Instructions to use prxy5605/5198936b-d29b-46b8-be70-b3538b6b8874 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use prxy5605/5198936b-d29b-46b8-be70-b3538b6b8874 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2") model = PeftModel.from_pretrained(base_model, "prxy5605/5198936b-d29b-46b8-be70-b3538b6b8874") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from prxy5605/5198936b-d29b-46b8-be70-b3538b6b8874: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/prxy5605/5198936b-d29b-46b8-be70-b3538b6b8874/resolve/main/training_args.bin
- Command line
-
hf download hf://prxy5605/5198936b-d29b-46b8-be70-b3538b6b8874/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/prxy5605/5198936b-d29b-46b8-be70-b3538b6b8874/resolve/main/training_args.bin
6.84 kB
- Xet hash:
- 7a1314a4092708c69467be8bed24824f9c578a63f0c7fe977f9b8b932fb779e1
- Size of remote file:
- 6.84 kB
- SHA256:
- 2b672663c9c1836fd469fa5fe59ec58b88c76d19d0d4cbdd51b7fee19473a82b
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