Instructions to use RajuEEE/RewardModelForQuestionAnswering_LLama2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use RajuEEE/RewardModelForQuestionAnswering_LLama2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("abhishek/llama-2-7b-hf-small-shards") model = PeftModel.from_pretrained(base_model, "RajuEEE/RewardModelForQuestionAnswering_LLama2") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from RajuEEE/RewardModelForQuestionAnswering_LLama2: direct link, hf CLI and curl.
- Browser
- Download file 33.6 MB
-
https://huggingface.co/RajuEEE/RewardModelForQuestionAnswering_LLama2/resolve/main/adapter_model.bin
- Command line
-
hf download hf://RajuEEE/RewardModelForQuestionAnswering_LLama2/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/RajuEEE/RewardModelForQuestionAnswering_LLama2/resolve/main/adapter_model.bin
33.6 MB
- Xet hash:
- 24ef092a76a42b840deeaff3ee8b87425e28097edc605efd07f6d34673019cf0
- Size of remote file:
- 33.6 MB
- SHA256:
- ad8df1c8c5087c375b7301e7aa97e793218846bbf76c47b4916ecc32cd1f216a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.