Feature Extraction
Transformers
Safetensors
PEFT
reward-model
social-intelligence
reinforcement-learning
llm
qwen
Instructions to use ulab-ai/sotopia-rl-qwen2.5-7B-rm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ulab-ai/sotopia-rl-qwen2.5-7B-rm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ulab-ai/sotopia-rl-qwen2.5-7B-rm")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ulab-ai/sotopia-rl-qwen2.5-7B-rm", dtype="auto") - PEFT
How to use ulab-ai/sotopia-rl-qwen2.5-7B-rm with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Improve model card: Add tags, links, and usage
#1
by nielsr HF Staff - opened
This PR significantly enhances the model card for ulab-ai/sotopia-rl-qwen-2.5-7B-grpo by:
- Adding the
pipeline_tag: feature-extraction, which is appropriate for a reward model that outputs scores/features from text. - Specifying
library_name: transformersas the primary library for model interaction, while also addingpeftto the generaltagsto indicate its adapter nature. - Including relevant tags such as
reward-model,social-intelligence, andreinforcement-learningfor better discoverability. - Linking directly to the associated paper: Sotopia-RL: Reward Design for Social Intelligence.
- Adding links to the official project page: https://rl.sotopia.world.
- Providing a direct link to the GitHub repository: https://github.com/sotopia-lab/sotopia-rl.
- Expanding the model description with an abstract and introduction.
- Including a practical Python usage example for loading the model and performing inference as a sequence classification model.
- Adding a citation section for the paper.
These improvements will make the model more informative and user-friendly on the Hugging Face Hub.
skyyyyks changed pull request status to merged