Instructions to use KasperRH/Raiyan_Kasper_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KasperRH/Raiyan_Kasper_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="KasperRH/Raiyan_Kasper_Model")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("KasperRH/Raiyan_Kasper_Model") model = AutoModelForObjectDetection.from_pretrained("KasperRH/Raiyan_Kasper_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KasperRH/Raiyan_Kasper_Model: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/KasperRH/Raiyan_Kasper_Model/resolve/main/training_args.bin
- Command line
-
hf download hf://KasperRH/Raiyan_Kasper_Model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KasperRH/Raiyan_Kasper_Model/resolve/main/training_args.bin
3.52 kB
- Xet hash:
- 5e5cc6121bc2891228dbe5fc7c068c76ebd18f0ed561a8ddfdc7783509124893
- Size of remote file:
- 3.52 kB
- SHA256:
- ceff6a564afde06b3730df5e4befaaf161f5d9e1264ab7f77e5b407f89098fad
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.