Text Classification
PEFT
Safetensors
rag-gym
retrieval-augmented-generation
agent
lora
process-supervision
reward-model
Instructions to use RAG-Gym/ReAct-MedQA-PRM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use RAG-Gym/ReAct-MedQA-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/ReAct-MedQA-PRM") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from RAG-Gym/ReAct-MedQA-PRM: direct link, hf CLI and curl.
- Browser
- Download file 5.75 kB
-
https://huggingface.co/RAG-Gym/ReAct-MedQA-PRM/resolve/main/training_args.bin
- Command line
-
hf download hf://RAG-Gym/ReAct-MedQA-PRM/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/RAG-Gym/ReAct-MedQA-PRM/resolve/main/training_args.bin
5.75 kB
- Xet hash:
- 98425188ed352dc08bc7a61f95190307f7f95f371ded9136bee78d481ee947f3
- Size of remote file:
- 5.75 kB
- SHA256:
- 1df805c55a8d88c74af35ebe0f5d0c8251647e427de07988fa74d75d6c6600cb
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