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:
- 1ef151fe1d0221907231da24dc7a868dfd758b39c5e7c5f2ebc9f78753820e00
- Size of remote file:
- 4 GB
- SHA256:
- ddfbab410f38951993deb28d7135db0fd73b774e88e778518aa8d7fb897405f4
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