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
Could you clarify whether the PRM800K deduplication was performed using the original 5000-test set from MATH or the MATH500 dataset?
#2
by masterLan - opened
Could you clarify whether the PRM800K deduplication was performed using the original 5000-test set from MATH or the MATH500 dataset?
The original 5000-test set from MATH.
The original 5000-test set from MATH.
So your MATH scores are on the full MATH test set?
The original 5000-test set from MATH.
So your MATH scores are on the full MATH test set?
Yes. We follow the setting of Qwen2.5-Math as described in the paper.