Text Classification
Transformers
Safetensors
English
Chinese
qwen2
feature-extraction
reward model
custom_code
text-embeddings-inference
Instructions to use Qwen/Qwen2.5-Math-PRM-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qwen/Qwen2.5-Math-PRM-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Qwen/Qwen2.5-Math-PRM-7B", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Math-PRM-7B", trust_remote_code=True) model = AutoModel.from_pretrained("Qwen/Qwen2.5-Math-PRM-7B", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Questions about data scale
#9
by masterLan - opened
How much data was used to train the final version of Qwen-2.5-MATH-PRM?
While reading the paper, I noticed that in Figure 8, the score for ProcessBench is 66.5. However, in the final results presented in Table 7, the score for Qwen-2.5-MATH-PRM is 73.5. Additionally, the data in Figure 8 does not match the data in Table 4, which raises some concerns.

