Instructions to use shibajustfor/97e6c917-e820-40ce-af2e-b33a1b676b69 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/97e6c917-e820-40ce-af2e-b33a1b676b69 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1_5") model = PeftModel.from_pretrained(base_model, "shibajustfor/97e6c917-e820-40ce-af2e-b33a1b676b69") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from shibajustfor/97e6c917-e820-40ce-af2e-b33a1b676b69: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/shibajustfor/97e6c917-e820-40ce-af2e-b33a1b676b69/resolve/main/training_args.bin
- Command line
-
hf download hf://shibajustfor/97e6c917-e820-40ce-af2e-b33a1b676b69/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/shibajustfor/97e6c917-e820-40ce-af2e-b33a1b676b69/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 60847405a5a56c21593dee4734ffda8b9669d915cf52f4dd1f08234061cb1bcc
- Size of remote file:
- 6.78 kB
- SHA256:
- 56cde9d99a5d19d69d2cf4c8681a80c44a373b08e12ceff3d3b1fa3f01a4743c
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