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<h1 id="wint2-quantization">WINT2 Quantization</h1>
<p>Weights are compressed offline using the <a href="https://arxiv.org/pdf/2507.07145">CCQ (Convolutional Coding Quantization)</a> method. The actual stored numerical type of weights is INT8, with 4 weights packed into each INT8 value, equivalent to 2 bits per weight. Activations are not quantized. During inference, weights are dequantized and decoded in real-time to BF16 numerical type, and calculations are performed using BF16 numerical type.
- <strong>Supported Hardware</strong>: GPU
- <strong>Supported Architecture</strong>: MoE architecture
This method relies on the convolution algorithm to use overlapping bits to map 2-bit values ​​to a larger numerical representation space, so that the model weight quantization retains more information of the original data while compressing the true value to an extremely low 2-bit size. The general principle can be seen in the figure below:
<img alt="卷积编码量化示意图" src="../images/wint2.png" /></p>
<p>CCQ WINT2 is generally used in resource-constrained and low-threshold scenarios. Taking ERNIE-4.5-300B-A47B as an example, weights are compressed to 89GB, supporting single-card deployment on 141GB H20.</p>
<h2 id="executing-wint2-offline-inference">Executing WINT2 Offline Inference</h2>
<ul>
<li>When executing TP2/TP4 models, you can change the <code>model_name_or_path</code> and <code>tensor_parallel_size</code> parameters.</li>
</ul>
<pre><code>model_name_or_path = &quot;baidu/ERNIE-4.5-300B-A47B-2Bits-Paddle&quot;
prompts = [&quot;解析三首李白的诗&quot;]
from fastdeploy import LLM, SamplingParams
sampling_params = SamplingParams(temperature=0.7, top_p=0, max_tokens=128)
llm = LLM(model=model_name_or_path, tensor_parallel_size=1, use_cudagraph=True,)
outputs = llm.generate(prompts, sampling_params)
print(outputs)
</code></pre>
<h2 id="run-wint2-inference-service">Run WINT2 Inference Service</h2>
<ul>
<li>When executing TP2/TP4 models, you can change the <code>--model</code> and <code>tensor-parallel-size</code> parameters.</li>
</ul>
<pre><code>python -m fastdeploy.entrypoints.openai.api_server \
--model baidu/ERNIE-4.5-300B-A47B-2Bits-Paddle \
--port 8180 \
--metrics-port 8181 \
--engine-worker-queue-port 8182 \
--cache-queue-port 8183 \
--tensor-parallel-size 1 \
--max-model-len 32768 \
--use-cudagraph \
--enable-prefix-caching \
--enable-chunked-prefill \
--max-num-seqs 256
</code></pre>
<h2 id="request-the-service">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://0.0.0.0:1822/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;Write me a poem about large language model.&quot;}
],
&quot;stream&quot;: true
}'
</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;system&quot;, &quot;content&quot;: &quot;I'm a helpful AI assistant.&quot;},
{&quot;role&quot;: &quot;user&quot;, &quot;content&quot;: &quot;Write me a poem about large language model.&quot;},
],
stream=True,
)
for chunk in response:
if chunk.choices[0].delta:
print(chunk.choices[0].delta.content, end='')
print('\n')
</code></pre>
<p>By specifying <code>--model baidu/ERNIE-4.5-300B-A47B-2Bits-Paddle</code>, the offline quantized WINT2 model can be automatically downloaded from AIStudio. In the config.json file of this model, there will be WINT2 quantization-related configuration information, so there's no need to set <code>--quantization</code> when starting the inference service.</p>
<p>Example of quantization configuration in the model's config.json file:</p>
<pre><code>&quot;quantization_config&quot;: {
&quot;dense_quant_type&quot;: &quot;wint8&quot;,
&quot;moe_quant_type&quot;: &quot;w4w2&quot;,
&quot;quantization&quot;: &quot;wint2&quot;,
&quot;moe_quant_config&quot;: {
&quot;moe_w4_quant_config&quot;: {
&quot;quant_type&quot;: &quot;wint4&quot;,
&quot;quant_granularity&quot;: &quot;per_channel&quot;,
&quot;quant_start_layer&quot;: 0,
&quot;quant_end_layer&quot;: 6
},
&quot;moe_w2_quant_config&quot;: {
&quot;quant_type&quot;: &quot;wint2&quot;,
&quot;quant_granularity&quot;: &quot;pp_acc&quot;,
&quot;quant_group_size&quot;: 64,
&quot;quant_start_layer&quot;: 7,
&quot;quant_end_layer&quot;: 53
}
}
}
</code></pre>
<ul>
<li>For more deployment tutorials, please refer to <a href="../../get_started/ernie-4.5/">get_started</a>;</li>
<li>For more model descriptions, please refer to <a href="../../supported_models/">Supported Model List</a>.</li>
</ul>
<h2 id="wint2-performance">WINT2 Performance</h2>
<p>On the ERNIE-4.5-300B-A47B model, comparison of WINT2 vs WINT4 performance:</p>
<table>
<thead>
<tr>
<th>Test Set</th>
<th>Dataset Size</th>
<th>WINT4</th>
<th>WINT2</th>
</tr>
</thead>
<tbody>
<tr>
<td>IFEval</td>
<td>500</td>
<td>88.17</td>
<td>85.95</td>
</tr>
<tr>
<td>BBH</td>
<td>6511</td>
<td>94.43</td>
<td>90.06</td>
</tr>
<tr>
<td>DROP</td>
<td>9536</td>
<td>91.17</td>
<td>89.32</td>
</tr>
<tr>
<td>CMMLU</td>
<td>11477</td>
<td>89.92</td>
<td>86.55</td>
</tr>
</tbody>
</table>
</article>
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