Add long cat

Contributors.md

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README.md

 # LongCat_sglang
 
-美团开源模型龙猫，一个强大且高效的语言模型，拥有总计 5600 亿个参数，采用了创新的专家混合（MoE）架构。
+## 论文
\ No newline at end of file
+[LongCat-Flash Technical Report](https://github.com/meituan-longcat/LongCat-Flash-Chat/blob/main/tech_report.pdf)
+
+## 模型结构
+美团开源模型龙猫，一个强大且高效的语言模型，拥有总计 5600 亿个参数，采用了创新的专家混合（MoE）架构。该模型采用了一种动态计算机制，能够根据上下文需求激活 186 亿至 313 亿个参数（平均约 270 亿个），从而在计算效率和性能之间实现了优化。
+
+<div align=center>
+    <img src="./doc/model.png"/>
+</div>
+
+## 算法原理
+LongCat-Flash 是基于两个关键原则进行设计和优化的：高效计算利用以及高效训练和推理。具体而言:
+
+（1）由于并非所有标记都同等重要，在 MoE 块中引入了零计算专家机制，根据标记的重要性动态分配计算预算，即根据上下文需求激活 186 亿至 313 亿个参数（总参数量为 5600 亿）。为确保计算负载一致，采用由 PID 控制器调整的专家偏差，使每个标记平均激活约 270 亿个参数。
+
+（2）由于在 MoE 模型扩展过程中通信开销成为瓶颈，引入了快捷连接的 MoE（ScMoE）设计，以扩大计算与通信的重叠窗口。结合定制的基础设施优化，该设计能够支持在数万个加速器上进行大规模训练，并实现高吞吐量和低延迟的推理。
+
+<div align=center>
+    <img src="./doc/methods.png"/>
+</div>
+
+## 环境配置
+### 硬件需求
+DCU型号：K100_AI,节点数量：4台,卡数：32 张。
+
+`-v 路径`、`docker_name`和`imageID`根据实际情况修改
+
+### Docker（方法一）
+```bash
+docker pull image.sourcefind.cn:5000/dcu/admin/base/vllm:0.9.2-ubuntu22.04-dtk25.04.1-rc5-rocblas101839-0811-das1.6-py3.10-20250812-beta
+docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
+
+## 安装sglang
+git clone https://developer.sourcefind.cn/codes/OpenDAS/sglang
+
+cd sglang
+# 编译安装sgl-kernel包
+cd sgl-kernel
+python setup_hip.py install
+# 安装sglang Python依赖
+cd ..
+pip install -e "python[all_hip]"
+pip install transformers==4.56.0
+
+cd /your_code_path/longcat_sglang
+```
+
+### Dockerfile（方法二）
+```bash
+cd docker
+docker build --no-cache -t longcat:latest .
+docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
+
+## 安装sglang
+git clone https://developer.sourcefind.cn/codes/OpenDAS/sglang
+
+cd sglang
+# 编译安装sgl-kernel包
+cd sgl-kernel
+python setup_hip.py install
+# 安装sglang Python依赖
+cd ..
+pip install -e "python[all_hip]"
+pip install transformers==4.56.0
+
+cd /your_code_path/longcat_sglang
+```
+
+### Anaconda（方法三）
+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。
+```bash
+DTK: 25.04.1
+python: 3.10.12
+torch: 2.5.1+das.opt1.dtk25041
+deepspeed: 0.14.2+das.opt1.dtk25041
+transformers: 4.56.0
+```
+`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应`
+
+``` bash
+## 安装sglang
+git clone https://developer.sourcefind.cn/codes/OpenDAS/sglang
+
+cd sglang
+# 编译安装sgl-kernel包
+cd sgl-kernel
+python setup_hip.py install
+# 安装sglang Python依赖
+cd ..
+pip install -e "python[all_hip]"
+pip install transformers==4.56.0
+```
+
+## 数据集
+无
+
+## 训练
+暂无
+
+## 推理
+### sglang推理方法
+#### server 多机
+样例模型：[LongCat-Flash-Chat](https://huggingface.co/meituan-longcat/LongCat-Flash-Chat)
+
+启动sglang server，每个节点都需要启动
+> 参数说明：
+> x.x.x.x 对应主节点的IP
+>
+> 主节点NODE_RANK为0，工作节点分别为1，2，3
+>
+> nnodes 节点数量, 每个节点都需要执行下面脚本，NODE_RANK根据每个节点输入
+
+主节点启动示例：
+```bash
+bash start_sglang.sh 0
+```
+
+启动完成后可通过以下方式访问：
+```bash
+curl http://127.0.0.2:8001/v1/chat/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "meituan-longcat/LongCat-Flash-Chat",
+        "messages": [
+            {
+                "role": "user",
+                "content": "Explain Machine Learning to me in a nutshell."
+            }
+        ],
+        "temperature": 0.15,
+        "top_p": 1.0,
+        "max_tokens": 2048,
+        "stream": false
+}'
+```
+
+## result
+<div align=center>
+    <img src="./doc/results-dcu.png"/>
+</div>
+
+### 精度
+DCU与GPU精度一致，推理框架：sglang。
+
+## 应用场景
+### 算法类别
+对话问答
+
+### 热点应用行业
+制造,广媒,家居,教育
+
+## 预训练权重
+- [LongCat-Flash-Chat](https://huggingface.co/meituan-longcat/LongCat-Flash-Chat)
+
+## 源码仓库及问题反馈
+- https://developer.sourcefind.cn/codes/modelzoo/longcat_sglang
+
+## 参考资料
+- https://github.com/meituan-longcat/LongCat-Flash-Chat

docker/Dockerfile

+FROM image.sourcefind.cn:5000/dcu/admin/base/vllm:0.9.2-ubuntu22.04-dtk25.04.1-rc5-rocblas101839-0811-das1.6-py3.10-20250812-beta
\ No newline at end of file

model.properties

+# 模型唯一标识
+modelCode=1723
+# 模型名称
+modelName=longcat_sglang
+# 模型描述
+modelDescription=美团开源模型龙猫，一个强大且高效的语言模型，拥有总计 5600 亿个参数，采用了创新的专家混合（MoE）架构。
+# 应用场景
+appScenario=推理,对话问答,制造,广媒,家居,教育
+# 框架类型
+frameType=sglang

start_sglang.sh

+export HSA_FORCE_FINE_GRAIN_PCIE=1
+export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export NCCL_IB_HCA=mlx5_0:1
+
+NODE_RANK=$1
+
+python3 -m sglang.launch_server \
+    --model meituan-longcat/LongCat-Flash-Chat \
+    --trust-remote-code \
+    --dtype bfloat16 \
+    --enable-ep-moe \
+    --tp 32 \
+    --nnodes 4 \
+    --node-rank $NODE_RANK \
+    --host 127.0.0.2 \
+    --port 8001 \
+    --dist-init-addr x.x.x.x:20000
\ No newline at end of file