First add

Contributors.md

+# Contributors
+None
\ No newline at end of file

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README.md

+# Qwen3-Next
+
+## 论文
+[Qwen3-Next](https://qwen.ai/blog?id=4074cca80393150c248e508aa62983f9cb7d27cd&from=research.latest-advancements-list)
+
+## 模型结构
+Qwen3-Next-80B-A3B 是 Qwen3-Next 系列的第一个版本，具有以下关键增强功能：
+
+- **混合注意力机制**：用 Gated DeltaNet 和 Gated Attention 的组合取代标准注意力机制，从而能够高效地对超长上下文进行建模。
+
+- **高稀疏度混合专家（MoE）**：在 MoE 层中实现了极低的激活比率，大幅减少了每个 token 的 FLOPs，同时保持了模型容量。
+
+- **稳定性优化**：包括**零中心化和权重衰减的层归一化**以及其他稳定增强技术，以实现稳健的预训练和后训练。
+
+- **Multi-Token Prediction (MTP)**：提升预训练模型性能并加速推理，但是其在 Hugging Face Transformers 中通常不可用。
+
+<div align=center>
+    <img src="./doc/model.png"/>
+</div>
+
+## 算法原理
+Qwen3-Next在模型架构上实现了重大突破，引入了注意力机制方面的多项创新，包括线性注意力和注意力门控机制，并进一步提高了MoE 的稀疏度。Qwen3-Next-80B-A3B 在思考模式和非思考模式下的性能均与规模更大的 Qwen3-235B-A22B-2507 相当，同时在推理速度上，特别是长上下文场景中，提升显著。
+
+Qwen3-Next-80B-A3B 有两个版本：`Qwen3-Next-80B-A3B-Instruct` 和 `Qwen3-Next-80B-A3B-Thinking`。
+
+> 注意：
+> Qwen3-Next-80B-A3B-Instruct 仅支持指令（非思考）模式，并且在其输出中不会生成 <think></think> 块。
+>
+> Qwen3-Next-80B-A3B-Thinking 仅支持思考模式。 为了强制模型思考，默认的聊天模板自动包含 <think>。 因此，模型的输出可能只包含 </think> 而没有显式的 <think> 开始标签，这是正常的。
+
+## 环境配置
+### 硬件需求
+DCU型号：K100_AI,节点数量：1台,卡数：4 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-py3.10-20250908-rc1
+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 -b v0.5.2 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]"
+
+cd /your_code_path/qwen3-next_sglang
+```
+
+### Dockerfile（方法二）
+```bash
+cd docker
+docker build --no-cache -t qwen3-next: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 -b v0.5.2 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]"
+
+cd /your_code_path/qwen3-next_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: 4.56.1
+```
+`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 -b v0.5.2 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]"
+```
+
+## 数据集
+无
+
+## 训练
+暂无
+
+## 推理
+### sglang推理方法
+#### server
+样例模型：[Qwen3-Next-80B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct)
+
+启动服务：
+```bash
+bash start_sglang.sh
+```
+
+启动完成后可通过以下方式访问：
+```bash
+curl http://127.0.0.1:8001/v1/chat/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "Qwen/Qwen3-Next-80B-A3B-Instruct",
+        "messages": [
+            {
+                "role": "user",
+                "content": "Explain Machine Learning to me in a nutshell."
+            }
+        ],
+        "temperature": 0.7,
+        "top_p": 0.8,
+        "top_k": 20,
+        "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
+- [Qwen3-Next-80B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct)
+- [Qwen3-Next-80B-A3B-Thinking](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking)
+
+## 源码仓库及问题反馈
+- https://developer.sourcefind.cn/codes/modelzoo/qwen3-next_sglang
+
+## 参考资料
+- https://qwen.ai/blog?id=4074cca80393150c248e508aa62983f9cb7d27cd&from=research.latest-advancements-list

docker/Dockerfile

+FROM image.sourcefind.cn:5000/dcu/admin/base/vllm:0.9.2-ubuntu22.04-dtk25.04.1-rc5-rocblas101839-0811-das1.6-py3.10-20250908-rc1
\ No newline at end of file

model.properties

+# 模型唯一标识
+modelCode=1736
+# 模型名称
+modelName=qwen3-next_sglang
+# 模型描述
+modelDescription=为了进一步提升模型在长上下文和大规模总参数下的训练和推理效率，Qwen团队设计了全新的Qwen3-Next的模型结构。
+# 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
+export SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1
+
+python3 -m sglang.launch_server \
+    --model Qwen/Qwen3-Next-80B-A3B-Instruct \
+    --tp 4 \
+    --dtype bfloat16 \
+    --port 8001
\ No newline at end of file