Instructions to use mdeputy/windowz_test-020525-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mdeputy/windowz_test-020525-1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import UNETForSegmentation model = UNETForSegmentation.from_pretrained("mdeputy/windowz_test-020525-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mdeputy/windowz_test-020525-1: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/mdeputy/windowz_test-020525-1/resolve/main/training_args.bin
- Command line
-
hf download hf://mdeputy/windowz_test-020525-1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mdeputy/windowz_test-020525-1/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- a03f76b4cc12318bbfd6f805fd5576c73fc9db2cf1979b5d7ad2c537d8baf3a6
- Size of remote file:
- 5.24 kB
- SHA256:
- 3bfb62009d38347524cca0b69f3f8a36197c8284418aa4532e75ca8063df32f1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.