Instructions to use RoadQAQ/ReLIFT-Qwen2.5-Math-1.5B-Zero with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RoadQAQ/ReLIFT-Qwen2.5-Math-1.5B-Zero with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="RoadQAQ/ReLIFT-Qwen2.5-Math-1.5B-Zero")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("RoadQAQ/ReLIFT-Qwen2.5-Math-1.5B-Zero") model = AutoModelForCausalLM.from_pretrained("RoadQAQ/ReLIFT-Qwen2.5-Math-1.5B-Zero") - Notebooks
- Google Colab
- Kaggle
Add model card with metadata and links
#1
by nielsr HF Staff - opened
This PR adds a comprehensive model card to improve the discoverability and usability of this model. The card includes essential metadata and links to relevant resources, including the paper and the project's GitHub repository. This makes the model more easily searchable and accessible within the Hugging Face ecosystem and via its associated question answering pipeline tag.
RoadQAQ changed pull request status to merged