--- base_model: Qwen/Qwen2.5-1.5B library_name: transformers model_name: Qwen2.5-1.5B-Reward-Math-Sheperd tags: - generated_from_trainer - trl - prm licence: license --- # Model Card for Qwen2.5-1.5B-Reward-Math-Sheperd This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B). 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="None", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [Visualize in Weights & Biases](https://wandb.ai/uncertainty-guided-reasoning/value-model/runs/ra2126bg) This model was trained with PRM. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.8.0.dev20250319+cu128 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite PRM as: ```bibtex @article{uesato2022solving, title = {{Solving Math Word Problems With Process- and Outcome-Based Feedback}}, author = {Uesato, Jonathan and Kushman, Nate and Kumar, Ramana and Song, Francis and Siegel, Noah and Wang, Lisa and Creswell, Antonia and Irving, Geoffrey and Higgins, Irina}, year = 2022, journal = {arXiv preprint arXiv:2211.14275} } ``` 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```