--- license: llama2 language: - en pipeline_tag: text-classification --- # **ReasonEval-34B Model Card** ## Model Description `ReasonEval-34B` is a 34B parameter decoder-only language model fine-tuned from [`llemma_34b`](https://huggingface.co./EleutherAI/llemma_34b). Given a mathematical problem and the solution, `ReasonEval-34B` assesses the problem-solving process in a step-by-step format from the following perspectives: - **Validity**: The step contains no mistakes in calculation and logic. - **Redundancy**: The step lacks utility in solving the problem but is still valid. With ReasonEval, you can - 📏 quantify the quality of reasoning steps free of human or close-source models. - 🤖 find the potential invalid or redundant steps in the solutions even with the correct results. - 🛠️ select high-quality training data for downstream tasks (e.g., fine-tuning). ## Model Details * **Model type**: `ReasonEval-34B`'s architecture is identical to [`llemma_34b`](https://huggingface.co./EleutherAI/llemma_34b), except that the classification head for next-token prediction is replaced with a classification head for outputting the possibilities of each class of reasong steps. * **Language(s)**: English * **Paper**: [Evaluating Mathematical Reasoning Beyond Accuracy](https://arxiv.org/pdf/2404.05692.pdf) * **Github**: [https://github.com/GAIR-NLP/ReasonEval](https://github.com/GAIR-NLP/ReasonEval) * **Finetuned from model**: [https://huggingface.co./EleutherAI/llemma_34b](https://huggingface.co./EleutherAI/llemma_34b) * **Fine-tuning Data**: [PRM800K](https://github.com/openai/prm800k) For detailed instructions on how to use the ReasonEval-34B model, visit our GitHub repository at [https://github.com/GAIR-NLP/ReasonEval](https://github.com/GAIR-NLP/ReasonEval). ## How to Cite ```bibtex @article{xia2024evaluating, title={Evaluating Mathematical Reasoning Beyond Accuracy}, author={Xia, Shijie and Li, Xuefeng and Liu, Yixin and Wu, Tongshuang and Liu, Pengfei}, journal={arXiv preprint arXiv:2404.05692}, year={2024}, } ```