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---
license: other
license_name: qwen
license_link: https://huggingface.co/Qwen/Qwen2.5-Math-PRM-7B/blob/main/LICENSE
language:
- en
- zh
pipeline_tag: text-classification
library_name: transformers
tags:
- reward model
base_model:
- Qwen/Qwen2.5-Math-7B-Instruct
---
# Qwen2.5-Math-PRM-7B # Qwen2.5-Math-PRM-7B
Qwen2.5-Math-PRM-7B ## Introduction
In addition to the mathematical Outcome Reward Model (ORM) Qwen2.5-Math-RM-72B, we release the Process Reward Model (PRM), namely Qwen2.5-Math-PRM-7B and Qwen2.5-Math-PRM-72B. PRMs emerge as a promising approach for process supervision in mathematical reasoning of Large Language Models (LLMs), aiming to identify and mitigate intermediate errors in the reasoning processes. Our trained PRMs exhibit both impressive performance in the Best-of-N (BoN) evaluation and stronger error identification performance in [ProcessBench](https://huggingface.co/papers/2412.06559).
![](http://qianwen-res.oss-accelerate-overseas.aliyuncs.com/Qwen2.5/Qwen2.5-Math-PRM/Qwen2.5-Math-PRM.png)
## Model Details
For more details, please refer to our [paper](https://arxiv.org/pdf/2501.07301).
## Requirements
* `transformers>=4.40.0` for Qwen2.5-Math models. The latest version is recommended.
> [!Warning]
> <div align="center">
> <b>
> 🚨 This is a must because `transformers` integrated Qwen2.5 codes since `4.37.0`.
> </b>
> </div>
For requirements on GPU memory and the respective throughput, see similar results of Qwen2 [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
## Quick Start
> [!Important]
>
> **Qwen2.5-Math-PRM-7B** is a process reward model typically used for offering feedback on the quality of reasoning and intermediate steps rather than generation.
### Prerequisites
- Step Separation: We recommend using double line breaks ("\n\n") to separate individual steps within the solution if using responses from Qwen2.5-Math-Instruct.
- Reward Computation: After each step, we insert a special token "`<extra_0>`". For reward calculation, we extract the probability score of this token being classified as positive, resulting in a reward value between 0 and 1.
### 🤗 Hugging Face Transformers
Here we show a code snippet to show you how to use the Qwen2.5-Math-PRM-7B with `transformers`:
```python
import torch
from transformers import AutoModel, AutoTokenizer
import torch.nn.functional as F
def make_step_rewards(logits, token_masks):
probabilities = F.softmax(logits, dim=-1)
probabilities = probabilities * token_masks.unsqueeze(-1) # bs, seq_len, num_labels
all_scores_res = []
for i in range(probabilities.size(0)):
sample = probabilities[i] # seq_len, num_labels
positive_probs = sample[sample != 0].view(-1, 2)[:, 1] # valid_tokens, num_labels
non_zero_elements_list = positive_probs.cpu().tolist()
all_scores_res.append(non_zero_elements_list)
return all_scores_res
model_name = "Qwen/Qwen2.5-Math-PRM-7B"
device = "auto"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModel.from_pretrained(
model_name,
device_map=device,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
).eval()
data = {
"system": "Please reason step by step, and put your final answer within \boxed{}.",
"query": "Sue lives in a fun neighborhood. One weekend, the neighbors decided to play a prank on Sue. On Friday morning, the neighbors placed 18 pink plastic flamingos out on Sue's front yard. On Saturday morning, the neighbors took back one third of the flamingos, painted them white, and put these newly painted white flamingos back out on Sue's front yard. Then, on Sunday morning, they added another 18 pink plastic flamingos to the collection. At noon on Sunday, how many more pink plastic flamingos were out than white plastic flamingos?",
"response": [
"To find out how many more pink plastic flamingos were out than white plastic flamingos at noon on Sunday, we can break down the problem into steps. First, on Friday, the neighbors start with 18 pink plastic flamingos.",
"On Saturday, they take back one third of the flamingos. Since there were 18 flamingos, (1/3 \times 18 = 6) flamingos are taken back. So, they have (18 - 6 = 12) flamingos left in their possession. Then, they paint these 6 flamingos white and put them back out on Sue's front yard. Now, Sue has the original 12 pink flamingos plus the 6 new white ones. Thus, by the end of Saturday, Sue has (12 + 6 = 18) pink flamingos and 6 white flamingos.",
"On Sunday, the neighbors add another 18 pink plastic flamingos to Sue's front yard. By the end of Sunday morning, Sue has (18 + 18 = 36) pink flamingos and still 6 white flamingos.",
"To find the difference, subtract the number of white flamingos from the number of pink flamingos: (36 - 6 = 30). Therefore, at noon on Sunday, there were 30 more pink plastic flamingos out than white plastic flamingos. The answer is (\boxed{30})."
]
}
messages = [
{"role": "system", "content": data['system']},
{"role": "user", "content": data['query']},
{"role": "assistant", "content": "<extra_0>".join(data['response']) + "<extra_0>"},
]
conversation_str = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=False
)
input_ids = tokenizer.encode(
conversation_str,
return_tensors="pt",
).to(model.device)
outputs = model(input_ids=input_ids)
step_sep_id = tokenizer.encode("<extra_0>")[0]
token_masks = (input_ids == step_sep_id)
step_reward = make_step_rewards(outputs[0], token_masks)
print(step_reward) # [[1.0, 0.1904296875, 0.9765625, 1.0]]
```
## Citation
If you find our work helpful, feel free to give us a citation.
```
@article{prmlessons,
title={The Lessons of Developing Process Reward Models in Mathematical Reasoning},
author={
Zhenru Zhang and Chujie Zheng and Yangzhen Wu and Beichen Zhang and Runji Lin and Bowen Yu and Dayiheng Liu and Jingren Zhou and Junyang Lin
},
journal={arXiv preprint arXiv:2501.07301},
year={2025}
}
```

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{
"architectures": [
"Qwen2ForProcessRewardModel"
],
"attention_dropout": 0.0,
"auto_map": {
"AutoConfig": "configuration_qwen2_rm.Qwen2RMConfig",
"AutoModel": "modeling_qwen2_rm.Qwen2ForProcessRewardModel"
},
"bos_token_id": 151643,
"eos_token_id": 151645,
"hidden_act": "silu",
"hidden_size": 3584,
"initializer_range": 0.02,
"intermediate_size": 18944,
"max_position_embeddings": 4096,
"max_window_layers": 28,
"model_type": "qwen2",
"num_attention_heads": 28,
"num_hidden_layers": 28,
"num_key_value_heads": 4,
"rms_norm_eps": 1e-05,
"rope_theta": 10000.0,
"sliding_window": 131072,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.37.0",
"use_cache": true,
"use_mrope": false,
"use_sliding_window": false,
"vocab_size": 152064
}

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{"framework":"Pytorch","task":"text-generation"}

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# coding=utf-8
# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Qwen2 model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class Qwen2RMConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Qwen2Model`]. It is used to instantiate a
Qwen2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of
Qwen2-7B-beta [Qwen/Qwen2-7B-beta](https://huggingface.co/Qwen/Qwen2-7B-beta).
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 151936):
Vocabulary size of the Qwen2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Qwen2Model`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 22016):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 32):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 32768):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether the model's input and output word embeddings should be tied.
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
use_sliding_window (`bool`, *optional*, defaults to `False`):
Whether to use sliding window attention.
sliding_window (`int`, *optional*, defaults to 4096):
Sliding window attention (SWA) window size. If not specified, will default to `4096`.
max_window_layers (`int`, *optional*, defaults to 28):
The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
```python
>>> from transformers import Qwen2Model, Qwen2Config
>>> # Initializing a Qwen2 style configuration
>>> configuration = Qwen2Config()
>>> # Initializing a model from the Qwen2-7B style configuration
>>> model = Qwen2Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "qwen2"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=151936,
hidden_size=4096,
intermediate_size=22016,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=32,
hidden_act="silu",
max_position_embeddings=32768,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
tie_word_embeddings=False,
rope_theta=10000.0,
use_sliding_window=False,
sliding_window=4096,
max_window_layers=28,
attention_dropout=0.0,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.use_sliding_window = use_sliding_window
self.sliding_window = sliding_window if use_sliding_window else None
self.max_window_layers = max_window_layers
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.attention_dropout = attention_dropout
super().__init__(
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)

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{
"bos_token_id": 151643,
"pad_token_id": 151643,
"do_sample": true,
"eos_token_id": [
151645,
151643
],
"repetition_penalty": 1.05,
"temperature": 0.7,
"top_p": 0.8,
"top_k": 20,
"transformers_version": "4.37.0"
}

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