Instructions to use sarosavo/Master-RM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sarosavo/Master-RM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sarosavo/Master-RM")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("sarosavo/Master-RM") model = AutoModelForCausalLM.from_pretrained("sarosavo/Master-RM") - Notebooks
- Google Colab
- Kaggle
Add pipeline tag and GitHub link to model card
#2
by nielsr HF Staff - opened
This PR improves the model card by:
- Adding the
pipeline_tag: text-classificationto the metadata for better discoverability. - Including a direct link to the associated GitHub repository (
https://github.com/Yulai-Zhao/Robust-Reward-Model), making it easier for users to find the code and further resources.
sarosavo changed pull request status to merged