Text Classification
Transformers
Safetensors
qwen2
feature-extraction
custom_code
text-embeddings-inference
Instructions to use Jennny/qwen-math-value-model-join-09-5e5-3ep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jennny/qwen-math-value-model-join-09-5e5-3ep with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jennny/qwen-math-value-model-join-09-5e5-3ep", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Jennny/qwen-math-value-model-join-09-5e5-3ep", trust_remote_code=True) model = AutoModel.from_pretrained("Jennny/qwen-math-value-model-join-09-5e5-3ep", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Verified signatureThis PR adds a model card for the model presented in the paper [Simultaneous Multi-objective Alignment Across Verifiable and Non-verifiable Rewards](https://huggingface.co/papers/2510.01167). The PR includes: - Metadata for `library_name` (`transformers`) and `pipeline_tag` (`text-classification`). - A link to the paper on Hugging Face. - A link to the official GitHub repository for code and context. - The project citation.6d3c316
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