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README.md

+# gemma-4-31B-it
+## 论文
+[gemma-4-31B-it](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/)
+
+## 模型简介
+Gemma 4 模型是多模态模型，用于处理文本和图片输入（小型模型支持音频输入）以及生成文本输出。此版本包含预训练和指令调优的开放权重模型。Gemma 4 的上下文窗口最多可容纳 25.6 万个 token，并支持 140 多种语言。
+
+Gemma 4 同时采用密集型架构和混合专家 (MoE) 架构，非常适合文本生成、编码和推理等任务。这些模型有四种不同的规模：E2B、E4B、26B A4B 和 31B。这些模型的大小各异，因此可部署在从高端手机到笔记本电脑和服务器的各种环境中，从而让更多人能够使用最先进的 AI。
+
+Gemma 4 引入了关键的功能和架构改进：
+- **推理** - 该系列中的所有模型都设计为高能力推理器，具有可配置的思考模式。
+
+- **扩展的多模态功能** - 处理文本、支持可变宽高比和分辨率的图片（所有型号）、视频和音频（在 E2B 和 E4B 型号上原生支持）。
+
+- **多样化且高效的架构** - 提供不同大小的密集型和混合专家 (MoE) 变体，以实现可伸缩的部署。
+
+- **针对设备端进行了优化** - 较小的模型专为在笔记本电脑和移动设备上高效本地执行而设计。
+
+- **更大的上下文窗口** - 小型模型的上下文窗口为 12.8 万个 token，中型模型的上下文窗口为 25.6 万个 token。
+
+- **增强的编码和智能体功能** - 在编码基准方面取得了显著改进，同时支持原生函数调用，可打造功能强大的自主代理。
+
+- **原生系统提示支持** - Gemma 4 引入了对 system 角色的原生支持，从而实现更结构化和可控的对话。
+
+## 环境依赖
+| 软件 |   版本    |
+| :------: |:-------:|
+| DTK |  26.04  |
+| python | 3.10.12 |
+| transformers |  5.5.0  |
+| vllm |   0.18.1+das.3266200.dtk2604   |
+| triton |   3.4.0+git1ef59765    |
+| torch |   2.10.0+das.opt1.dtk2604.20260325.g6b060a   |
+
+当前仅支持定制镜像: harbor.sourcefind.cn:5443/dcu/admin/base/custom:vllm018-ubuntu22.04-dtk26.04-gemma4-0413
+
+- 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 gemma-4 \
+    --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:vllm018-ubuntu22.04-dtk26.04-gemma4-0413 bash
+```
+更多镜像可前往[光源](https://sourcefind.cn/#/service-list)下载使用。
+
+关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.sourcefind.cn/tool/)开发者社区下载安装。
+```
+
+## 数据集
+暂无
+
+## 训练
+暂无
+
+## 推理
+### vllm
+#### 单机推理
+
+启动vllm server
+
+```bash
+## serve启动
+vllm serve google/gemma-4-31B-it \
+    -tp 2 \
+    --attention-backend TRITON_ATTN \
+    --trust-remote-code \
+    --enable-prefix-caching \
+    --enable-chunked-prefill \
+    --reasoning-parser gemma4 \
+    --tool-call-parser gemma4  \
+    --enable-auto-tool-choice
+
+## client访问
+curl http://localhost:8000/v1/chat/completions   \
+    -H "Content-Type: application/json"  \
+    -d '{
+        "model": "google/gemma-4-31B-it",
+        "messages": [
+          {"role": "user", "content": "which one is bigger, 9.11 or 9.9? think carefully"}
+        ],
+        "temperature": 0.6
+    }'
+```
+
+## 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          |
+|:------:|:----:|:------:|:------:|:---------------------:|
+| gemma-4-31B-it | 33B | BW1000 |   2    | [Hugging Face](https://huggingface.co/google/gemma-4-31B-it) |
+
+## 源码仓库及问题反馈
+- https://developer.sourcefind.cn/codes/modelzoo/gemma-4-31b-it_vllm
+
+## 参考资料
+- https://github.com/google-gemma

model.properties

+# 模型唯一标识
+modelCode=2384
+# 模型名称
+modelName=gemma-4-31B-it_vllm
+# 模型描述
+modelDescription=Gemma 4 模型旨在在每个尺寸级别上都达到前沿性能，覆盖从移动和边缘设备（E2B、E4B）到消费级 GPU 和工作站（26B A4B、31B）的多种部署场景。
+# 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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