Instructions to use gaf7921/cppe5_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gaf7921/cppe5_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="gaf7921/cppe5_use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("gaf7921/cppe5_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("gaf7921/cppe5_use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from gaf7921/cppe5_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/gaf7921/cppe5_use_data_finetuning/resolve/6cb071c1af2fdf7d316041459d6dbea3ec00e98d/training_args.bin
- Command line
-
hf download hf://gaf7921/cppe5_use_data_finetuning@6cb071c1af2fdf7d316041459d6dbea3ec00e98d/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/gaf7921/cppe5_use_data_finetuning/resolve/6cb071c1af2fdf7d316041459d6dbea3ec00e98d/training_args.bin
4.54 kB
- Xet hash:
- 17789f28613b936af67ca70cc4e321c6eb523ced6f120ca9e81e610d9cc7c533
- Size of remote file:
- 4.54 kB
- SHA256:
- 9fba07f2409b3638877821112a4861ad71d6fb9a6882856d87f70802a8763faa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.