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README.md

+# Qwen3-TTS
+## 论文
+[Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking](https://arxiv.org/abs/2601.04720)
+
+## 模型简介
+Qwen3-VL-Embedding 和 Qwen3-VL-Reranker 模型系列是通义千问（Qwen）家族的最新成员，基于近期开源且强大的 Qwen3-VL 基础模型构建而成。该系列专为多模态信息检索与跨模态理解而设计，可接受包括文本、图像、截图和视频在内的多种输入形式，以及这些模态任意组合的混合输入。 Embedding 模型可生成高维向量，适用于检索、聚类等广泛场景；而 Reranker 模型则用于对初步结果进行精细化排序，二者共同构建了一套完整的前沿多模态搜索流水线。
+多模态通用性：两个模型均能在统一框架下无缝处理文本、图像、截图和视频等多种输入，在图像-文本检索、视频-文本匹配、视觉问答（VQA）以及多模态内容聚类等多样化任务中均达到业界领先水平。
+统一表征学习（Embedding）：依托 Qwen3-VL 架构，Embedding 模型在共享语义空间中生成同时包含视觉与文本信息的丰富向量，从而高效支持跨模态的相似度计算与检索。
+高精度重排序（Reranker）：我们还推出了 Qwen3-VL-Reranker 系列模型，以补充 Embedding 模型的能力。Reranker 接收一个（查询，文档）对作为输入——其中查询和文档均可包含任意单一或混合模态——并输出精确的相关性分数。在检索流程中，通常先由 Embedding 模型执行高效的初步召回，再由 Reranker 在后续阶段对结果进行精细化重排序。这种两阶段方法显著提升了检索准确率。
+卓越的实用性：继承 Qwen3-VL 的多语言能力，该系列支持 30 多种语言，非常适合全球化应用。其在实际场景中高度实用，提供灵活的向量维度、针对特定用例可定制的指令支持，即使使用量化后的嵌入向量也能保持强劲性能。这些特性使开发者能够轻松将两个模型集成到现有系统中，实现强大的跨语言与跨模态理解能力。
+<div align=center>
+    <img src="./doc/01.png"/>
+</div>
+
+
+## 环境依赖
+| 软件 | 版本 |
+| :------: | :------: |
+| DTK | 25.04.2 |
+| python | 3.10.12 |
+| transformers | 4.57.6 |
+| vllm | 0.11.0+das.opt1.rc2.dtk2604 |
+
+推荐使用镜像:harbor.sourcefind.cn:5443/dcu/admin/base/vllm:0.11.0-ubuntu22.04-dtk26.04-0130-py3.10-20260202
+
+```bash
+docker run -it \
+    --shm-size 60g \
+    --network=host \
+    --name qwen3-tts \
+    --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-dtk26.04-0130-py3.10-20260202 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
+
+镜像内其他环境配置
+```
+
+1.解压vllm.zip到/usr/local/lib/python3.10/dist-packages直接覆盖需要修改的文件
+    unzip -o vllm.zip -d /usr/local/lib/python3.10/dist-packages
+```
+
+
+## 数据集
+暂无
+
+## 训练
+暂无
+
+## 推理
+### vllm
+#### 单机推理
+
+启动服务
+```bash
+
+vllm serve /public/weishb/Qwen/Qwen3-VL-Reranker-8B \
+    --runner pooling \
+    --hf-overrides '{"architectures":  ["Qwen3VLForSequenceClassification"],"classifier_from_token":["no","yes"],"is_original_qwen3_reranker":true}' \
+    --chat-template qwen3_vl_reranker.jinja \
+    --max-model-len 4096 \
+    --served-model-name qwen3-vl-reranker
+```
+调用服务：
+```
+curl -s http://127.0.0.1:8000/rerank \
+    -H "Content-Type: application/json" \
+    -d '{
+      "model": "qwen3-vl-reranker",
+      "query": "如何部署 vLLM 的 reranker 服务？",
+      "documents": [
+        "先安装依赖，然后使用 vllm serve 启动服务，并调用 /score 接口。",
+        "今天的天气不错，适合出去散步。"
+      ]
+    }'
+```
+
+
+## 效果展示
+
+![alt text](![alt text](./doc/image.png))
+
+### 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|
+|:-----:|:----------:|:----------:|:---------------------:|:----------:|
+| Qwen3-VL-Reranker-8B | 8B | K100AI | 1 | [Modelscope](https://www.modelscope.cn/models/Qwen/Qwen3-VL-Reranker-8B)|
+| Qwen3-VL-Reranker-2B | 2B | K100AI | 1 | [Modelscope](https://www.modelscope.cn/models/Qwen/Qwen3-VL-Reranker-2B)|
+
+## 源码仓库及问题反馈
+- https://developer.sourcefind.cn/codes/modelzoo/qwen3-vl-reranker-8b
+
+## 参考资料
+- https://github.com/QwenLM/Qwen3-VL-Embedding
\ No newline at end of file

model.properties

+# 模型唯一标识
+modelCode=2089
+# 模型名称
+modelName=Qwen3-VL-Reranker_vllm
+# 模型描述
+modelDescription=Qwen3-VL-Reranker 模型则用于对初步结果进行精细化排序
+# 运行过程
+processType=推理
+# 算法类别
+appCategory=文本理解
+# 框架类型
+frameType=vllm
+# 加速卡类型
+accelerateType=K100AI

qwen3_vl_reranker.jinja

+<|im_start|>system
+Judge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be "yes" or "no".<|im_end|>
+<|im_start|>user
+<Instruct>: {{
+    messages
+    | selectattr("role", "eq", "system")
+    | map(attribute="content")
+    | first
+    | default("Given a search query, retrieve relevant candidates that answer the query.")
+}}<Query>:{{
+    messages
+    | selectattr("role", "eq", "query")
+    | map(attribute="content")
+    | first
+}}
+<Document>:{{
+    messages
+    | selectattr("role", "eq", "document")
+    | map(attribute="content")
+    | first
+}}<|im_end|>
+<|im_start|>assistant
+