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<p><a href="../zh/features/reasoning_output.md">简体中文</a></p>
<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>Enable thinking by Default</th>
<th>Tool Calling</th>
<th>Thinking switch parameters</th>
</tr>
</thead>
<tbody>
<tr>
<td>baidu/ERNIE-4.5-VL-424B-A47B-Paddle</td>
<td>ernie-45-vl</td>
<td>✅</td>
<td>❌</td>
<td>"chat_template_kwargs":{"enable_thinking": true/false}</td>
</tr>
<tr>
<td>baidu/ERNIE-4.5-VL-28B-A3B-Paddle</td>
<td>ernie-45-vl</td>
<td>✅</td>
<td>❌</td>
<td>"chat_template_kwargs":{"enable_thinking": true/false}</td>
</tr>
<tr>
<td>baidu/ERNIE-4.5-21B-A3B-Thinking</td>
<td>ernie-x1</td>
<td>✅ Not supported for turning off</td>
<td>✅</td>
<td>❌</td>
</tr>
<tr>
<td>baidu/ERNIE-4.5-VL-28B-A3B-Thinking</td>
<td>ernie-45-vl-thinking</td>
<td>✅ Not recommended to turn off</td>
<td>✅</td>
<td>"chat_template_kwargs": {"options": {"thinking_mode": "open/close"}}</td>
</tr>
</tbody>
</table>
<p>The reasoning model requires a specified parser to extract reasoning content. Referring to the <code>thinking switch parameters</code> of each model can turn off the model's thinking mode.</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.
Taking the baidu/ERNIE-4.5-VL-28B-A3B-Paddle model as an example：</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>
<h2 id="tool-calling">Tool Calling</h2>
<p>The reasoning content is also available when both tool calling and the reasoning parser are enabled. Additionally, tool calling only parses functions from the <code>content</code> field, not from the <code>reasoning_content</code>.</p>
<p>Model request example:</p>
<pre><code class="language-bash">curl -X POST &quot;http://0.0.0.0:8390/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;Get the current weather in BeiJing&quot;
}
],
&quot;tools&quot;: [
{
&quot;type&quot;: &quot;function&quot;,
&quot;function&quot;: {
&quot;name&quot;: &quot;get_weather&quot;,
&quot;description&quot;: &quot;Determine weather in my location&quot;,
&quot;parameters&quot;: {
&quot;type&quot;: &quot;object&quot;,
&quot;properties&quot;: {
&quot;location&quot;: {
&quot;type&quot;: &quot;string&quot;,
&quot;description&quot;: &quot;The city and state e.g. San Francisco, CA&quot;
},
&quot;unit&quot;: {
&quot;type&quot;: &quot;string&quot;,
&quot;enum&quot;: [
&quot;c&quot;,
&quot;f&quot;
]
}
},
&quot;additionalProperties&quot;: false,
&quot;required&quot;: [
&quot;location&quot;,
&quot;unit&quot;
]
},
&quot;strict&quot;: true
}
}],
&quot;stream&quot;: false
}'
</code></pre>
<p>Model output example</p>
<pre><code class="language-json">{
&quot;choices&quot;: [
{
&quot;index&quot;: 0,
&quot;message&quot;: {
&quot;role&quot;: &quot;assistant&quot;,
&quot;content&quot;: &quot;&quot;,
&quot;reasoning_content&quot;: &quot;The user asks about ...&quot;,
&quot;tool_calls&quot;: [
{
&quot;id&quot;: &quot;chatcmpl-tool-311b9bda34274722afc654c55c8ce6a0&quot;,
&quot;type&quot;: &quot;function&quot;,
&quot;function&quot;: {
&quot;name&quot;: &quot;get_weather&quot;,
&quot;arguments&quot;: &quot;{\&quot;location\&quot;: \&quot;BeiJing\&quot;, \&quot;unit\&quot;: \&quot;c\&quot;}&quot;
}
}
]
},
&quot;finish_reason&quot;: &quot;tool_calls&quot;
}
]
}
</code></pre>
<p>More reference documentation related to tool calling usage： <a href="../tool_calling/">Tool Calling</a></p>
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