gPRM-14B-merged/README.md

base_model, library_name, pipeline_tag, tags, license, language

base_model	library_name	pipeline_tag	tags	license	language
deepseek-ai/DeepSeek-R1-Distill-Qwen-14B	transformers	text-generation	base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B lora transformers reward-model	apache-2.0	en

gPRM-14B-merged

This model is a LoRA-merged version of gPRM-14B for vLLM inference.

For details:

• Paper: Rethinking Reward Models for Multi-Domain Test-Time Scaling
• Repository: https://github.com/db-Lee/Multi-RM

Direct Use

import math
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# tokenizer
tokenizer = AutoTokenizer.from_pretrained('dongboklee/gPRM-14B-merged')
yes_id = tokenizer.encode(" Yes", add_special_tokens=False)[-1]
no_id = tokenizer.encode(" No", add_special_tokens=False)[-1]

# model
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = AutoModelForCausalLM.from_pretrained('dongboklee/gPRM-14B-merged')
model.eval()
model.to(device)

# prompt formatting
question = 'Question: In Python 3, which of the following function convert a string to an int in python?\nA. short(x)\nB. float(x)\nC. integer(x [,base])\nD. double(x)\nE. int(x [,base])\nF. long(x [,base] )\nG. num(x)\nH. str(x)\nI. char(x)\nJ. digit(x [,base])'
solution = ["To convert a string to an integer in Python 3, we use the built-in function int().",
            "The int() function takes two arguments: the string to be converted and an optional base (default is 10, which is for decimal).",
            "For example: int(\"123\", 10) converts the string \"123\" to the integer 123.",
            "Looking at the options, we can see that the correct function is option E: int(x [,base]).",
            "The answer is (E)."]
category_name = "computer science"
steps = [ f"Step {str(i+1)}: {step}" for i, step in enumerate(solution) ]
prefix = "\n".join(steps)

# Create the prompt
prompt_text = (
    f"You are given a {category_name} problem and a proposed step-by-step solution:\n\n"
    f"[{category_name.capitalize()} Problem]\n{question}\n\n"
    f"[Solution]\n{prefix}\n\n"
    "Review and critique each step in the proposed solution to determine whether each step is correct. If the solution is incomplete, only verify the provided steps."
)

prompt = tokenizer.apply_chat_template(
    [{'role': "user", "content": prompt_text}],
    tokenize=False, add_generation_prompt=True, add_special_tokens=False
) + "Let's verify step by step:"

# Tokenize the prompt
inputs = tokenizer(prompt, return_tensors="pt").to(device)

# generate
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=8192,
        return_dict_in_generate=True,
        output_scores=True,
        pad_token_id=tokenizer.eos_token_id
    )

# compute reward
logits = outputs.logits[0, -2, :]
yes_logit, no_logit = logits[yes_id].item(), logits[no_id].item()
reward = math.exp(yes_logit) / (math.exp(yes_logit) + math.exp(no_logit))

Citation

@article{multi-rm,
  title   = {Rethinking Reward Models for Multi-Domain Test-Time Scaling},
  author  = {Lee, Dong Bok and Lee, Seanie and Park, Sangwoo and Kang, Minki and Baek, Jinheon and Kim, Dongki and Wagner, Dominik and Jin, Jiongdao and Lee, Heejun and Bocklet, Tobias and Wang, Jinyu and Fu, Jingjing and Hwang, Sung Ju and Bian, Jiang and Song, Lei},
  journal = {arXiv preprint arXiv:2510.00492},
  year    = {2025}
}