---
title: CodeQwen1.5-7B-Chat-GGUF
canonical_url: "https://www.modelscope.cn/models/Qwen/CodeQwen1.5-7B-Chat-GGUF"
md_url: "https://www.modelscope.cn/models/Qwen/CodeQwen1.5-7B-Chat-GGUF.md"
repository: Qwen/CodeQwen1.5-7B-Chat-GGUF
last_updated: 2025-02-26
license: other
pipeline_tag: text-generation
tasks:
  - text-generation
library_name:
  - gguf
  - pytorch
frameworks:
  - pytorch
language:
  - en
downloads: 5409
stars: 17
tags:
  - chat
  - gguf
---

# CodeQwen1.5-7B-Chat-GGUF

> CodeQwen1.5-7B-Chat-GGUF - Qwen 在 ModelScope 开源的模型。代码专家模型 CodeQwen1.5! CodeQwen1.5 基于 Qwen 语言模型初始化，拥有 7B 参数的模型，其拥有 GQA 架构，经过了 ~3T tokens 代码相关的数据进行预训练，共计支持 92 种编程语言、且最长支持 64K 的上下文输入。效果方面，CodeQwen1.5 展现出了非凡的代码生成、长序列建模、代码修改、SQL…

Qwen/CodeQwen1.5-7B-Chat-GGUF 是 ModelScope 魔搭社区上的text-generation模型，采用 other 许可。

- **Repository**: Qwen/CodeQwen1.5-7B-Chat-GGUF
- **License**: other
- **Tasks**: text-generation
- **Tags**: chat, gguf
- **Downloads**: 5409
- **Stars**: 17
- **Last updated**: 2025-02-26

Source: https://www.modelscope.cn/models/Qwen/CodeQwen1.5-7B-Chat-GGUF

---

# CodeQwen1.5-7B-Chat-GGUF


## Introduction

CodeQwen1.5 is the Code-Specific version of Qwen1.5. It is a transformer-based decoder-only language model pretrained on a large amount of data of codes. 

* Strong code generation capabilities and competitve performance across a series of benchmarks;
* Supporting long context understanding and generation with the context length of 64K tokens;
* Supporting 92 coding languages
* Excellent performance in text-to-SQL, bug fix, etc.


For more details, please refer to our [blog post](https://qwenlm.github.io/blog/codeqwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5). 
In this repo, we provide quantized models in the GGUF formats, including `q2_k`, `q3_k_m`, `q4_0`, `q4_k_m`, `q5_0`, `q5_k_m`, `q6_k` and `q8_0`. 


## Model Details
CodeQwen1.5 is based on Qwen1.5, a language model series including decoder language models of different model sizes. It is trained on 3 trillion tokens of data of codes, and it includes group query attention (GQA) for efficient inference.

## Requirements
We advise you to clone [`llama.cpp`](https://github.com/ggerganov/llama.cpp) and install it following the official guide.


## How to use
Cloning the repo may be inefficient, and thus you can manually download the GGUF file that you need or use `huggingface-cli` (`pip install huggingface_hub`) as shown below:
```shell
huggingface-cli download Qwen/CodeQwen1.5-7B-Chat-GGUF codeqwen-1_5-7b-chat-q5_k_m.gguf --local-dir . --local-dir-use-symlinks False
```

We demonstrate how to use `llama.cpp` to run Qwen1.5:
```shell
./main -m codeqwen-1_5-7b-chat-q5_k_m.gguf -n 512 --color -i -cml -f prompts/chat-with-qwen.txt
```


## Citation

If you find our work helpful, feel free to give us a cite.

```
@article{qwen,
  title={Qwen Technical Report},
  author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu},
  journal={arXiv preprint arXiv:2309.16609},
  year={2023}
}
```