Instructions to use RoadQAQ/ReLIFT-Qwen2.5-Math-7B-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-7B-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-7B-Zero")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("RoadQAQ/ReLIFT-Qwen2.5-Math-7B-Zero") model = AutoModelForCausalLM.from_pretrained("RoadQAQ/ReLIFT-Qwen2.5-Math-7B-Zero") - Notebooks
- Google Colab
- Kaggle
Add model card
#1
by nielsr HF Staff - opened
This PR adds a model card for the ReLIFT model, linking it to the paper and the code repository. It also adds relevant metadata, including the pipeline tag, library name, and license.
RoadQAQ changed pull request status to merged