Text Classification
Transformers
Safetensors
English
Chinese
qwen2
feature-extraction
reward model
custom_code
text-embeddings-inference
Instructions to use Qwen/Qwen2.5-Math-PRM-72B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qwen/Qwen2.5-Math-PRM-72B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Qwen/Qwen2.5-Math-PRM-72B", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Math-PRM-72B", trust_remote_code=True) model = AutoModel.from_pretrained("Qwen/Qwen2.5-Math-PRM-72B", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Questions about data scale
#1
by masterLan - opened
How much data was used to train the final version of Qwen-2.5-MATH-PRM?
The final version of Qwen‑2.5‑Math‑PRM (the Qwen2.5‑Math‑7B‑PRM model) was trained on 3 million MC-estimation samples, which underwent a consensus filtering step that retained only about 40% of them. That leaves a final training set of approximately 1.2 million high-consensus samples