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# GLM-4.6V
## 论文
[GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning](https://arxiv.org/abs/2507.01006)
## 模型简介
GLM-4.6V 系列模型包含两个版本：GLM-4.6V（106B），一款专为云端和高性能集群场景设计的基础模型；以及 GLM-4.6V-Flash（9B），一款针对本地部署和低延迟应用优化的轻量级模型。
GLM-4.6V 在训练中将上下文窗口扩展至 128k tokens，并在相近参数规模的模型中实现了视觉理解能力的最先进（SoTA）性能。
更重要的是，该模型首次集成了原生函数调用（Function Calling）能力，有效弥合了“视觉感知”与“可执行动作”之间的鸿沟，为现实业务场景中的多模态智能体提供了统一的技术基础。
<div align=center>
<img src="./doc/bench_46v.jpeg"/>
</div>
除在主流多模态基准测试中达到同规模模型的 SoTA 性能外，GLM-4.6V 还引入了若干关键特性：
- 原生多模态函数调用
支持原生的视觉驱动工具调用。图像、截图和文档页面可直接作为工具输入，无需转换为文本；同时，视觉输出（如图表、搜索图片、渲染页面）可被模型解析并整合进推理链路，从而实现从感知到理解再到执行的闭环。
- 交错式图文内容生成
支持基于复杂多模态输入生成高质量混合媒体内容。GLM-4.6V 能够处理包含文档、用户输入和工具检索图像在内的多模态上下文，并合成任务导向的、图文交错的连贯内容。在生成过程中，模型还能主动调用搜索与检索工具，收集并筛选额外的文本与视觉素材，产出丰富且视觉锚定的内容。
- 多模态文档理解
GLM-4.6V 可处理最多 128K tokens 的多文档或长文档输入，直接将格式丰富的页面作为图像进行解析。它能联合理解文本、版式、图表、表格和插图，无需事先转换为纯文本，即可准确理解复杂且富含图像的文档。
- 前端复现与视觉编辑
可从 UI 截图重建像素级精确的 HTML/CSS 代码，并支持自然语言驱动的编辑。模型通过视觉方式识别布局、组件和样式，生成干净的代码，并可通过简单的用户指令进行迭代式的视觉修改。
## 环境依赖
| 软件 | 版本 |
| :------: | :------: |
| DTK | 26.04 |
| python | 3.10.12 |
| torch | 2.9.0+das.opt1.dtk2604.20260206.g275d08c2 |
| transformers | 5.2.0 |
| vllm | 0.15.1+das.opt1.alpha.dtk2604.20260220.g2799735a |
推荐使用镜像: harbor.sourcefind.cn:5443/dcu/admin/base/custom:vllm0.15.1-ubuntu22.04-dtk26.04-0130-py3.10-20260220
- 挂载地址`-v`根据实际模型情况修改
```bash
docker run -it \
--shm-size 200g \
--network=host \
--name glm-4.6v \
--privileged \
--device=/dev/kfd \
--device=/dev/dri \
--device=/dev/mkfd \
--group-add video \
--cap-add=SYS_PTRACE \
--security-opt seccomp=unconfined \
-u root \
-v /opt/hyhal/:/opt/hyhal/:ro \
-v /path/your_code_data/:/path/your_code_data/ \
harbor.sourcefind.cn:5443/dcu/admin/base/custom:vllm0.15.1-ubuntu22.04-dtk26.04-0130-py3.10-20260220 bash
```
更多镜像可前往[光源](https://sourcefind.cn/#/service-list)下载使用，其它包安装：
```
pip install pycountry
pip install -U transformers
```
## 数据集
`暂无`
## 训练
`暂无`
## 推理
### vllm
#### 单机推理
```bash
## serve启动
export ALLREDUCE_STREAM_WITH_COMPUTE=1
export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
export MOE_NN=0
vllm serve ZhipuAI/GLM-4.6V \
--tensor-parallel-size 8 \
--gpu-memory-utilization 0.95 \
--port 8001 \
--max-model-len 32768 \
--tool-call-parser glm45 \
--reasoning-parser glm45 \
--enable-auto-tool-choice \
--allowed-local-media-path / \
--mm-encoder-tp-mode data \
--mm_processor_cache_type shm \
--served-model-name GLM-4.6V
## client访问
curl http://localhost:8001/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "GLM-4.6V",
"messages": [
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "file:///path/of/doc/Grayscale_8bits_palette_sample_image.png"
}
},
{
"type": "text",
"text": "请描述这张图片的内容。"
}
]
}
],
"temperature": 0.8,
"chat_template_kwargs": {
"thinking": true
}
}'
```
## 效果展示
<div align=center>
<img src="./doc/result-dcu.png"/>
</div>
### 精度
`DCU与GPU精度一致，推理框架：vllm。`
## 预训练权重
| 模型名称 | 权重大小 | DCU型号 | 最低卡数需求 |下载地址|
|:-----:|:----------:|:----------:|:---------------------:|:----------:|
| GLM-4.6V | 106B-A12B | BW1000 | 8 | [Modelscope](https://modelscope.cn/models/ZhipuAI/GLM-4.6V) |
| GLM-4.6V-Flash | 9B | BW1000 | 1 | [Modelscope](https://modelscope.cn/models/ZhipuAI/GLM-4.6V-Flash) |
## 源码仓库及问题反馈
- https://developer.sourcefind.cn/codes/modelzoo/glm-4.6v_vllm
## 参考资料
- https://github.com/zai-org/GLM-V
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# 模型唯一标识
modelCode=2142
# 模型名称
modelName=glm-4.6v_vllm
# 模型描述
modelDescription=GLM-4.6V 首次集成了原生函数调用（Function Calling）能力，有效弥合了“视觉感知”与“可执行动作”之间的鸿沟，为现实业务场景中的多模态智能体提供了统一的技术基础。
# 运行过程
processType=推理
# 算法类别
appCategory=对话问答
# 框架类型
frameType=vllm
# 加速卡类型
accelerateType=BW1000
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