Instructions to use RajuEEE/RewardModelForQuestionAnswering_GPT2_RevisedData with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use RajuEEE/RewardModelForQuestionAnswering_GPT2_RevisedData with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("GPT2-Large") model = PeftModel.from_pretrained(base_model, "RajuEEE/RewardModelForQuestionAnswering_GPT2_RevisedData") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from RajuEEE/RewardModelForQuestionAnswering_GPT2_RevisedData: direct link, hf CLI and curl.
- Browser
- Download file 11.8 MB
-
https://huggingface.co/RajuEEE/RewardModelForQuestionAnswering_GPT2_RevisedData/resolve/main/adapter_model.bin
- Command line
-
hf download hf://RajuEEE/RewardModelForQuestionAnswering_GPT2_RevisedData/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/RajuEEE/RewardModelForQuestionAnswering_GPT2_RevisedData/resolve/main/adapter_model.bin
11.8 MB
- Xet hash:
- 714e5e85b8e678ac0f930bdc234d2fc313e4bd1e79a3c0f55bd9520491377db5
- Size of remote file:
- 11.8 MB
- SHA256:
- e9ae76017c5239d3e916892e9c58196e6d1b6c93bb11901884e23bc702a9bd23
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