Instructions to use dongboklee/dORM-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dongboklee/dORM-14B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dongboklee/dORM-14B")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dongboklee/dORM-14B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Improve model card: Add description, links, license, update pipeline tag, and sample usage
#1
by nielsr HF Staff - opened
This PR significantly improves the model card for the "Rethinking Reward Models for Multi-Domain Test-Time Scaling" model by:
- Updating the
licensetoapache-2.0. - Changing the
pipeline_tagfromtext-generationtotext-classification, which more accurately reflects the model's function as a reward model. - Setting the
library_nametotransformersfor better integration and discoverability, as the model is compatible withtransformers(especially for merged versions), while retainingpeftas a tag. - Populating the model description with details from the paper's abstract.
- Including direct links to the paper on Hugging Face and the GitHub repository.
- Providing a concrete Python code snippet for inference, adapted from the GitHub README's usage patterns for the merged model variant.
- Adding relevant
tags, includingreward-model. - Populating training and evaluation details.
- Including the BibTeX citation.
Please review and merge this PR.