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[Docs] Add XPU tutorials for Qwen / InternVL (#140)

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Signed-off-by: Joeegin <3318329726@qq.com>
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Joeegin
2026-01-22 13:50:49 +08:00
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docs/source/tutorials/index.md
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@@ -4,6 +4,9 @@
:caption: Deployment
:maxdepth: 1
single_xpu_Qwen3-8B
single_xpu_Qwen3-VL-32B
single_xpu_InternVL2_5-26B
multi_xpu_Qwen2.5-VL-32B
multi_xpu_GLM-4.5
multi_xpu_Qwen3-Coder-480B-A35B(W8A8)
:::

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docs/source/tutorials/multi_xpu_Qwen2.5-VL-32B.md Normal file
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# Multi XPU (Qwen2.5-VL-32B)
## Run vllm-kunlun on Multi XPU
Setup environment using container:
```bash
# !/bin/bash
# rundocker.sh
XPU_NUM=8
DOCKER_DEVICE_CONFIG=""
if [ $XPU_NUM -gt 0 ]; then
for idx in $(seq 0 $((XPU_NUM-1))); do
DOCKER_DEVICE_CONFIG="${DOCKER_DEVICE_CONFIG} --device=/dev/xpu${idx}:/dev/xpu${idx}"
done
DOCKER_DEVICE_CONFIG="${DOCKER_DEVICE_CONFIG} --device=/dev/xpuctrl:/dev/xpuctrl"
fi
export build_image="xxxxxxxxxxxxxxxxx"
docker run -itd ${DOCKER_DEVICE_CONFIG} \
--net=host \
--cap-add=SYS_PTRACE --security-opt seccomp=unconfined \
--tmpfs /dev/shm:rw,nosuid,nodev,exec,size=32g \
--cap-add=SYS_PTRACE \
-v /home/users/vllm-kunlun:/home/vllm-kunlun \
-v /usr/local/bin/xpu-smi:/usr/local/bin/xpu-smi \
--name "$1" \
-w /workspace \
"$build_image" /bin/bash
```
### Offline Inference on Multi XPU
Start the server in a container:
```bash
from vllm import LLM, SamplingParams
def main():
model_path = "/models/Qwen2.5-VL-32B-Instruct"
llm_params = {
"model": model_path,
"tensor_parallel_size": 2,
"trust_remote_code": True,
"dtype": "float16",
"enable_chunked_prefill": False,
"enable_prefix_caching": False,
"distributed_executor_backend": "mp",
"max_model_len": 16384,
"gpu_memory_utilization": 0.9,
}
llm = LLM(**llm_params)
messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": "你好！你是谁？"
}
]
}
]
sampling_params = SamplingParams(
max_tokens=200,
temperature=0.7,
top_k=50,
top_p=0.9
)
outputs = llm.chat(messages, sampling_params=sampling_params)
response = outputs[0].outputs[0].text
print("=" * 50)
print("Input content:", messages)
print("Model response:\n", response)
print("=" * 50)
if __name__ == "__main__":
main()
```
:::::
If you run this script successfully, you can see the info shown below:
```bash
==================================================
Input content: [{'role': 'user', 'content': [{'type': 'text', 'text': '你好！你是谁？'}]}]
Model response:
你好！我是通义千问，阿里巴巴集团旗下的超大规模语言模型。你可以叫我Qwen。我能够回答问题、创作文字，比如写故事、写公文、写邮件、写剧本、逻辑推理、编程等等，还能表达观点，玩游戏等。有什么我可以帮助你的吗？ 😊
==================================================
```
### Online Serving on Multi XPU
Start the vLLM server on a multi XPU:
```bash
python -m vllm.entrypoints.openai.api_server \
--host 0.0.0.0 \
--port 9988 \
--model /models/Qwen2.5-VL-32B-Instruct \
--gpu-memory-utilization 0.9 \
--trust-remote-code \
--max-model-len 32768 \
--tensor-parallel-size 2 \
--dtype float16 \
--max_num_seqs 128 \
--max_num_batched_tokens 32768 \
--block-size 128 \
--no-enable-prefix-caching \
--no-enable-chunked-prefill \
--distributed-executor-backend mp \
--served-model-name Qwen2.5-VL-32B-Instruct \
--compilation-config '{"splitting_ops": ["vllm.unified_attention",
"vllm.unified_attention_with_output",
"vllm.unified_attention_with_output_kunlun",
"vllm.mamba_mixer2",
"vllm.mamba_mixer",
"vllm.short_conv",
"vllm.linear_attention",
"vllm.plamo2_mamba_mixer",
"vllm.gdn_attention",
"vllm.sparse_attn_indexer"]}'
#Version 0.11.0
```
If your service start successfully, you can see the info shown below:
```bash
(APIServer pid=110552) INFO: Started server process [110552]
(APIServer pid=110552) INFO: Waiting for application startup.
(APIServer pid=110552) INFO: Application startup complete.
```
Once your server is started, you can query the model with input prompts:
```bash
curl http://localhost:9988/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen2.5-VL-32B-Instruct",
"prompt": "你好！你是谁?",
"max_tokens": 100,
"temperature": 0.7
}'
```
If you query the server successfully, you can see the info shown below (client):
```bash
{"id":"cmpl-9784668ac5bc4b4e975d0aa5ee8377c6","object":"text_completion","created":1768898088,"model":"Qwen2.5-VL-32B-Instruct","choices":[{"index":0,"text":" 你好！我是通义千问，阿里巴巴集团旗下的超大规模语言模型。你可以回答问题、创作文字，如写故事、公文、邮件、剧本等，还能表达\n","logprobs":null,"finish_reason":"stop","stop_reason":null,"token_ids":null,"prompt_logprobs":null,"prompt_token_ids":null}],"service_tier":null,"system_fingerprint":null,"usage":{"prompt_tokens":5,"total_tokens":45,"completion_tokens":40,"prompt_tokens_details":null},"kv_transfer_params":null}
```
Logs of the vllm server:
```bash
(APIServer pid=110552) INFO 01-20 16:34:48 [loggers.py:127] Engine 000: Avg prompt throughput: 0.5 tokens/s, Avg generation throughput: 0.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0%
(APIServer pid=110552) INFO: 127.0.0.1:17988 - "POST /v1/completions HTTP/1.1" 200 OK
(APIServer pid=110552) INFO 01-20 16:34:58 [loggers.py:127] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 3.4 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0%
(APIServer pid=110552) INFO 01-20 16:35:08 [loggers.py:127] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0%
```
Input an image for testing.Here,a python script is used:
```python
import requests
import base64
API_URL = "http://localhost:9988/v1/chat/completions"
MODEL_NAME = "Qwen2.5-VL-32B-Instruct"
IMAGE_PATH = "/images.jpeg"
def encode_image(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')
base64_image = encode_image(IMAGE_PATH)
payload = {
"model": MODEL_NAME,
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "你好！请描述一下这张图片。"
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{base64_image}"
}
}
]
}
],
"max_tokens": 300,
"temperature": 0.1,
"top_p": 0.9,
"top_k": 50
}
response = requests.post(API_URL, json=payload)
print(response.json())
```
If you query the server successfully, you can see the info shown below (client):
```bash
{'id': 'chatcmpl-9857119aed664a3e8f078efd90defdca', 'object': 'chat.completion', 'created': 1768898198, 'model': 'Qwen2.5-VL-32B-Instruct', 'choices': [{'index': 0, 'message': {'role': 'assistant', 'content': '你好！这张图片展示了一个标志，内容如下：\n\n1. **左侧图标**：\n - 一个黄色的圆形笑脸表情符号。\n - 笑脸的表情非常开心，眼睛眯成弯弯的形状，嘴巴张开露出牙齿，显得非常愉快。\n - 笑脸的双手在胸前做出拥抱的动作，手掌朝外，象征着“拥抱”或“友好的姿态”。\n\n2. **右侧文字**：\n - 文字是英文单词：“Hugging Face”。\n - 字体为黑色，字体风格简洁、现代，看起来像是无衬线字体（sans-serif）。\n\n3. **整体设计**：\n - 整个标志的设计非常简洁明了，颜色对比鲜明（黄色笑脸和黑色文字），背景为纯白色，给人一种干净、友好的感觉。\n - 笑脸和文字之间的间距适中，布局平衡。\n\n这个标志可能属于某个品牌或组织，名字为“Hugging Face”，从设计来看，它传达了一种友好、开放和积极的形象。', 'refusal': None, 'annotations': None, 'audio': None, 'function_call': None, 'tool_calls': [], 'reasoning_content': None}, 'logprobs': None, 'finish_reason': 'stop', 'stop_reason': None, 'token_ids': None}], 'service_tier': None, 'system_fingerprint': None, 'usage': {'prompt_tokens': 95, 'total_tokens': 311, 'completion_tokens': 216, 'prompt_tokens_details': None}, 'prompt_logprobs': None, 'prompt_token_ids': None, 'kv_transfer_params': None}
```
Logs of the vllm server:
```bash
(APIServer pid=110552) INFO: 127.0.0.1:19378 - "POST /v1/chat/completions HTTP/1.1" 200 OK
(APIServer pid=110552) INFO 01-20 16:36:49 [loggers.py:127] Engine 000: Avg prompt throughput: 9.5 tokens/s, Avg generation throughput: 21.6 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0%
(APIServer pid=110552) INFO 01-20 16:36:59 [loggers.py:127] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0%
```

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docs/source/tutorials/single_xpu_InternVL2_5-26B.md Normal file
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# Single XPU (InternVL2_5-26B)
## Run vllm-kunlun on Single XPU
Setup environment using container:
```bash
# !/bin/bash
# rundocker.sh
XPU_NUM=8
DOCKER_DEVICE_CONFIG=""
if [ $XPU_NUM -gt 0 ]; then
for idx in $(seq 0 $((XPU_NUM-1))); do
DOCKER_DEVICE_CONFIG="${DOCKER_DEVICE_CONFIG} --device=/dev/xpu${idx}:/dev/xpu${idx}"
done
DOCKER_DEVICE_CONFIG="${DOCKER_DEVICE_CONFIG} --device=/dev/xpuctrl:/dev/xpuctrl"
fi
export build_image="xxxxxxxxxxxxxxxxx"
docker run -itd ${DOCKER_DEVICE_CONFIG} \
--net=host \
--cap-add=SYS_PTRACE --security-opt seccomp=unconfined \
--tmpfs /dev/shm:rw,nosuid,nodev,exec,size=32g \
--cap-add=SYS_PTRACE \
-v /home/users/vllm-kunlun:/home/vllm-kunlun \
-v /usr/local/bin/xpu-smi:/usr/local/bin/xpu-smi \
--name "$1" \
-w /workspace \
"$build_image" /bin/bash
```
### Offline Inference on Single XPU
Start the server in a container:
```bash
from vllm import LLM, SamplingParams
def main():
model_path = "/models/InternVL2_5-26B"
llm_params = {
"model": model_path,
"tensor_parallel_size": 1,
"trust_remote_code": True,
"dtype": "float16",
"enable_chunked_prefill": False,
"enable_prefix_caching": False,
"distributed_executor_backend": "mp",
"max_model_len": 16384,
"gpu_memory_utilization": 0.9,
}
llm = LLM(**llm_params)
messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": "你好！你是谁？"
}
]
}
]
sampling_params = SamplingParams(
max_tokens=200,
temperature=0.7,
top_k=50,
top_p=0.9
)
outputs = llm.chat(messages, sampling_params=sampling_params)
response = outputs[0].outputs[0].text
print("=" * 50)
print("Input content:", messages)
print("Model response:\n", response)
print("=" * 50)
if __name__ == "__main__":
main()
```
:::::
If you run this script successfully, you can see the info shown below:
```bash
==================================================
Input content: [{'role': 'user', 'content': [{'type': 'text', 'text': '你好！你是谁？'}]}]
Model response:
你好！我是一个由人工智能驱动的助手，旨在帮助回答问题、提供信息和解决日常问题。请问有什么我可以帮助你的？
==================================================
```
### Online Serving on Single XPU
Start the vLLM server on a single XPU:
```bash
python -m vllm.entrypoints.openai.api_server \
--host 0.0.0.0 \
--port 9988 \
--model /models/InternVL2_5-26B \
--gpu-memory-utilization 0.9 \
--trust-remote-code \
--max-model-len 32768 \
--tensor-parallel-size 1 \
--dtype float16 \
--max_num_seqs 128 \
--max_num_batched_tokens 32768 \
--block-size 128 \
--no-enable-prefix-caching \
--no-enable-chunked-prefill \
--distributed-executor-backend mp \
--served-model-name InternVL2_5-26B \
--compilation-config '{"splitting_ops": ["vllm.unified_attention",
"vllm.unified_attention_with_output",
"vllm.unified_attention_with_output_kunlun",
"vllm.mamba_mixer2",
"vllm.mamba_mixer",
"vllm.short_conv",
"vllm.linear_attention",
"vllm.plamo2_mamba_mixer",
"vllm.gdn_attention",
"vllm.sparse_attn_indexer"]}
#Version 0.11.0
```
If your service start successfully, you can see the info shown below:
```bash
(APIServer pid=157777) INFO: Started server process [157777]
(APIServer pid=157777) INFO: Waiting for application startup.
(APIServer pid=157777) INFO: Application startup complete.
```
Once your server is started, you can query the model with input prompts:
```bash
curl http://localhost:9988/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "InternVL2_5-26B",
"prompt": "你好！你是谁?",
"max_tokens": 100,
"temperature": 0.7,
"top_p": 0.9,
"top_k": 50
}'
```
If you query the server successfully, you can see the info shown below (client):
```bash
{"id":"cmpl-23a24afd616d4a47910aeeccb20921ed","object":"text_completion","created":1768891222,"model":"InternVL2_5-26B","choices":[{"index":0,"text":" 你有什么问题吗?\n\n你好！我是书生·AI，很高兴能与你交流。请问有什么我可以帮助你的吗？无论是解答问题、提供信息还是其他方面的帮助，我都会尽力而为。请告诉我你的需求。","logprobs":null,"finish_reason":"stop","stop_reason":92542,"token_ids":null,"prompt_logprobs":null,"prompt_token_ids":null}],"service_tier":null,"system_fingerprint":null,"usage":{"prompt_tokens":6,"total_tokens":53,"completion_tokens":47,"prompt_tokens_details":null},"kv_transfer_params":null}
```
Logs of the vllm server:
```bash
(APIServer pid=161632) INFO: 127.0.0.1:56708 - "POST /v1/completions HTTP/1.1" 200 OK
(APIServer pid=161632) INFO 01-20 14:40:25 [loggers.py:127] Engine 000: Avg prompt throughput: 0.6 tokens/s, Avg generation throughput: 4.6 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0%
(APIServer pid=161632) INFO 01-20 14:40:35 [loggers.py:127] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0%
```
Input an image for testing.Here,a python script is used:
```python
import requests
import base64
API_URL = "http://localhost:9988/v1/chat/completions"
MODEL_NAME = "InternVL2_5-26B"
IMAGE_PATH = "/images.jpeg"
def encode_image(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')
base64_image = encode_image(IMAGE_PATH)
payload = {
"model": MODEL_NAME,
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "你好！请描述一下这张图片。"
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{base64_image}"
}
}
]
}
],
"max_tokens": 300,
"temperature": 0.1,
"top_p": 0.9,
"top_k": 50
}
response = requests.post(API_URL, json=payload)
print(response.json())
```
If you query the server successfully, you can see the info shown below (client):
```bash
{'id': 'chatcmpl-9aeab6044795458da04f2fdcf1d0445d', 'object': 'chat.completion', 'created': 1768891349, 'model': 'InternVL2_5-26B', 'choices': [{'index': 0, 'message': {'role': 'assistant', 'content': '你好！这张图片上有一个黄色的笑脸表情符号，双手合十，旁边写着“Hugging Face”。这个表情符号看起来很开心，似乎在表示拥抱或欢迎。', 'refusal': None, 'annotations': None, 'audio': None, 'function_call': None, 'tool_calls': [], 'reasoning_content': None}, 'logprobs': None, 'finish_reason': 'stop', 'stop_reason': 92542, 'token_ids': None}], 'service_tier': None, 'system_fingerprint': None, 'usage': {'prompt_tokens': 790, 'total_tokens': 827, 'completion_tokens': 37, 'prompt_tokens_details': None}, 'prompt_logprobs': None, 'prompt_token_ids': None, 'kv_transfer_params': None}
```
Logs of the vllm server:
```bash
(APIServer pid=161632) INFO: 127.0.0.1:58686 - "POST /v1/chat/completions HTTP/1.1" 200 OK
(APIServer pid=161632) INFO 01-20 14:42:35 [loggers.py:127] Engine 000: Avg prompt throughput: 79.0 tokens/s, Avg generation throughput: 3.7 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0%
(APIServer pid=161632) INFO 01-20 14:42:45 [loggers.py:127] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0%
```

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docs/source/tutorials/single_xpu_Qwen3-VL-32B.md Normal file
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# Single XPU (Qwen3-VL-32B)
## Run vllm-kunlun on Single XPU
Setup environment using container:
```bash
# !/bin/bash
# rundocker.sh
XPU_NUM=8
DOCKER_DEVICE_CONFIG=""
if [ $XPU_NUM -gt 0 ]; then
for idx in $(seq 0 $((XPU_NUM-1))); do
DOCKER_DEVICE_CONFIG="${DOCKER_DEVICE_CONFIG} --device=/dev/xpu${idx}:/dev/xpu${idx}"
done
DOCKER_DEVICE_CONFIG="${DOCKER_DEVICE_CONFIG} --device=/dev/xpuctrl:/dev/xpuctrl"
fi
export build_image="xxxxxxxxxxxxxxxxx"
docker run -itd ${DOCKER_DEVICE_CONFIG} \
--net=host \
--cap-add=SYS_PTRACE --security-opt seccomp=unconfined \
--tmpfs /dev/shm:rw,nosuid,nodev,exec,size=32g \
--cap-add=SYS_PTRACE \
-v /home/users/vllm-kunlun:/home/vllm-kunlun \
-v /usr/local/bin/xpu-smi:/usr/local/bin/xpu-smi \
--name "$1" \
-w /workspace \
"$build_image" /bin/bash
```
### Offline Inference on Single XPU
Start the server in a container:
```bash
from vllm import LLM, SamplingParams
def main():
model_path = "/models/Qwen3-VL-32B"
llm_params = {
"model": model_path,
"tensor_parallel_size": 1,
"trust_remote_code": True,
"dtype": "float16",
"enable_chunked_prefill": False,
"distributed_executor_backend": "mp",
"max_model_len": 16384,
"gpu_memory_utilization": 0.9,
}
llm = LLM(**llm_params)
messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": "tell a joke"
}
]
}
]
sampling_params = SamplingParams(
max_tokens=200,
temperature=1.0,
top_k=50,
top_p=1.0
)
outputs = llm.chat(messages, sampling_params=sampling_params)
response = outputs[0].outputs[0].text
print("=" * 50)
print("Input content:", messages)
print("Model response:\n", response)
print("=" * 50)
if __name__ == "__main__":
main()
```
:::::
If you run this script successfully, you can see the info shown below:
```bash
==================================================
Input content: [{'role': 'user', 'content': [{'type': 'text', 'text': 'tell a joke'}]}]
Model response:
Why don’t skeletons fight each other?
Because they don’t have the guts! 🦴😄
==================================================
```
### Online Serving on Single XPU
Start the vLLM server on a single XPU:
```bash
python -m vllm.entrypoints.openai.api_server \
--host 0.0.0.0 \
--port 9988 \
--model /models/Qwen3-VL-32B \
--gpu-memory-utilization 0.9 \
--trust-remote-code \
--max-model-len 32768 \
--tensor-parallel-size 1 \
--dtype float16 \
--max_num_seqs 128 \
--max_num_batched_tokens 32768 \
--block-size 128 \
--no-enable-prefix-caching \
--no-enable-chunked-prefill \
--distributed-executor-backend mp \
--served-model-name Qwen3-VL-32B \
--compilation-config '{"splitting_ops": ["vllm.unified_attention",
"vllm.unified_attention_with_output",
"vllm.unified_attention_with_output_kunlun",
"vllm.mamba_mixer2",
"vllm.mamba_mixer",
"vllm.short_conv",
"vllm.linear_attention",
"vllm.plamo2_mamba_mixer",
"vllm.gdn_attention",
"vllm.sparse_attn_indexer"]}
#Version 0.11.0
```
If your service start successfully, you can see the info shown below:
```bash
(APIServer pid=109442) INFO: Started server process [109442]
(APIServer pid=109442) INFO: Waiting for application startup.
(APIServer pid=109442) INFO: Application startup complete.
```
Once your server is started, you can query the model with input prompts:
```bash
curl http://localhost:9988/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen3-VL-32B",
"prompt": "你好！你是谁?",
"max_tokens": 100,
"temperature": 0
}'
```
If you query the server successfully, you can see the info shown below (client):
```bash
{"id":"cmpl-4f61fe821ff34f23a91baade5de5103e","object":"text_completion","created":1768876583,"model":"Qwen3-VL-32B","choices":[{"index":0,"text":" 你好！我是通义千问，是阿里云研发的超大规模语言模型。我能够回答问题、创作文字、编程等，还能根据你的需求进行多轮对话。有什么我可以帮你的吗？😊\n\n（温馨提示：我是一个AI助手，虽然我尽力提供准确和有用的信息，但请记得在做重要决策时，最好结合专业意见或进一步核实信息哦！）","logprobs":null,"finish_reason":"stop","stop_reason":null,"token_ids":null,"prompt_logprobs":null,"prompt_token_ids":null}],"service_tier":null,"system_fingerprint":null,"usage":{"prompt_tokens":5,"total_tokens":90,"completion_tokens":85,"prompt_tokens_details":null},"kv_transfer_params":null}
```
Logs of the vllm server:
```bash
(APIServer pid=109442) INFO: 127.0.0.1:19962 - "POST /v1/completions HTTP/1.1" 200 OK
(APIServer pid=109442) INFO 01-20 10:36:28 [loggers.py:127] Engine 000: Avg prompt throughput: 0.5 tokens/s, Avg generation throughput: 8.5 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0%
(APIServer pid=109442) INFO 01-20 10:36:38 [loggers.py:127] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0%
(APIServer pid=109442) INFO 01-20 10:43:23 [chat_utils.py:560] Detected the chat template content format to be 'openai'. You can set `--chat-template-content-format` to override this.
(APIServer pid=109442) INFO 01-20 10:43:28 [loggers.py:127] Engine 000: Avg prompt throughput: 9.0 tokens/s, Avg generation throughput: 6.9 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.5%, Prefix cache hit rate: 0.0%
```
Input an image for testing.Here,a python script is used:
```python
import requests
import base64
API_URL = "http://localhost:9988/v1/chat/completions"
MODEL_NAME = "Qwen3-VL-32B"
IMAGE_PATH = "/images.jpeg"
def encode_image(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')
base64_image = encode_image(IMAGE_PATH)
payload = {
"model": MODEL_NAME,
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "你好！请描述一下这张图片。"
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{base64_image}"
}
}
]
}
],
"max_tokens": 300,
"temperature": 0.1
}
response = requests.post(API_URL, json=payload)
print(response.json())
```
If you query the server successfully, you can see the info shown below (client):
```bash
{'id': 'chatcmpl-4b42fe46f2c84991b0af5d5e1ffad9ba', 'object': 'chat.completion', 'created': 1768877003, 'model': 'Qwen3-VL-32B', 'choices': [{'index': 0, 'message': {'role': 'assistant', 'content': '你好！这张图片展示的是“Hugging Face”的标志。\n\n图片左侧是一个黄色的圆形表情符号（emoji），它有着圆圆的眼睛、张开的嘴巴露出微笑，双手合拢在脸颊两侧，做出一个拥抱或欢迎的姿态，整体传达出友好、温暖和亲切的感觉。\n\n图片右侧是黑色的英文文字“Hugging Face”，字体简洁现代，与左侧的表情符号相呼应。\n\n整个标志设计简洁明了，背景为纯白色，突出了标志本身。这个标志属于Hugging Face公司，它是一家知名的开源人工智能公司，尤其在自然语言处理（NLP）领域以提供预训练模型（如Transformers库）和模型托管平台而闻名。\n\n整体来看，这个标志通过可爱的表情符号和直白的文字，成功传达了公司“拥抱”技术、开放共享、友好的品牌理念。', 'refusal': None, 'annotations': None, 'audio': None, 'function_call': None, 'tool_calls': [], 'reasoning_content': None}, 'logprobs': None, 'finish_reason': 'stop', 'stop_reason': None, 'token_ids': None}], 'service_tier': None, 'system_fingerprint': None, 'usage': {'prompt_tokens': 90, 'total_tokens': 266, 'completion_tokens': 176, 'prompt_tokens_details': None}, 'prompt_logprobs': None, 'prompt_token_ids': None, 'kv_transfer_params': None}
```
Logs of the vllm server:
```bash
(APIServer pid=109442) INFO: 127.0.0.1:26854 - "POST /v1/chat/completions HTTP/1.1" 200 OK
(APIServer pid=109442) INFO 01-20 10:43:38 [loggers.py:127] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 10.7 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0%
(APIServer pid=109442) INFO 01-20 10:43:48 [loggers.py:127] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0%
```