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)# 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
Add model card and metadata
#1
by nielsr HF Staff - opened
This PR adds a model card for the model presented in the paper Simultaneous Multi-objective Alignment Across Verifiable and Non-verifiable Rewards.
The PR includes:
- Metadata for
library_name(transformers) andpipeline_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.
Jennny changed pull request status to merged