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<h1 id="deploy-ernie-45-vl-28b-a3b-paddle-multimodal-model-in-10-minutes">Deploy ERNIE-4.5-VL-28B-A3B-Paddle Multimodal Model in 10 Minutes</h1>
<p>Before deployment, please ensure your environment meets the following requirements:</p>
<ul>
<li>GPU Driver &gt;= 535</li>
<li>CUDA &gt;= 12.3</li>
<li>CUDNN &gt;= 9.5</li>
<li>Linux X86_64</li>
<li>Python &gt;= 3.10</li>
<li>Hardware configuration meets minimum requirements (refer to <a href="../../supported_models/">Supported Models</a>)</li>
</ul>
<p>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.</p>
<p>For more information about how to install FastDeploy, refer to the <a href="../installation/">installation document</a>.</p>
<blockquote>
<p>💡 <strong>Note</strong>: All ERNIE multimodal models support reasoning capability. Enable/disable it by setting <code>enable_thinking</code> in requests (see example below).</p>
</blockquote>
<h2 id="1-launch-service">1. Launch Service</h2>
<p>After installing FastDeploy, execute the following command in the terminal to start the service. For the configuration method of the startup command, refer to <a href="../../parameters/">Parameter Description</a></p>
<pre><code class="language-shell">export ENABLE_V1_KVCACHE_SCHEDULER=1
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
</code></pre>
<blockquote>
<p>💡 Note: In the path specified by <code>--model</code>, 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 <code>baidu/ERNIE-4.5-0.3B-Base-Paddle</code>). If it exists, it will automatically start downloading. The default download path is: <code>~/xx</code>. For instructions and configuration on automatic model download, see <a href="../../supported_models/">Model Download</a>.
<code>--max-model-len</code> indicates the maximum number of tokens supported by the currently deployed service.
<code>--max-num-seqs</code> indicates the maximum number of concurrent processing supported by the currently deployed service.
<code>--reasoning-parser</code> specifies the thinking content parser.
<code>--enable-mm</code> indicates whether to enable multi-modal support.</p>
</blockquote>
<p><strong>Related Documents</strong>
- <a href="../../online_serving/">Service Deployment</a>
- <a href="../../online_serving/metrics/">Service Monitoring</a></p>
<h2 id="2-request-the-service">2. Request the Service</h2>
<p>After starting the service, the following output indicates successful initialization:</p>
<pre><code class="language-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)
</code></pre>
<h3 id="health-check">Health Check</h3>
<p>Verify service status (HTTP 200 indicates success):</p>
<pre><code class="language-shell">curl -i http://0.0.0.0:8180/health
</code></pre>
<h3 id="curl-request">cURL Request</h3>
<p>Send requests to the service with the following command:</p>
<pre><code class="language-shell">curl -X POST &quot;http://0.0.0.0:8180/v1/chat/completions&quot; \
-H &quot;Content-Type: application/json&quot; \
-d '{
&quot;messages&quot;: [
{&quot;role&quot;: &quot;user&quot;, &quot;content&quot;: [
{&quot;type&quot;: &quot;image_url&quot;, &quot;image_url&quot;: {&quot;url&quot;: &quot;https://paddlenlp.bj.bcebos.com/datasets/paddlemix/demo_images/example2.jpg&quot;}},
{&quot;type&quot;: &quot;text&quot;, &quot;text&quot;: &quot;What era does this artifact belong to?&quot;}
]}
],
&quot;chat_template_kwargs&quot;:{&quot;enable_thinking&quot;: false}
}'
</code></pre>
<h3 id="python-client-openai-compatible-api">Python Client (OpenAI-compatible API)</h3>
<p>FastDeploy's API is OpenAI-compatible. You can also use Python for requests:</p>
<pre><code class="language-python">import openai
host = &quot;0.0.0.0&quot;
port = &quot;8180&quot;
client = openai.Client(base_url=f&quot;http://{host}:{port}/v1&quot;, api_key=&quot;null&quot;)
response = client.chat.completions.create(
model=&quot;null&quot;,
messages=[
{&quot;role&quot;: &quot;user&quot;, &quot;content&quot;: [
{&quot;type&quot;: &quot;image_url&quot;, &quot;image_url&quot;: {&quot;url&quot;: &quot;https://paddlenlp.bj.bcebos.com/datasets/paddlemix/demo_images/example2.jpg&quot;}},
{&quot;type&quot;: &quot;text&quot;, &quot;text&quot;: &quot;What era does this artifact belong to?&quot;},
]},
],
extra_body={&quot;enable_thinking&quot;: false},
stream=True,
)
for chunk in response:
if chunk.choices[0].delta:
print(chunk.choices[0].delta.content, end='')
print('\n')
</code></pre>
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