Instructions to use PavanMV/sft_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PavanMV/sft_output with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("PavanMV/sft_output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from PavanMV/sft_output: direct link, hf CLI and curl.
- Browser
- Download file 5.62 kB
-
https://huggingface.co/PavanMV/sft_output/resolve/main/training_args.bin
- Command line
-
hf download hf://PavanMV/sft_output/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/PavanMV/sft_output/resolve/main/training_args.bin
5.62 kB
- Xet hash:
- 69097715ee70efdfc42c036a19e85bb9fa73a0f418afe44d07624b5d43df48f9
- Size of remote file:
- 5.62 kB
- SHA256:
- 7a8e3675b87e78cbcb154a3cc424cb7f453c9a284afd5748956524109f8e4aa8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.