diff --git a/docker/download_model.py b/docker/download_model.py
new file mode 100644
index 0000000..bd246ac
--- /dev/null
+++ b/docker/download_model.py
@@ -0,0 +1,34 @@
+import os
+from huggingface_hub import snapshot_download
+
+# 1. 设置镜像源（国内加速）
+# os.environ["HF_ENDPOINT"] = "https://mirrors.tuna.tsinghua.edu.cn/hugging-face/"
+
+# 2. 定义模型列表（名称 + 下载路径）
+models_to_download = [
+    {
+        "repo_id": "BAAI/bge-m3",  # Embedding 模型
+        "local_dir": os.path.expanduser("./models/bge-m3"),
+    },
+    {
+        "repo_id": "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",  # LLM 模型
+        "local_dir": os.path.expanduser("./models/DeepSeek-R1-1.5B"),
+    }
+]
+
+# 3. 遍历下载所有模型
+for model in models_to_download:
+    while True:  # 断点续传重试机制
+        try:
+            print(f"开始下载模型: {model['repo_id']} 到目录: {model['local_dir']}")
+            snapshot_download(
+                repo_id=model["repo_id"],
+                local_dir=model["local_dir"],
+                resume_download=True,  # 启用断点续传
+                force_download=False,  # 避免重复下载已有文件
+                token=None,            # 如需访问私有模型，替换为你的 token
+            )
+            print(f"模型 {model['repo_id']} 下载完成！")
+            break
+        except Exception as e:
+            print(f"下载失败: {e}, 重试中...")
\ No newline at end of file
diff --git a/docker/model_test.py b/docker/model_test.py
new file mode 100644
index 0000000..6f6b45a
--- /dev/null
+++ b/docker/model_test.py
@@ -0,0 +1,49 @@
+import requests
+from openai import OpenAI
+
+# 测试 embedding 模型 (vllm-bge)
+def test_embedding(model, text):
+    """测试嵌入模型"""
+    client = OpenAI(base_url="http://localhost:8000/v1", api_key="1")
+    
+    response = client.embeddings.create(
+        model=model,  # 使用支持嵌入的模型
+        input=text  # 需要嵌入的文本
+    )
+    
+    # 打印嵌入响应内容
+    # print(f"Embedding response: {response}")
+    
+    result = response.data[0].embedding
+
+    if response and response.data:
+        print(len(result))
+    else:
+        print("Failed to get embedding.")
+
+# 测试文本生成模型 (vllm-deepseek)
+def test_chat(model, prompt):
+    """测试文本生成模型"""
+    client = OpenAI(base_url="http://localhost:8001/v1", api_key="1")
+    
+    response = client.completions.create(
+        model=model,
+        prompt=prompt
+    )
+    
+    # 打印生成的文本
+    print(f"Chat response: {response.choices[0].text}")
+
+def main():
+    # 测试文本生成模型 deepseek-r1
+    prompt = "你好，今天的天气怎么样？"
+    print("Testing vllm-deepseek model for chat...")
+    test_chat("deepseek-r1", prompt)
+    
+    # 测试嵌入模型 bge-m3
+    embedding_text = "我喜欢编程，尤其是做AI模型。"
+    print("\nTesting vllm-bge model for embedding...")
+    test_embedding("bge-m3", embedding_text)
+
+if __name__ == "__main__":
+    main()