Text Classification
PEFT
Safetensors
rag-gym
retrieval-augmented-generation
agent
lora
process-supervision
reward-model
Instructions to use RAG-Gym/ReSearch-HotpotQA-PRM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use RAG-Gym/ReSearch-HotpotQA-PRM with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct") model = PeftModel.from_pretrained(base_model, "RAG-Gym/ReSearch-HotpotQA-PRM") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from RAG-Gym/ReSearch-HotpotQA-PRM: direct link, hf CLI and curl.
- Browser
- Download file 5.69 kB
-
https://huggingface.co/RAG-Gym/ReSearch-HotpotQA-PRM/resolve/main/training_args.bin
- Command line
-
hf download hf://RAG-Gym/ReSearch-HotpotQA-PRM/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/RAG-Gym/ReSearch-HotpotQA-PRM/resolve/main/training_args.bin
5.69 kB
- Xet hash:
- 706831c8b1377142a5a587b46fab9a47f92f3c1d0b0149b51943591e49e416b9
- Size of remote file:
- 5.69 kB
- SHA256:
- 1671e5edfdee6fb0764a9bcbff92ceee7372106dae65d1a6dc0caac8b7b7fd5f
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