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
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by nielsr HF Staff - opened
README.md
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---
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license: cc-by-nc-4.0
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library_name: transformers
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pipeline_tag: question-answering
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---
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This repository contains the ReLIFT model presented in [Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest Questions](https://huggingface.co/papers/2506.07527).
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Code: https://github.com/TheRoadQaQ/ReLIFT
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Hugging Face Collection: https://huggingface.co/collections/RoadQAQ/relift-684535e199a909cad16d8b05
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