QuantFactory/Qwen2.5-Math-7B-GGUF

base_model: Qwen/Qwen2.5-7B language:

• —en pipelinetag: text-generation libraryname: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-Math-7B/blob/main/LICENSE


QuantFactory/Qwen2.5-Math-7B-GGUF

This is quantized version of Qwen/Qwen2.5-Math-7B created using llama.cpp

Original Model Card

Qwen2.5-Math-7B

[!Warning] <div align="center"> <b> 🚨 Qwen2.5-Math mainly supports solving English and Chinese math problems through CoT and TIR. We do not recommend using this series of models for other tasks. </b> </div>

Introduction

In August 2024, we released the first series of mathematical LLMs - Qwen2-Math - of our Qwen family. A month later, we have upgraded it and open-sourced Qwen2.5-Math series, including base models Qwen2.5-Math-1.5B/7B/72B, instruction-tuned models Qwen2.5-Math-1.5B/7B/72B-Instruct, and mathematical reward model Qwen2.5-Math-RM-72B.

Unlike Qwen2-Math series which only supports using Chain-of-Thught (CoT) to solve English math problems, Qwen2.5-Math series is expanded to support using both CoT and Tool-integrated Reasoning (TIR) to solve math problems in both Chinese and English. The Qwen2.5-Math series models have achieved significant performance improvements compared to the Qwen2-Math series models on the Chinese and English mathematics benchmarks with CoT.

While CoT plays a vital role in enhancing the reasoning capabilities of LLMs, it faces challenges in achieving computational accuracy and handling complex mathematical or algorithmic reasoning tasks, such as finding the roots of a quadratic equation or computing the eigenvalues of a matrix. TIR can further improve the model's proficiency in precise computation, symbolic manipulation, and algorithmic manipulation. Qwen2.5-Math-1.5B/7B/72B-Instruct achieve 79.7, 85.3, and 87.8 respectively on the MATH benchmark using TIR.

Model Details

For more details, please refer to our blog post and GitHub repo.

Requirements

• —transformers>=4.37.0 for Qwen2.5-Math models. The latest version is recommended.

[!Warning] <div align="center"> <b> 🚨 This is a must because <code>transformers</code> integrated Qwen2 codes since <code>4.37.0</code>. </b> </div>

For requirements on GPU memory and the respective throughput, see similar results of Qwen2 here.

Quick Start

[!Important] Qwen2.5-Math-7B-Instruct is an instruction model for chatting; Qwen2.5-Math-7B 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}
}