Text Classification
PEFT
Safetensors
rag-gym
retrieval-augmented-generation
agent
lora
process-supervision
reward-model
Instructions to use RAG-Gym/ReSearch-MedQA-PRM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use RAG-Gym/ReSearch-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/ReSearch-MedQA-PRM") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from RAG-Gym/ReSearch-MedQA-PRM: direct link, hf CLI and curl.
- Browser
- Download file 17.2 MB
-
https://huggingface.co/RAG-Gym/ReSearch-MedQA-PRM/resolve/main/tokenizer.json
- Command line
-
hf download hf://RAG-Gym/ReSearch-MedQA-PRM/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/RAG-Gym/ReSearch-MedQA-PRM/resolve/main/tokenizer.json
17.2 MB
- Xet hash:
- f945fd7b0925634ee03a5a04a0c3307616ad654788e5ccae12ff4b8e329b486d
- Size of remote file:
- 17.2 MB
- SHA256:
- 44c194730e115c4c06f0cf5ae599e25c455f68c360eb061d5a60d55fcb87aef9
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