--- base_model: Qwen/Qwen2.5-Math-7B-Instruct library_name: transformers model_name: Qwen2.5-Math-7B-Instruct-PRM-0.1 tags: - generated_from_trainer - trl - prm licence: license --- # Model Card for Qwen2.5-Math-7B-Instruct-PRM-0.1 This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct). 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="plaguss/Qwen2.5-Math-7B-Instruct-PRM-0.1", 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/plaguss/huggingface/runs/ekj67hj1) This model was trained with PRM. ### Framework versions - TRL: 0.13.0.dev0 - Transformers: 4.47.0 - Pytorch: 2.4.1 - Datasets: 3.0.1 - Tokenizers: 0.21.0 ## 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```