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 adapter_model.safetensors from hchang/reward_modeling: direct link, hf CLI and curl.
- Browser
- Download file 6.84 MB
-
https://huggingface.co/hchang/reward_modeling/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://hchang/reward_modeling/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/hchang/reward_modeling/resolve/main/adapter_model.safetensors
6.84 MB
- Xet hash:
- 937b48f136e03e190c932cfb3a266cd10fdf3165d29a6619f5b202ca2386afbb
- Size of remote file:
- 6.84 MB
- SHA256:
- f0dea6afe6af4add454fb5cee0c4d3aeb9b71fd79ceeabfff5bfb89d45ea94ce
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