Text Classification
Transformers
Safetensors
qwen3
reward-model
rlhf
dpo
alignment
wildchat
text-embeddings-inference
Instructions to use THU-KEG/WildReward-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use THU-KEG/WildReward-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="THU-KEG/WildReward-8B")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("THU-KEG/WildReward-8B") model = AutoModelForSequenceClassification.from_pretrained("THU-KEG/WildReward-8B") - Notebooks
- Google Colab
- Kaggle
Add paper link, fix repository URL, and update metadata
#1
by nielsr HF Staff - opened
Hi! I'm Niels from the Hugging Face community team.
This pull request improves the model card for WildReward-8B by:
- Linking the research paper WildReward: Learning Reward Models from In-the-Wild Human Interactions via the
arxivmetadata field. - Adding the
pipeline_tag: text-classificationfor better model discovery. - Fixing the placeholder GitHub repository link in the footer.
- Adding the BibTeX citation to help researchers cite your work.
- Listing the associated
WildFBdataset in the metadata.
These changes help users find the relevant research context and code implementation more easily.