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Model: dongboklee/gPRM-14B-merged
Source: Original Platform

.gitattributes

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README.md

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+---
+base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
+- lora
+- transformers
+- reward-model
+license: apache-2.0
+language:
+- en
+---
+
+# gPRM-14B-merged
+
+
+This model is a LoRA-merged version of [gPRM-14B](https://huggingface.co/dongboklee/gPRM-14B) for vLLM inference.
+
+For details:
+- **Paper:** [Rethinking Reward Models for Multi-Domain Test-Time Scaling](https://huggingface.co/papers/2510.00492)
+- **Repository:** [https://github.com/db-Lee/Multi-RM](https://github.com/db-Lee/Multi-RM)
+
+
+### Direct Use
+```python
+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}
+}
+```

chat_template.jinja

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+{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '
+' + '```json' + '
+' + tool['function']['arguments'] + '
+' + '```' + '<｜tool▁call▁end｜>'}}{%- set ns.is_first = true -%}{%- else %}{{'
+' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '
+' + '```json' + '
+' + tool['function']['arguments'] + '
+' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'
+<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜><think>
+'}}{% endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 13824,
+  "layer_types": [
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 131072,
+  "max_window_layers": 48,
+  "model_type": "qwen2",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 151646,
+  "do_sample": true,
+  "eos_token_id": 151643,
+  "temperature": 0.6,
+  "top_p": 0.95,
+  "transformers_version": "4.53.0"
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special_tokens_map.json

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