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model.properties

+# 模型唯一标识
+modelCode=2454
+# 模型名称
+modelName=MiMo-V2.5
+# 模型描述
+modelDescription=MiMo-V2.5 是一个原生全模态模型，具备强大的智能体（agentic）能力，通过统一架构支持文本、图像、视频和音频的理解。
+# 运行过程
+processType=推理
+# 算法类别
+appCategory=多模态
+# 框架类型
+frameType=sglang
+# 加速卡类型
+accelerateType=BW1100

readme.md

+# MiMo-V2.5
+## 论文
+[MiMo-V2-Flash Technical Report](https://mimo.xiaomi.com/mimo-v2-5)
+
+## 模型简介
+MiMo-V2.5 是一个原生全模态模型，具备强大的智能体（agentic）能力，通过统一架构支持文本、图像、视频和音频的理解。该模型基于 MiMo-V2-Flash 主干网络构建，并扩展了专用的视觉和音频编码器，在多模态感知、长上下文推理和智能体工作流方面表现出卓越性能。其主要特性包括：
+
+- 混合注意力架构：继承自 MiMo-V2-Flash 的混合设计，以 5:1 的比例交错使用滑动窗口注意力（SWA）和全局注意力（GA），滑动窗口大小为 128。这使 KV 缓存存储量减少近 6 倍，同时通过可学习的注意力汇聚偏置（attention sink bias）维持长上下文性能。
+- 原生全模态编码器：配备一个 7.29 亿参数的 Vision Transformer（ViT），采用混合窗口注意力机制，并集成一个专用音频编码器（其权重初始化自 MiMo-Audio），从而实现高质量的图像、视频和音频理解。
+- 多 Token 预测（MTP）：包含三个轻量级 MTP 模块，采用密集前馈网络（FFN），通过推测解码（speculative decoding）加速推理，并提升强化学习（RL）训练效率。
+- 高效预训练：使用 FP8 混合精度在总计约 48T tokens 的数据上进行训练，上下文窗口最大支持 1M tokens。
+- 智能体能力：后训练阶段融合了监督微调（SFT）、大规模智能体强化学习（agentic RL）以及多教师在线策略蒸馏（MOPD），在智能体任务和多模态理解基准测试中均取得优异表现。
+
+<div align=center>
+    <img src="./doc/architecture.svg"/>
+</div>
+
+## 环境依赖
+
+| 软件 | 版本 |
+| :------: | :------: |
+| DTK | 26.04 |
+| Python | 3.10.12 |
+| torch | 2.9.0+das.opt1.dtk2604.2604151933.g4ed9ab |
+| triton | 3.3.0+das.opt2.dtk2604.torch290.20260331.g31542e |
+| Transformers | 5.3.0 |
+| SGLang | 0.5.10rc0+das.opt2.alpha.dtk2604.torch290.2604151948.gfdf368 |
+
+当前仅支持镜像: harbor.sourcefind.cn:5443/dcu/admin/base/custom:sglang-0.5.10-mimov2.5-0429
+
+- 挂载地址`-v`根据实际模型情况修改
+
+```bash
+docker run -it \
+    --shm-size 256g \
+    --network=host \
+    --name mimo-v2.5 \
+    --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:sglang-0.5.10-mimov2.5-0429 bash
+```
+更多镜像可前往[光源](https://sourcefind.cn/#/service-list)下载使用。
+
+## 预训练权重
+**请根据`支持的DCU型号`选择对应模型下载，FP8模型仅在BW1100/BW1101上支持，其他型号请勿使用！**
+
+| 模型名称  | 权重大小  | 数据类型 |  支持的DCU型号  | 最低卡数需求 | 下载地址 |
+|:-----:|:----------:|:----------:|:----------:|:---------------------:|:----------:|
+| MiMo-V2.5 | 310B | FP8 | BW1100 | 4 | [ModelScope](https://modelscope.cn/models/XiaomiMiMo/MiMo-V2.5/summary) |
+
+## 数据集
+`暂无`
+
+## 训练
+`暂无`
+
+## 推理
+### SGLang
+#### 单机推理
+1. 启动服务
+
+当前版本除文字推理之外，模态推理暂时有bug，正在积极解决，若有多模态需求，请耐心等待。
+
+用例以最低所需4卡为例，如果需要8卡，需要增加`--dp-size 2`参数。
+
+```bash
+export SGLANG_USE_LIGHTOP=1
+export SGLANG_KV_LAYOUT_DCU_FA=0
+export SGLANG_ENABLE_SPEC_V2=1
+export SGLANG_USE_AITER_FP8_ASM_MOE=1
+export SGLANG_USE_TRITON_EXTEND_FROM_AITER=1
+
+sglang serve --model-path /home/dengjb1/models/XiaomiMiMo/MiMo-V2.5 \
+        --tp-size 4 \
+        --page-size 64 \
+        --host 0.0.0.0 \
+        --port 8001 \
+        --trust-remote-code \
+        --mem-fraction-static 0.85 \
+        --max-running-requests 64 \
+        --tool-call-parser mimo \
+        --context-length 262144 \
+        --attention-backend triton \
+        --chunked-prefill-size -1
+```
+
+2. 访问推理服务
+
+
+```bash
+curl -s -X POST http://localhost:8001/v1/chat/completions   -H "Content-Type: application/json"   -d '{
+    "model": "XiaomiMiMo/MiMo-V2.5",
+    "messages": [{"role": "user", "content": "你叫什么名字哇，你能帮我做什么"}]
+  }'
+```
+
+## 效果展示
+<div align=center>
+    <img src="./doc/results-dcu.png"/>
+</div>
+
+### 精度
+`DCU与GPU精度一致，推理框架：SGLang。`
+
+## 源码仓库及问题反馈
+- https://developer.sourcefind.cn/codes/modelzoo/mimo-v2.5
+
+## 参考资料
+- https://modelscope.cn/models/XiaomiMiMo/MiMo-V2.5/summary