Instructions to use Baidicoot/reward_modeling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Baidicoot/reward_modeling with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("google/gemma-2b") model = PeftModel.from_pretrained(base_model, "Baidicoot/reward_modeling") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Baidicoot/reward_modeling: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/Baidicoot/reward_modeling/resolve/main/training_args.bin
- Command line
-
hf download hf://Baidicoot/reward_modeling/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Baidicoot/reward_modeling/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 02954dc94096adfdfded621126a478880056a0468ed4310262a205a3bcd41bad
- Size of remote file:
- 5.11 kB
- SHA256:
- a0e8eb4d94fcecbd8ab0aa24ee61662b7a5da2eef5f366546a325524fc03e575
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.