Instructions to use gkalsrudals/use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gkalsrudals/use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="gkalsrudals/use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("gkalsrudals/use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("gkalsrudals/use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gkalsrudals/use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/gkalsrudals/use_data_finetuning/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://gkalsrudals/use_data_finetuning/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gkalsrudals/use_data_finetuning/resolve/main/pytorch_model.bin
167 MB
- Xet hash:
- ea3a8d0761bfb00ef356a33f951b00bd56d40e3e6fe4c50cde9d1d6a19c82ef8
- Size of remote file:
- 167 MB
- SHA256:
- 343ddf993527e8563c96296476caf37cffcc6698638ddc6c25fb6148a0340ee1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.