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:
- 2f8c099a0174ca2b6020609646f1e2e10ff58cee83210ba59ea95117066da2cf
- Size of remote file:
- 4 GB
- SHA256:
- 9df8dee9e4609bcd3a3e1443db79c27a1d0e5829443ce988ab3e70fb4cb066d0
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