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Contributors.md

+# Contributors
+None
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README.md

-# Qwen3-Embedding-4B_pytorch
+# Qwen3-Embedding
+## 论文
+[Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models](https://arxiv.org/abs/2506.05176)
 
-Qwen3嵌入模型系列是Qwen3家族最新的专有模型，专门为文本嵌入和排序任务而设计。
\ No newline at end of file
+## 模型结构
+采用双编码器和交叉编码器架构。
+<div align=center>
+    <img src="./doc/model.png"/>
+</div>
+
+## 算法原理
+Qwen3嵌入模型系列是Qwen3家族最新的专有模型，专门为文本嵌入和排序任务而设计。此系列继承了其基础模型出色的多语言能力、长文本理解和推理技能。Qwen3 嵌入系列在文本检索、代码检索、文本分类、文本聚类和双语文本挖掘等多种文本嵌入和排序任务中取得了显著进展。
+<div align=center>
+    <img src="./doc/methods.png"/>
+</div>
+
+## 环境配置
+`-v 路径`、`docker_name`和`imageID`根据实际情况修改
+
+### Docker（方法一）
+```bash
+dcoker pull image.sourcefind.cn:5000/dcu/admin/base/custom:vllm0.8.5-ubuntu22.04-dtk25.04-rc7-das1.5-py3.10-20250521-fixpy-rocblas0521-beta2
+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
+
+cd /your_code_path/qwen3-embedding_pytorch
+pip install transformers>=4.51.0
+pip install sentence-transformers>=2.7.0
+```
+
+### Dockerfile（方法二）
+```bash
+cd docker
+docker build --no-cache -t qwen3-embedding: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
+
+cd /your_code_path/qwen3-embedding_pytorch
+pip install transformers>=4.51.0
+pip install sentence-transformers>=2.7.0
+```
+
+### 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.hpccube.com/tool/)开发者社区下载安装。
+```bash
+DTK: 25.04
+python: 3.10
+torch: 2.4.1+das.opt2.dtk2504
+deepspeed: 0.14.2+das.opt2.dtk2504
+vllm: 0.8.5
+```
+`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应`
+
+其它非深度学习库安装方式如下：
+```bash
+pip install transformers>=4.51.0
+pip install sentence-transformers>=2.7.0
+```
+
+## 数据集
+无
+
+## 训练
+暂无
+
+## 推理
+### vllm推理方法
+```bash
+## 必须添加HF_ENDPOINT环境变量
+export HF_ENDPOINT=https://hf-mirror.com
+## model_name_or_path 模型地址参数
+python infer_vllm.py --model_name_or_path /path/your_model_path/
+```
+
+### sentence-transformers推理方法
+```bash
+## 必须添加HF_ENDPOINT环境变量
+export HF_ENDPOINT=https://hf-mirror.com
+## model_name_or_path 模型地址参数
+python infer_infer_sentence_transformers.py
+```
+
+## result
+<div align=center>
+    <img src="./doc/results.png"/>
+</div>
+
+### 精度
+暂无
+
+## 应用场景
+### 算法类别
+文本理解
+
+### 热点应用行业
+制造,广媒,家居,教育
+
+## 预训练权重
+- [Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B)
+- [Qwen3-Embedding-4B](https://huggingface.co/Qwen/Qwen3-Embedding-4B)
+- [Qwen3-Embedding-8B](https://huggingface.co/Qwen/Qwen3-Embedding-8B)
+
+## 源码仓库及问题反馈
+- https://developer.sourcefind.cn/codes/modelzoo/qwen3-embedding_pytorch
+
+## 参考资料
+- https://qwenlm.github.io/blog/qwen3-embedding/
+- https://github.com/QwenLM/Qwen3-Embedding

docker/Dockerfile

+FROM image.sourcefind.cn:5000/dcu/admin/base/custom:vllm0.8.5-ubuntu22.04-dtk25.04-rc7-das1.5-py3.10-20250521-fixpy-rocblas0521-beta2
\ No newline at end of file

infer_sentence_transformers.py

+import argparse
+
+from sentence_transformers import SentenceTransformer
+
+
+parse = argparse.ArgumentParser()
+parse.add_argument("--model_name_or_path", type=str, default="Qwen/Qwen3-Embedding-0.6B")
+args = parse.parse_args()
+
+# Load the model
+model = SentenceTransformer(args.model_name_or_path)
+
+# We recommend enabling flash_attention_2 for better acceleration and memory saving,
+# together with setting `padding_side` to "left":
+# model = SentenceTransformer(
+#     "Qwen/Qwen3-Embedding-0.6B",
+#     model_kwargs={"attn_implementation": "flash_attention_2", "device_map": "auto"},
+#     tokenizer_kwargs={"padding_side": "left"},
+# )
+
+# The queries and documents to embed
+queries = [
+    "What is the capital of China?",
+    "Explain gravity",
+]
+documents = [
+    "The capital of China is Beijing.",
+    "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun.",
+]
+
+# Encode the queries and documents. Note that queries benefit from using a prompt
+# Here we use the prompt called "query" stored under `model.prompts`, but you can
+# also pass your own prompt via the `prompt` argument
+query_embeddings = model.encode(queries, prompt_name="query")
+document_embeddings = model.encode(documents)
+
+# Compute the (cosine) similarity between the query and document embeddings
+similarity = model.similarity(query_embeddings, document_embeddings)
+print(similarity)

infer_vllm.py

+import argparse
+import torch
+import vllm
+
+from vllm import LLM
+
+
+parse = argparse.ArgumentParser()
+parse.add_argument("--model_name_or_path", type=str, default="Qwen/Qwen3-Embedding-0.6B")
+args = parse.parse_args()
+
+def get_detailed_instruct(task_description: str, query: str) -> str:
+    return f'Instruct: {task_description}\nQuery:{query}'
+
+# Each query must come with a one-sentence instruction that describes the task
+task = 'Given a web search query, retrieve relevant passages that answer the query'
+
+queries = [
+    get_detailed_instruct(task, 'What is the capital of China?'),
+    get_detailed_instruct(task, 'Explain gravity')
+]
+# No need to add instruction for retrieval documents
+documents = [
+    "The capital of China is Beijing.",
+    "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun."
+]
+
+input_texts = queries + documents
+print("input_texts:", input_texts)
+
+# Initialize the model for embedding
+model = LLM(model=args.model_name_or_path, task="embed")
+
+outputs = model.embed(input_texts)
+embeddings = torch.tensor([o.outputs.embedding for o in outputs])
+scores = (embeddings[:2] @ embeddings[2:].T)
+print("scores:", scores.tolist())

model.properties

+# 模型唯一标识
+modelCode=1589
+# 模型名称
+modelName=qwen3-embedding_pytorch
+# 模型描述
+modelDescription=Qwen3嵌入模型系列是Qwen3家族最新的专有模型，专门为文本嵌入和排序任务而设计。
+# 应用场景
+appScenario=推理,文本理解,制造,广媒,家居,教育
+# 框架类型
+frameType=pytorch