|  | --- | 
					
						
						|  | base_model: Qwen/Qwen2.5-Math-7B | 
					
						
						|  | library_name: transformers | 
					
						
						|  | model_name: Qwen-2.5-Math-7B-RL-E4 | 
					
						
						|  | tags: | 
					
						
						|  | - generated_from_trainer | 
					
						
						|  | - trl | 
					
						
						|  | - grpo | 
					
						
						|  | licence: license | 
					
						
						|  | --- | 
					
						
						|  |  | 
					
						
						|  | # Model Card for Qwen-2.5-Math-7B-RL-E4 | 
					
						
						|  |  | 
					
						
						|  | This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B). | 
					
						
						|  | 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="chenggong1995/Qwen-2.5-Math-7B-RL-E4", device="cuda") | 
					
						
						|  | output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] | 
					
						
						|  | print(output["generated_text"]) | 
					
						
						|  | ``` | 
					
						
						|  |  | 
					
						
						|  | ## Training procedure | 
					
						
						|  |  | 
					
						
						|  | [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/gongc1995-city-university-of-hong-kong/huggingface/runs/epbkgvrt) | 
					
						
						|  |  | 
					
						
						|  |  | 
					
						
						|  | 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: 4.49.0 | 
					
						
						|  | - Pytorch: 2.5.1 | 
					
						
						|  | - Datasets: 3.3.2 | 
					
						
						|  | - Tokenizers: 0.21.0 | 
					
						
						|  |  | 
					
						
						|  | ## 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}} | 
					
						
						|  | } | 
					
						
						|  | ``` |