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-3004/rng_state.pth from hchang/reward_modeling: direct link, hf CLI and curl.
- Browser
- Download file 14.2 kB
-
https://huggingface.co/hchang/reward_modeling/resolve/main/checkpoint-3004/rng_state.pth
- Command line
-
hf download hf://hchang/reward_modeling/checkpoint-3004/rng_state.pth
-
curl -L -o rng_state.pth https://huggingface.co/hchang/reward_modeling/resolve/main/checkpoint-3004/rng_state.pth
14.2 kB
- Xet hash:
- c27aec9c265c9b95a760789519abe52f94645400a037cd5b8fd8290daf4ec365
- Size of remote file:
- 14.2 kB
- SHA256:
- 837c235220b00aa9bd6fd9e829dc15309c352bfb953f2629a19c980bd73a1269
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