Instructions to use 0x1202/9a8ca2d8-f627-4801-9f82-83118641eafd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use 0x1202/9a8ca2d8-f627-4801-9f82-83118641eafd 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, "0x1202/9a8ca2d8-f627-4801-9f82-83118641eafd") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from 0x1202/9a8ca2d8-f627-4801-9f82-83118641eafd: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/0x1202/9a8ca2d8-f627-4801-9f82-83118641eafd/resolve/main/training_args.bin
- Command line
-
hf download hf://0x1202/9a8ca2d8-f627-4801-9f82-83118641eafd/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/0x1202/9a8ca2d8-f627-4801-9f82-83118641eafd/resolve/main/training_args.bin
6.84 kB
- Xet hash:
- fb059fdc26946d1e76a99d3af16884bf5cc7e1516d89266253755348cfbf75cc
- Size of remote file:
- 6.84 kB
- SHA256:
- e9468aa0bf865ec8c72bf1cba8e26a34209825fd90cc2f7efc69246f0053c724
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