- bb3f38403e947b5c4aacf658eb8a20974f035c7566f83e16d18ac9f923489e96
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.