Instructions to use uracoder/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use uracoder/results with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("uracoder/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from uracoder/results: direct link, hf CLI and curl.
- Browser
- Download file 6.1 kB
-
https://huggingface.co/uracoder/results/resolve/main/training_args.bin
- Command line
-
hf download hf://uracoder/results/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/uracoder/results/resolve/main/training_args.bin
6.1 kB
- Xet hash:
- be0445e116cef1d42210ddad71d6122e7cdbdd3bbba43a78c88ad4b177bcfc54
- Size of remote file:
- 6.1 kB
- SHA256:
- f9b5f8e4873800c4137ae4b1240f4d4d1664b15eb6739efdbc78469ae2272619
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.