Instructions to use peter881122/freewaynModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use peter881122/freewaynModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="peter881122/freewaynModel") 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("peter881122/freewaynModel") model = AutoModelForImageClassification.from_pretrained("peter881122/freewaynModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from peter881122/freewaynModel: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/peter881122/freewaynModel/resolve/main/training_args.bin
- Command line
-
hf download hf://peter881122/freewaynModel/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/peter881122/freewaynModel/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 50b3c70640b3615a3010fd41b83ef931137978f523b7033ea488a33f213ecb90
- Size of remote file:
- 5.11 kB
- SHA256:
- cf2f3b23650696500124f7559f69dabb049043ad970bd8bf3121e19fd0a97d25
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.