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<p><a href="../zh/features//pooling_models.md">简体中文</a></p>
<h1 id="pooling-models">Pooling Models</h1>
<p>FastDeploy also supports pooling models, such as embedding models.</p>
<p>In FastDeploy, pooling models implement the <code>FdModelForPooling</code> interface.
These models use a <code>Pooler</code> to extract the final hidden states of the input
before returning them.</p>
<h2 id="configuration">Configuration</h2>
<h3 id="model-runner">Model Runner</h3>
<p>Run a model in pooling mode via the option <code>--runner pooling</code>.</p>
<p>!!! tip<br>
There is no need to set this option in the vast majority of cases as Fastdeploy can automatically
detect the appropriate model runner via <code>--runner auto</code>.</p>
<h3 id="model-conversion">Model Conversion</h3>
<p>FastDeploy can adapt models for various pooling tasks via the option <code>--convert &lt;type&gt;</code>.</p>
<p>If <code>--runner pooling</code> has been set (manually or automatically) but the model does not implement the
<code>FdModelForPooling</code> interface,
vLLM will attempt to automatically convert the model according to the architecture names
shown in the table below.</p>
<table>
<thead>
<tr>
<th>Architecture</th>
<th><code>--convert</code></th>
<th>Supported pooling tasks</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>*ForTextEncoding</code>, <code>*EmbeddingModel</code>, <code>*Model</code> <code>*ForProcessRewardModel</code></td>
<td><code>embed</code></td>
<td><code>embed</code></td>
</tr>
</tbody>
</table>
<p>!!! tip<br>
You can explicitly set <code>--convert &lt;type&gt;</code> to specify how to convert the model.</p>
<h3 id="pooler-configuration">Pooler Configuration</h3>
<h4 id="predefined-models">Predefined models</h4>
<p>If the <code>Pooler</code> defined by the model accepts <code>pooler_config</code>,
you can override some of its attributes via the <code>--pooler-config</code> option.</p>
<h4 id="converted-models">Converted models</h4>
<p>If the model has been converted via <code>--convert</code> (see above),
the pooler assigned to each task has the following attributes by default:</p>
<table>
<thead>
<tr>
<th>Task</th>
<th>Pooling Type</th>
<th>Normalization</th>
<th>Softmax</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>embed</code></td>
<td><code>LAST</code></td>
<td>✅︎</td>
<td>❌</td>
</tr>
</tbody>
</table>
<p>When loading <a href="https://huggingface.co/sentence-transformers">Sentence Transformers</a> models,
its Sentence Transformers configuration file (<code>modules.json</code>) takes priority over the model's defaults and It can also be specified during model network construction through @default_pooling_type("LAST").</p>
<h5 id="pooling-type">Pooling Type</h5>
<p>1.LastPool(PoolingType.LAST)</p>
<p>Purpose:Extracts the hidden state of the last token in each sequence</p>
<p>2.AllPool(PoolingType.ALL)</p>
<p>Purpose:Returns the hidden states of all tokens in each sequence</p>
<p>3.CLSPool(PoolingType.CLS)</p>
<p>Purpose:Returns the hidden state of the first token in each sequence (CLS token)</p>
<p>4.MeanPool(PoolingType.MEAN)</p>
<p>Purpose:Computes the average of all token hidden states in each sequence</p>
<h2 id="online-serving">Online Serving</h2>
<p>FastDeploy's OpenAI-compatible server provides API endpoints and custom reward interfaces.</p>
<p>[Embeddings API], supports text and multi-modal inputs</p>
<p>[Reward API], scores specific content</p>
<h3 id="embedding-model">Embedding Model:</h3>
<pre><code class="language-python">model_path=Qwen/Qwen3-Embedding-0.6B
python -m fastdeploy.entrypoints.openai.api_server --model ${model_path} \
--max-num-seqs 256 --max-model-len 32768 \
--port 9412 --engine-worker-queue-port 7142 \
--metrics-port 7211 --tensor-parallel-size 1 \
--gpu-memory-utilization 0.9 \
--runner pooling
</code></pre>
<p>Request Methods:
A. EmbeddingCompletionRequest Example (Standard Text Input)</p>
<pre><code class="language-bash">curl -X POST 'YOUR_SERVICE_URL/v1/embeddings' \
-H 'Content-Type: application/json' \
-d '{
&quot;model&quot;: &quot;text-embedding-chat-model&quot;,
&quot;input&quot;: [
&quot;This is a sentence for pooling embedding.&quot;,
&quot;Another input text.&quot;
],
&quot;user&quot;: &quot;test_client&quot;
}'
</code></pre>
<p>B. EmbeddingChatRequest Example (Message Sequence Input)</p>
<pre><code class="language-bash">curl -X POST 'YOUR_SERVICE_URL/v1/embeddings' \
-H 'Content-Type: application/json' \
-d '{
&quot;model&quot;: &quot;text-embedding-chat-model&quot;,
&quot;messages&quot;: [
{&quot;role&quot;: &quot;user&quot;, &quot;content&quot;: &quot;Generate embedding for user query.&quot;}
]
}'
</code></pre>
<h3 id="pooling-model-and-reward-score">Pooling Model and reward score</h3>
<pre><code class="language-python">model_path=RM_v1008
python -m fastdeploy.entrypoints.openai.api_server \
--model ${model_path} \
--max-num-seqs 256 \
--max-model-len 8192 \
--port 13351 \
--engine-worker-queue-port 7562 \
--metrics-port 7531 \
--tensor-parallel-size 8 \
--gpu-memory-utilization 0.9 \
--runner pooling \
--convert embed
</code></pre>
<p>Request Method: ChatRewardRequest</p>
<pre><code class="language-bash">curl --location 'http://xxxx/v1/chat/reward' \
--header 'Content-Type: application/json' \
--data '{
&quot;model&quot;: &quot;&quot;,
&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://xxx/a.png&quot;
}
}
]
},
{
&quot;role&quot;: &quot;assistant&quot;,
&quot;content&quot;: [
{
&quot;type&quot;: &quot;text&quot;,
&quot;text&quot;: &quot;图里有几个人&quot;
}
]
}
],
&quot;user&quot;: &quot;user-123&quot;,
&quot;chat_template&quot;: null,
&quot;chat_template_kwargs&quot;: {
&quot;custom_var&quot;: &quot;value&quot;
},
&quot;mm_processor_kwargs&quot;: {
&quot;image_size&quot;: 224
}
}'
</code></pre>
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