Instructions to use hyunjong7/gemma-fire-finetun-27b_it_1500_0621_rl1s_10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hyunjong7/gemma-fire-finetun-27b_it_1500_0621_rl1s_10 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hyunjong7/gemma-fire-finetun-27b_it_1500_0621_rl1s_10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from hyunjong7/gemma-fire-finetun-27b_it_1500_0621_rl1s_10: direct link, hf CLI and curl.
- Browser
- Download file 6.1 kB
-
https://huggingface.co/hyunjong7/gemma-fire-finetun-27b_it_1500_0621_rl1s_10/resolve/main/training_args.bin
- Command line
-
hf download hf://hyunjong7/gemma-fire-finetun-27b_it_1500_0621_rl1s_10/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/hyunjong7/gemma-fire-finetun-27b_it_1500_0621_rl1s_10/resolve/main/training_args.bin
6.1 kB
- Xet hash:
- 27ce43f02abaae35cced65eb79133d37a51a82f5013d4d1251bca1ec7b17e635
- Size of remote file:
- 6.1 kB
- SHA256:
- bfc9b08212fc844a555e92decc6f978fc346fec3ad51796562105f9ef0d69764
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.