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
- Xet hash:
- a6d714ad1833d678ba5716aab6b81b1eaae9846fb6b8ffc9dfa58b4017ebce9c
- Size of remote file:
- 4 GB
- SHA256:
- 1ffa9b939b79c9026dcca2af2d397213c5e3ff0247dfc99b5c7e0fb7ae6bdcd8
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