---
language:
- zh
- en
tags:
- glm
- visualglm
- chatglm
- thudm
---
# VisualGLM-6B
💻 Github Repo • 🐦 Twitter • 📃 [GLM@ACL 22] [GitHub] • 📃 [GLM-130B@ICLR 23] [GitHub]
👋 Join our Slack and WeChat
## 介绍
VisualGLM-6B 是一个开源的，支持**图像、中文和英文**的多模态对话语言模型，语言模型基于 [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B)，具有 62 亿参数；图像部分通过训练 [BLIP2-Qformer](https://arxiv.org/abs/2301.12597) 构建起视觉模型与语言模型的桥梁，整体模型共78亿参数。
VisualGLM-6B 依靠来自于 [CogView](https://arxiv.org/abs/2105.13290) 数据集的30M高质量中文图文对，与300M经过筛选的英文图文对进行预训练，中英文权重相同。该训练方式较好地将视觉信息对齐到ChatGLM的语义空间；之后的微调阶段，模型在长视觉问答数据上训练，以生成符合人类偏好的答案。
## 软件依赖
```shell
pip install SwissArmyTransformer>=0.3.6 torch>1.10.0 torchvision transformers>=4.27.1 cpm_kernels
```
## 代码调用
可以通过如下代码调用 VisualGLM-6B 模型来生成对话：
```ipython
>>> from transformers import AutoTokenizer, AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained("THUDM/visualglm-6b", trust_remote_code=True)
>>> model = AutoModel.from_pretrained("THUDM/visualglm-6b", trust_remote_code=True).half().cuda()
>>> image_path = "your image path"
>>> response, history = model.chat(tokenizer, image_path, "描述这张图片。", history=[])
>>> print(response)
>>> response, history = model.chat(tokenizer, image_path, "这张图片可能是在什么场所拍摄的？", history=history)
>>> print(response)
```
关于更多的使用说明，包括如何运行命令行和网页版本的 DEMO，以及使用模型量化以节省显存，请参考我们的 [Github Repo](https://github.com/THUDM/VisualGLM-6B)。
For more instructions, including how to run CLI and web demos, and model quantization, please refer to our [Github Repo](https://github.com/THUDM/VisualGLM-6B).
## 协议
本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源，VisualGLM-6B 模型的权重的使用则需要遵循 [Model License](MODEL_LICENSE)。
## 引用
如果你觉得我们的工作有帮助的话，请考虑引用下列论文：
If you find our work helpful, please consider citing the following paper.
```
@misc{glm2024chatglm,
title={ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools},
author={Team GLM and Aohan Zeng and Bin Xu and Bowen Wang and Chenhui Zhang and Da Yin and Diego Rojas and Guanyu Feng and Hanlin Zhao and Hanyu Lai and Hao Yu and Hongning Wang and Jiadai Sun and Jiajie Zhang and Jiale Cheng and Jiayi Gui and Jie Tang and Jing Zhang and Juanzi Li and Lei Zhao and Lindong Wu and Lucen Zhong and Mingdao Liu and Minlie Huang and Peng Zhang and Qinkai Zheng and Rui Lu and Shuaiqi Duan and Shudan Zhang and Shulin Cao and Shuxun Yang and Weng Lam Tam and Wenyi Zhao and Xiao Liu and Xiao Xia and Xiaohan Zhang and Xiaotao Gu and Xin Lv and Xinghan Liu and Xinyi Liu and Xinyue Yang and Xixuan Song and Xunkai Zhang and Yifan An and Yifan Xu and Yilin Niu and Yuantao Yang and Yueyan Li and Yushi Bai and Yuxiao Dong and Zehan Qi and Zhaoyu Wang and Zhen Yang and Zhengxiao Du and Zhenyu Hou and Zihan Wang},
year={2024},
eprint={2406.12793},
archivePrefix={arXiv},
primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}
}
```
```
@misc{wang2023cogvlm,
title={CogVLM: Visual Expert for Pretrained Language Models},
author={Weihan Wang and Qingsong Lv and Wenmeng Yu and Wenyi Hong and Ji Qi and Yan Wang and Junhui Ji and Zhuoyi Yang and Lei Zhao and Xixuan Song and Jiazheng Xu and Bin Xu and Juanzi Li and Yuxiao Dong and Ming Ding and Jie Tang},
year={2023},
eprint={2311.03079},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```