|
--- |
|
license: other |
|
license_name: tongyi-qianwen |
|
license_link: https://huggingface.co/Qwen/Qwen2-Math-72B/blob/main/LICENSE |
|
language: |
|
- en |
|
pipeline_tag: text-generation |
|
tags: |
|
- chat |
|
--- |
|
|
|
|
|
# Qwen2-Math-72B |
|
|
|
> [!Warning] |
|
> <div align="center"> |
|
> <b> |
|
> 🚨 Temporarily this model mainly supports English. We will release bilingual (English & Chinese) models soon! |
|
> </b> |
|
> </div> |
|
|
|
## Introduction |
|
|
|
Over the past year, we have dedicated significant effort to researching and enhancing the reasoning capabilities of large language models, with a particular focus on their ability to solve arithmetic and mathematical problems. Today, we are delighted to introduce a serise of math-specific large language models of our Qwen2 series, Qwen2-Math and Qwen2-Math-Instruct-1.5B/7B/72B. Qwen2-Math is a series of specialized math language models built upon the Qwen2 LLMs, which significantly outperforms the mathematical capabilities of open-source models and even closed-source models (e.g., GPT4o). We hope that Qwen2-Math can contribute to the scientific community for solving advanced mathematical problems that require complex, multi-step logical reasoning. |
|
|
|
|
|
## Model Details |
|
|
|
|
|
For more details, please refer to our [blog post](https://qwenlm.github.io/blog/qwen2-math/) and [GitHub repo](https://github.com/QwenLM/Qwen2-Math). |
|
|
|
|
|
## Requirements |
|
* `transformers>=4.40.0` for Qwen2-Math models. The latest version is recommended. |
|
|
|
> [!Warning] |
|
> <div align="center"> |
|
> <b> |
|
> 🚨 This is a must because `transformers` integrated Qwen2 codes since `4.37.0`. |
|
> </b> |
|
> </div> |
|
|
|
For requirements on GPU memory and the respective throughput, see similar results of Qwen2 [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html). |
|
|
|
> [!Important] |
|
> |
|
> **Qwen2-Math-72B-Instruct** is an instruction model for chatting; |
|
> |
|
> **Qwen2-Math-72B** is a base model typically used for completion and few-shot inference, serving as a better starting point for fine-tuning. |
|
> |
|
|
|
## Citation |
|
|
|
If you find our work helpful, feel free to give us a citation. |
|
|
|
``` |
|
@article{yang2024qwen2, |
|
title={Qwen2 technical report}, |
|
author={Yang, An and Yang, Baosong and Hui, Binyuan and Zheng, Bo and Yu, Bowen and Zhou, Chang and Li, Chengpeng and Li, Chengyuan and Liu, Dayiheng and Huang, Fei and others}, |
|
journal={arXiv preprint arXiv:2407.10671}, |
|
year={2024} |
|
} |
|
``` |
|
|