Instructions to use arpanl/Fine-Tuned_Model2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arpanl/Fine-Tuned_Model2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="arpanl/Fine-Tuned_Model2") 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_Model2") model = AutoModelForImageClassification.from_pretrained("arpanl/Fine-Tuned_Model2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from arpanl/Fine-Tuned_Model2: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/arpanl/Fine-Tuned_Model2/resolve/main/training_args.bin
- Command line
-
hf download hf://arpanl/Fine-Tuned_Model2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/arpanl/Fine-Tuned_Model2/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- 76003f8181d3b751edbf1c48720fcaf66569dc30b2fd23dc1b62b41cda9376b8
- Size of remote file:
- 4.73 kB
- SHA256:
- f1e4d94a1b00b12ced2e71a18171331ad652ad836ccaa50438622fa4c51759c5
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