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README.md

-# GLM-5
+## GLM-5
 
+## 论文
+
+[Qwen3-VL Technical Report](https://arxiv.org/pdf/2511.21631)
+
+## 模型简介
+- 作为智谱AI新一代旗舰大模型，GLM-5在Coding与Agent能力上取得开源 SOTA 表现，在真实编程场景的使用体感逼近Claude Opus 4.5，擅长复杂系统工程与长程Agent任务：
+
+  
+
+  - 参数规模从355B（激活32B）扩展至744B（激活40B），预训练数据从23T提升至28.5T，大幅提升通用智能水平；
+  - 构建全新“Slime”异步强化学习框架，支持更大模型规模及更复杂强化学习任务，可通过异步智能体强化学习算法持续从长程交互中学习；
+  - 首次集成DeepSeek Sparse Attention稀疏注意力机制，在维持长文本效果无损的同时，大幅降低模型部署成本、提升Token Efficiency，完全满足产业级部署需求。
+<div align=center>
+    <img src="./doc/qwen3vl_arc.jpg"/>
+</div>
+
+1. **Interleaved-MRoPE**: 通过稳健的位置嵌入，在时间、宽度和高度上进行全频分配，增强长时程视频推理。
+
+2. **DeepStack**: 融合多级 ViT 特征，以捕捉精细细节并增强图像与文本的对齐。
+
+3. **Text–Timestamp Alignment:** 超越 T-RoPE，实现基于时间戳的精确事件定位，从而增强视频时间建模能力。
+## 环境依赖
+
+|     软件     |                       版本                        |
+| :----------: | :-----------------------------------------------: |
+|     DTK      |                      26.04.2                      |
+|    python    |                      3.10.12                      |
+| transformers |                      4.57.6                       |
+|    torch     |              2.5.1+das.opt1.dtk2604.20260116.g78471bfd              |
+|  accelerate  |                      1.12.0                       |
+| torchvision  |             0.20.1+das.opt1.dtk2604.20260116.g65c66897              |
+|  flash_attn  |              2.6.1+das.opt1.dtk2504               |
+|     vllm     | 0.11.0+das.opt1.alpha.dtk25042.20251225.gca4598a4 |
+
+推荐使用镜像:harbor.sourcefind.cn:5443/dcu/admin/base/vllm:0.11.0-ubuntu22.04-dtk25.04.2-1226-das1.7-py3.10-20251226
+
+- 挂载地址`-v`根据实际模型情况修改
+
+```bash
+docker run -it \
+    --shm-size 60g \
+    --network=host \
+    --name qwen3 \
+    --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/vllm:0.11.0-ubuntu22.04-dtk25.04.2-1226-das1.7-py3.10-20251226
+```
+
+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，其它包参照requirements.txt安装：
+
+```
+pip install -r requirements.txt
+```
+
+## 数据集
+
+`暂无`
+
+## 训练
+`暂无`
+
+## 推理
+
+### transformers
+#### 单机推理
+```bash
+#普通图文对话
+HIP_VISIBLE_DEVICES=0 python qwen3vl_infer.py
+#多图像推理
+HIP_VISIBLE_DEVICES=0 python qwen3vl_infer_multi_images.py
+#视频推理
+HIP_VISIBLE_DEVICES=0 python qwen3vl_infer_video.py
+```
+
+## vllm
+
+### 多机推理
+
+1. 加入环境变量
+> 请注意：
+> 每个节点上的环境变量都写到.sh文件中，保存后各个计算节点分别source`.sh`文件
+>
+> VLLM_HOST_IP：节点本地通信口ip，尽量选择IB网卡的IP，**避免出现rccl超时问题**
+>
+> NCCL_SOCKET_IFNAME和 GLOO_SOCKET_IFNAME：节点本地通信网口ip对应的名称
+>
+> 通信口和ip查询方法：ifconfig
+>
+> IB口状态查询：ibstat  !!!一定要active激活状态才可用，各个节点要保持统一
+
+```bash
+export ALLREDUCE_STREAM_WITH_COMPUTE=1
+export VLLM_HOST_IP=x.x.x.x # 对应计算节点的IP，选择IB口SOCKET_IFNAME对应IP地址
+export NCCL_SOCKET_IFNAME=ibxxxx
+export GLOO_SOCKET_IFNAME=ibxxxx
+export NCCL_IB_HCA=mlx5_0:1 # 环境中的IB网卡名字
+unset NCCL_ALGO
+export NCCL_MIN_NCHANNELS=16
+export NCCL_MAX_NCHANNELS=16
+export NCCL_NET_GDR_READ=1
+export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export VLLM_SPEC_DECODE_EAGER=1
+export VLLM_MLA_DISABLE=0
+export VLLM_USE_FLASH_MLA=1
+
+# K100_AI集群建议额外设置的环境变量：
+export VLLM_ENFORCE_EAGER_BS_THRESHOLD=44
+export VLLM_RPC_TIMEOUT=1800000
+
+# 海光CPU绑定核
+export VLLM_NUMA_BIND=1
+export VLLM_RANK0_NUMA=0
+export VLLM_RANK1_NUMA=1
+export VLLM_RANK2_NUMA=2
+export VLLM_RANK3_NUMA=3
+export VLLM_RANK4_NUMA=4
+export VLLM_RANK5_NUMA=5
+export VLLM_RANK6_NUMA=6
+export VLLM_RANK7_NUMA=7
+```
+
+2. 启动RAY集群
+> x.x.x.x 对应第一步 VLLM_HOST_IP
+
+```bash
+# head节点执行
+ray start --head --node-ip-address=x.x.x.x --port=6379 --num-gpus=8 --num-cpus=32
+# worker节点执行
+ray start --address='x.x.x.x:6379' --num-gpus=8 --num-cpus=32
+```
+3. 启动vllm server
+
+```bash
+vllm serve Qwen/GLM-5 \
+  --host x.x.x.x \
+  --port 8000 \
+  --distributed-executor-backend ray \
+  --tensor-parallel-size 8 \
+  --pipeline-parallel-size 2 \
+  --trust-remote-code \
+  --dtype bfloat16 \
+  --max-model-len 32768 \
+  --max-num-seqs 128 \
+  --block-size 64 \
+  --gpu-memory-utilization 0.90 \
+  --allowed-local-media-path / \
+  --served-model-name GLM-5
+```
+
+启动完成后可通过以下方式访问：
+```bash
+# /path/to/your/project 请改为图像文件存储的目录
+curl http://x.x.x.x:8000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "qwen-vl",
+    "messages": [
+      {
+        "role": "user",
+        "content": [
+          {
+            "type": "image_url",
+            "image_url": {
+              "url": "file:///path/to/your/project/doc/dog.png"
+            }
+          },
+          {
+            "type": "text",
+            "text": "请详细描述这张图片的内容。"
+          }
+        ]
+      }
+    ],
+    "max_tokens": 512,
+    "temperature": 0.7
+  }'
+```
+
+## 效果展示
+
+- image:
+
+<div align=center>
+    <img src="./doc/demo.jpeg"/>
+</div>
+- text: "Describe this image."
+
+Output:
+
+<div align=center>
+    <img src="./doc/result.png"/>
+</div>
+
+
+### 精度
+
+`DCU与GPU精度一致，推理框架：transformers、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**                                  |
+| :-----------------------------: | :----------: | :---------: | :--------------: | :----------------------------------------------------------: |
+| **Qwen3-VL-2B-Instruct**        | 2B           | K100AI、BW1000      | 1                | [Qwen3-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct) |
+| **Qwen3-VL-4B-Instruct**        | 4B           | K100AI、BW1000      | 1                | [Qwen3-VL-4B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct) |
+| **Qwen3-VL-8B-Instruct**        | 8B           | K100AI、BW1000      | 1                | [Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct) |
+| **Qwen3-VL-32B-Instruct**       | 32B          | K100AI、BW1000      | 4                | [Qwen3-VL-32B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct) |
+| **Qwen3-VL-30B-A3B-Instruct**   | 30B          | K100AI、BW1000      | 2                | [Qwen3-VL-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct) |
+| **Qwen3-VL-30B-A3B-Thinking**   | 30B          | K100AI、BW1000      | 2                | [Qwen3-VL-30B-A3B-Thinking](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Thinking) |
+| **Qwen3-VL-235B-A22B-Instruct** | 235B         | K100AI、BW1000      | 8                | [Qwen3-VL-235B-A22B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct) |
+| **Qwen3-VL-235B-A22B-Thinking** | 235B         | K100AI、BW1000      | 16               | [Qwen3-VL-235B-A22B-Thinking](https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Thinking) |
+
+
+
+
+
+## 源码仓库及问题反馈
+
+- https://developer.sourcefind.cn/codes/modelzoo/qwen3-vl_pytorch
+
+## 参考资料
+- https://github.com/QwenLM/Qwen3-VL

model.properties

+# 模型唯一标识
+modelCode=1858
+# 模型名称
+modelName=Qwen3-VL_pytorch
+# 模型描述
+modelDescription=Qwen3-VL这一代产品在各方面都进行了全面升级，迄今为止 是Qwen 系列中最强大的视觉语言模型。
+# 运行过程
+processType=推理
+# 算法类别
+appCategory=多模态
+# 框架类型
+frameType=pytorch
+# 加速卡类型
+accelerateType=BW1000.K100AI