Instructions to use NTQuoc/OpenRS-GRPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NTQuoc/OpenRS-GRPO with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("NTQuoc/OpenRS-GRPO", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| base_model: Qwen/Qwen3.5-0.8B | |
| datasets: knoveleng/open-rs | |
| library_name: transformers | |
| model_name: OpenRS-GRPO | |
| tags: | |
| - generated_from_trainer | |
| - open-r1 | |
| - trl | |
| - grpo | |
| licence: license | |
| # Model Card for OpenRS-GRPO | |
| This model is a fine-tuned version of [Qwen/Qwen3.5-0.8B](https://huggingface.co/Qwen/Qwen3.5-0.8B) on the [knoveleng/open-rs](https://huggingface.co/datasets/knoveleng/open-rs) dataset. | |
| It has been trained using [TRL](https://github.com/huggingface/trl). | |
| ## Quick start | |
| ```python | |
| from transformers import pipeline | |
| question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" | |
| generator = pipeline("text-generation", model="NTQuoc/OpenRS-GRPO", device="cuda") | |
| output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] | |
| print(output["generated_text"]) | |
| ``` | |
| ## Training procedure | |
| This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). | |
| ### Framework versions | |
| - TRL: 0.16.0.dev0 | |
| - Transformers: 5.8.0.dev0 | |
| - Pytorch: 2.5.1 | |
| - Datasets: 4.8.3 | |
| - Tokenizers: 0.22.2 | |
| ## Citations | |
| Cite GRPO as: | |
| ```bibtex | |
| @article{zhihong2024deepseekmath, | |
| title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, | |
| author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, | |
| year = 2024, | |
| eprint = {arXiv:2402.03300}, | |
| } | |
| ``` | |
| Cite TRL as: | |
| ```bibtex | |
| @misc{vonwerra2022trl, | |
| title = {{TRL: Transformer Reinforcement Learning}}, | |
| author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, | |
| year = 2020, | |
| journal = {GitHub repository}, | |
| publisher = {GitHub}, | |
| howpublished = {\url{https://github.com/huggingface/trl}} | |
| } | |
| ``` |