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+# Deploy ERNIE-4.5-VL-28B-A3B-Paddle Multimodal Model in 10 Minutes
+
+Before deployment, please ensure your environment meets the following requirements:
+
+- GPU Driver >= 535
+- CUDA >= 12.3
+- CUDNN >= 9.5
+- Linux X86_64
+- Python >= 3.10
+- Hardware configuration meets minimum requirements (refer to [Supported Models](../supported_models.md))
+
+For quick deployment across different hardware, this guide uses the ERNIE-4.5-VL-28B-A3B-Paddle multimodel model as an example, which can run on most hardware configurations.
+
+For more information about how to install FastDeploy, refer to the [installation document](./installation/README.md).
+
+>💡 **Note**: All ERNIE multimodal models support reasoning capability. Enable/disable it by setting ```enable_thinking``` in requests (see example below).
+
+## 1. Launch Service
+After installing FastDeploy, execute the following command in the terminal to start the service. For the configuration method of the startup command, refer to [Parameter Description](../parameters.md)
+
+```shell
+python -m fastdeploy.entrypoints.openai.api_server \
+       --model baidu/ERNIE-4.5-VL-28B-A3B-Paddle \
+       --port 8180 \
+       --metrics-port 8181 \
+       --engine-worker-queue-port 8182 \
+       --max-model-len 32768 \
+       --max-num-seqs 32 \
+       --reasoning-parser ernie-45-vl \
+       --enable-mm
+```
+
+> 💡 Note: In the path specified by ```--model```, if the subdirectory corresponding to the path does not exist in the current directory, it will try to query whether AIStudio has a preset model based on the specified model name (such as ```baidu/ERNIE-4.5-0.3B-Base-Paddle```). If it exists, it will automatically start downloading. The default download path is: ```~/xx```. For instructions and configuration on automatic model download, see [Model Download](../supported_models.md).  
+```--max-model-len``` indicates the maximum number of tokens supported by the currently deployed service.  
+```--max-num-seqs``` indicates the maximum number of concurrent processing supported by the currently deployed service.  
+```--reasoning-parser``` specifies the thinking content parser.  
+```--enable-mm``` indicates whether to enable multi-modal support.
+
+**Related Documents**
+- [Service Deployment](../online_serving/README.md)
+- [Service Monitoring](../online_serving/metrics.md)
+
+## 2. Request the Service
+After starting the service, the following output indicates successful initialization:
+
+```shell
+api_server.py[line:91] Launching metrics service at http://0.0.0.0:8181/metrics
+api_server.py[line:94] Launching chat completion service at http://0.0.0.0:8180/v1/chat/completions
+api_server.py[line:97] Launching completion service at http://0.0.0.0:8180/v1/completions
+INFO:     Started server process [13909]
+INFO:     Waiting for application startup.
+INFO:     Application startup complete.
+INFO:     Uvicorn running on http://0.0.0.0:8180 (Press CTRL+C to quit)
+```
+
+### Health Check
+
+Verify service status (HTTP 200 indicates success):
+
+```shell
+curl -i http://0.0.0.0:8180/health
+```
+
+### cURL Request
+Send requests to the service with the following command:
+
+```shell
+curl -X POST "http://0.0.0.0:8180/v1/chat/completions" \
+-H "Content-Type: application/json" \
+-d '{
+  "messages": [
+    {"role": "user", "content": [
+      {"type": "image_url", "image_url": {"url": "https://paddlenlp.bj.bcebos.com/datasets/paddlemix/demo_images/example2.jpg"}},
+      {"type": "text", "text": "What era does this artifact belong to?"}
+    ]}
+  ],
+  "metadata": {"enable_thinking": false}
+}'
+```
+
+### Python Client (OpenAI-compatible API)
+
+FastDeploy's API is OpenAI-compatible. You can also use Python for requests:
+
+```python
+import openai
+host = "0.0.0.0"
+port = "8180"
+client = openai.Client(base_url=f"http://{host}:{port}/v1", api_key="null")
+
+response = client.chat.completions.create(
+    model="null",
+    messages=[
+        {"role": "user", "content": [
+            {"type": "image_url", "image_url": {"url": "https://paddlenlp.bj.bcebos.com/datasets/paddlemix/demo_images/example2.jpg"}},
+            {"type": "text", "text": "What era does this artifact belong to?"},
+        ]},
+    ],
+    metadata={"enable_thinking": false},
+    stream=True,
+)
+for chunk in response:
+    if chunk.choices[0].delta:
+        print(chunk.choices[0].delta.content, end='')
+print('\n')
+```