--- base_model: Qwen/Qwen2.5-Math-1.5B-Instruct library_name: transformers model_name: out_biprm_math_qwen2.5_rlhflow_mistral tags: - generated_from_trainer - bidirectional-prm - trl licence: license --- # Model Card for out_biprm_math_qwen2.5_rlhflow_mistral This model is a fine-tuned version of [Qwen/Qwen2.5-Math-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B-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="None", 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 BidirectionalPRM, a method introduced in [Solving math word problems with process-and outcome-based feedback](https://huggingface.co/papers/2211.14275). ### Framework versions - TRL: 0.29.0 - Transformers: 5.12.1 - Pytorch: 2.12.1 - Datasets: 5.0.0 - Tokenizers: 0.22.2 ## Citations Cite BidirectionalPRM 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 @software{vonwerra2020trl, title = {{TRL: Transformers Reinforcement Learning}}, author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin}, license = {Apache-2.0}, url = {https://github.com/huggingface/trl}, year = {2020} } ```