Text Classification
Transformers
Safetensors
English
Chinese
qwen2
text-generation
reward model
Qwen-PRM
custom_code
text-embeddings-inference
Instructions to use prithivMLmods/PRM-Math-7B-Reasoner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/PRM-Math-7B-Reasoner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="prithivMLmods/PRM-Math-7B-Reasoner", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/PRM-Math-7B-Reasoner", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("prithivMLmods/PRM-Math-7B-Reasoner", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a75dad4bb4c6c54780b35de78b6128852f6f4301ec7313708d6a366f2458ce8e
- Size of remote file:
- 11.4 MB
- SHA256:
- 2f87f84cd744477c28ec88506449627f5caff7040f3cd320bc0f4f2b8de36279
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