Instructions to use arpanl/Fine-Tuned_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arpanl/Fine-Tuned_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="arpanl/Fine-Tuned_Model") 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("arpanl/Fine-Tuned_Model") model = AutoModelForImageClassification.from_pretrained("arpanl/Fine-Tuned_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from arpanl/Fine-Tuned_Model: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/arpanl/Fine-Tuned_Model/resolve/main/training_args.bin
- Command line
-
hf download hf://arpanl/Fine-Tuned_Model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/arpanl/Fine-Tuned_Model/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- d67ec37cc1e43109657258621a23625a7fac30b51d8ab2491f0c4130b55be546
- Size of remote file:
- 4.73 kB
- SHA256:
- 8e8971b8da8b7bebdc944ac1d479346946e9656093b496c506bf460de9f2ed6e
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