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To run Qwen3.8-27B locally without generating thinking output, serve the official Qwen/Qwen3.8-27B checkpoint with a supported inference framework and send enable_thinking: false inside the chat-template settings. With a local OpenAI-compatible endpoint, the model card’s Python example places that setting under extra_body.chat_template_kwargs—not as a top-level field.
Choose a local inference framework
Qwen lists Transformers, vLLM, SGLang and TokenSpeed as local inference options. The checkpoint identifier is Qwen/Qwen3.8-27B; Qwen says official weights are available from its repository and ModelScope. The repository’s availability entry is dated August 14, 2026. For framework-specific options and examples, consult the official Hugging Face model card.
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Transformers: start an OpenAI-compatible server
Qwen documents this server command:
transformers serve Qwen/Qwen3.8-27B --port 8000 --continuous-batchingThe local OpenAI-compatible API is available at http://localhost:8000/v1. Use that base URL in your client and pass the non-thinking setting with each chat-completions request.
vLLM: serve the checkpoint
The model card’s vLLM launch example is:
vllm serve "Qwen/Qwen3.8-27B"After starting the server, send chat-completions requests to its local endpoint, using the same chat-template setting shown below. Check the current vLLM instructions for endpoint details and supported options in your installed version.
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SGLang or TokenSpeed
Qwen also documents SGLang and TokenSpeed deployment routes. Their commands and configuration differ from the Transformers example, so follow the current instructions for the framework you choose and verify how that version accepts chat-template settings.
Disable thinking in a local chat-completions request
Thinking is enabled by default. For the model card’s local OpenAI-compatible Python example, put the setting inside extra_body and then inside chat_template_kwargs:
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from openai import OpenAI
client = OpenAI(
base_url="http://localhost:8000/v1",
api_key="EMPTY",
)
response = client.chat.completions.create(
model="Qwen/Qwen3.8-27B",
messages=[{"role": "user", "content": "Explain how a solar panel works."}],
extra_body={
"chat_template_kwargs": {
"enable_thinking": False
}
},
)
print(response.choices[0].message.content)The key is chat_template_kwargs.enable_thinking. Do not copy Qwen Cloud’s syntax into a local request: for Cloud, the model card specifies a direct enable_thinking: false field, whereas its local example uses the nested chat-template setting.
Use the recommended non-thinking sampling settings where supported
Qwen’s model card recommends these sampling values for non-thinking mode. Support varies by framework, so use only parameters accepted by your server and client.
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| Parameter | Qwen’s suggested value |
|---|---|
temperature |
0.7 |
top_p |
0.80 |
top_k |
20 |
min_p |
0.0 |
presence_penalty |
1.5 |
repetition_penalty |
1.0 |
These are model-card recommendations, not a guarantee that every serving stack exposes every setting or interprets it identically. Qwen explicitly notes that sampling-parameter support depends on the inference framework.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Understand what the switch changes—and what it does not
enable_thinking: false disables thinking-mode generation for the request. It is distinct from preserve_thinking, which controls whether thinking is retained in conversation history; changing history retention is not the switch for turning thinking generation off.
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The model card describes Qwen3.8-27B as a 27-billion-parameter causal language model with a vision encoder and image and video understanding capabilities. Its native context length is 262,144 tokens; the card describes extension up to 1,000,000 tokens using RoPE scaling such as YaRN. These are model specifications, not a promise that a particular local framework, hardware setup or request will support those limits unchanged.
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The official materials cited here do not specify a minimum GPU memory or system RAM requirement. Do not treat the model’s 27B parameter count as a VRAM estimate. Memory needs depend on the checkpoint precision or quantization, context length, and serving configuration. Confirm the requirements for the exact weights, framework and workload you intend to run before downloading or launching the model.
Quick Recap
Troubleshoot common setup issues
- Thinking still appears: Check that the request includes
extra_body.chat_template_kwargs.enable_thinkingset tofalseand that the server/client supports passing chat-template options. - The request rejects a sampling parameter: Remove or adapt the unsupported option; framework support varies, and the recommended list is not universal.
- The server cannot load the checkpoint or settings: Verify the checkpoint name and consult the current documentation for your installed framework version. Qwen cautions that framework versions and inference behavior change.
- Memory is insufficient: Reassess precision or quantization, the requested context length and serving setup against the requirements of your actual checkpoint and backend; the official materials do not establish a universal minimum configuration.
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