Instructions to use hchang/reward_modeling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use hchang/reward_modeling with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("meta-llama/Meta-Llama-3-8B") model = PeftModel.from_pretrained(base_model, "hchang/reward_modeling") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-3000/adapter_model.safetensors from hchang/reward_modeling: direct link, hf CLI and curl.
- Browser
- Download file 594 kB
-
https://huggingface.co/hchang/reward_modeling/resolve/main/checkpoint-3000/adapter_model.safetensors
- Command line
-
hf download hf://hchang/reward_modeling/checkpoint-3000/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/hchang/reward_modeling/resolve/main/checkpoint-3000/adapter_model.safetensors
594 kB
- Xet hash:
- 59cd78dbdaf694d3dd1aeea23b30ffcf74453e01f19db4d26ab12518ab26b4f3
- Size of remote file:
- 594 kB
- SHA256:
- b9a96974d5507d6c87ce1cb73655ab18eff252908ea00442a2b83fa42b8494cc
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