Qwen2-Math-1.5B-GGUF

QuantFactory/Qwen2-Math-1.5B-GGUF

This is quantized version of Qwen/Qwen2-Math-1.5B created using llama.cpp

Original Model Card

Qwen2-Math-1.5B

[!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 and GitHub repo.

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.

[!Important]

Qwen2-Math-1.5B-Instruct is an instruction model for chatting;

Qwen2-Math-1.5B 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}
}