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<h1 id="reasoning-outputs">Reasoning Outputs</h1>
<p>Reasoning models return an additional <code>reasoning_content</code> field in their output, which contains the reasoning steps that led to the final conclusion.</p>
<h2 id="supported-models">Supported Models</h2>
<table>
<thead>
<tr>
<th>Model Name</th>
<th>Parser Name</th>
<th>Eable_thinking by Default</th>
</tr>
</thead>
<tbody>
<tr>
<td>baidu/ERNIE-4.5-VL-424B-A47B-Paddle</td>
<td>ernie-45-vl</td>
<td>✓</td>
</tr>
<tr>
<td>baidu/ERNIE-4.5-VL-28B-A3B-Paddle</td>
<td>ernie-45-vl</td>
<td>✓</td>
</tr>
</tbody>
</table>
<p>The reasoning model requires a specified parser to extract reasoning content. The reasoning mode can be disabled by setting the <code>"enable_thinking": false</code> parameter.</p>
<p>Interfaces that support toggling the reasoning mode:
1. <code>/v1/chat/completions</code> requests in OpenAI services.
2. <code>/v1/chat/completions</code> requests in the OpenAI Python client.
3. <code>llm.chat</code> requests in Offline interfaces.</p>
<p>For reasoning models, the length of the reasoning content can be controlled via <code>reasoning_max_tokens</code>. Add <code>"reasoning_max_tokens": 1024</code> to the request.</p>
<h3 id="quick-start">Quick Start</h3>
<p>When launching the model service, specify the parser name using the <code>--reasoning-parser</code> argument.
This parser will process the model's output and extract the <code>reasoning_content</code> field.</p>
<pre><code class="language-bash">python -m fastdeploy.entrypoints.openai.api_server \
--model /path/to/your/model \
--enable-mm \
--tensor-parallel-size 8 \
--port 8192 \
--quantization wint4 \
--reasoning-parser ernie-45-vl
</code></pre>
<p>Next, make a request to the model that should return the reasoning content in the response.</p>
<pre><code class="language-bash">curl -X POST &quot;http://0.0.0.0:8192/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;Which era does the cultural relic in the picture belong to&quot;}
]}
],
&quot;chat_template_kwargs&quot;:{&quot;enable_thinking&quot;: true},
&quot;reasoning_max_tokens&quot;: 1024
}'
</code></pre>
<p>The <code>reasoning_content</code> field contains the reasoning steps to reach the final conclusion, while the <code>content</code> field holds the conclusion itself.</p>
<h3 id="streaming-chat-completions">Streaming chat completions</h3>
<p>Streaming chat completions are also supported for reasoning models. The <code>reasoning_content</code> field is available in the <code>delta</code> field in <code>chat completion response chunks</code></p>
<pre><code class="language-python">from openai import OpenAI
# Set OpenAI's API key and API base to use vLLM's API server.
openai_api_key = &quot;EMPTY&quot;
openai_api_base = &quot;http://localhost:8192/v1&quot;
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
chat_response = client.chat.completions.create(
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;Which era does the cultural relic in the picture belong to&quot;}]}
],
model=&quot;vl&quot;,
stream=True,
extra_body={
&quot;chat_template_kwargs&quot;:{&quot;enable_thinking&quot;: True},
&quot;reasoning_max_tokens&quot;: 1024
}
)
for chunk in chat_response:
if chunk.choices[0].delta is not None:
print(chunk.choices[0].delta, end='')
print(&quot;\n&quot;)
</code></pre>
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