Instructions to use zerojun/cppe5_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zerojun/cppe5_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="zerojun/cppe5_use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("zerojun/cppe5_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("zerojun/cppe5_use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from zerojun/cppe5_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/zerojun/cppe5_use_data_finetuning/resolve/1e7ff37e26a099cb27323bccbb26cec04e45111d/pytorch_model.bin
- Command line
-
hf download hf://zerojun/cppe5_use_data_finetuning@1e7ff37e26a099cb27323bccbb26cec04e45111d/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/zerojun/cppe5_use_data_finetuning/resolve/1e7ff37e26a099cb27323bccbb26cec04e45111d/pytorch_model.bin
167 MB
- Xet hash:
- 8ede743061c43dc7a87dc7e4912224cf43a96416e1b499944b06cc960c61c4fe
- Size of remote file:
- 167 MB
- SHA256:
- fc63f681ddca08ec1a5c6d7c3799acf639f44aad96dfcb505fa3ec967dba963d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.