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
Improve model card with metadata, links, and usage example
#1
by nielsr HF Staff - opened
This PR enhances the model card by:
- Adding the
pipeline_tag: text-classificationandlibrary_name: transformersto the metadata, improving discoverability and usability on the Hugging Face Hub. - Providing a direct link to the associated Hugging Face Paper: One Token to Fool LLM-as-a-Judge.
- Including links to the official GitHub repository for code and the synthetic training data, as mentioned in the paper's abstract.
- Adding a comprehensive Python code snippet demonstrating how to use the model with the Hugging Face
transformerslibrary for evaluation, including an example showcasing its robustness against adversarial inputs. - Updating the model description based on the paper's abstract for better clarity and context.
sarosavo changed pull request status to merged