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:
- 653bf14b93e237a898c8c7adb284105364b353fe169aad3fd2750673e6fd07c9
- Size of remote file:
- 3.81 GB
- SHA256:
- d9b61fdc269681faa6256c0757ebeac132ba563a47dfffa37fa8f3e316abe346
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