iApp vLLM Gateway

Available Aliases

Alias	Model
qwen3.5-122b	Qwen/Qwen3.5-122B-A10B-FP8
qwen3.5-35b	Qwen/Qwen3.5-35B-A3B-FP8
qwen3.5-35b-multi	Qwen/Qwen3.5-35B-A3B-FP8

Use either the alias or full model name in the model field.

Basic Request

curl

Python

curl http://api3-siamai.aieat.or.th/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3.5-122b",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

from openai import OpenAI

client = OpenAI(base_url="http://api3-siamai.aieat.or.th/v1", api_key="none")

response = client.chat.completions.create(
    model="qwen3.5-122b",
    messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)

Streaming

curl

Python

curl http://api3-siamai.aieat.or.th/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3.5-122b",
    "messages": [{"role": "user", "content": "Hello"}],
    "stream": true
  }'

stream = client.chat.completions.create(
    model="qwen3.5-122b",
    messages=[{"role": "user", "content": "Hello"}],
    stream=True
)
for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

Thinking Mode

Qwen3.5 models think by default. Disable with enable_thinking: false or append /no_think to your prompt.

Default (on)

Disable

# Thinking is enabled by default
response = client.chat.completions.create(
    model="qwen3.5-122b",
    messages=[{"role": "user", "content": "Solve: 2x + 3 = 7"}]
)
# Response includes reasoning in <think>...</think> tags

# Option 1: API parameter
response = client.chat.completions.create(
    model="qwen3.5-122b",
    messages=[{"role": "user", "content": "Hello"}],
    extra_body={"chat_template_kwargs": {"enable_thinking": False}}
)

# Option 2: Prompt suffix
messages=[{"role": "user", "content": "Hello /no_think"}]

Structured JSON Output

Force valid JSON output using response_format.

JSON mode

JSON schema

response = client.chat.completions.create(
    model="qwen3.5-122b",
    messages=[{"role": "user", "content": "List 3 countries as JSON"}],
    response_format={"type": "json_object"}
)

response = client.chat.completions.create(
    model="qwen3.5-122b",
    messages=[{
        "role": "user",
        "content": "Extract: John is 30 and lives in Tokyo"
    }],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "person",
            "schema": {
                "type": "object",
                "properties": {
                    "name": {"type": "string"},
                    "age": {"type": "integer"},
                    "city": {"type": "string"}
                },
                "required": ["name", "age", "city"]
            }
        }
    }
)
# {"name": "John", "age": 30, "city": "Tokyo"}

Parameters

Parameter	Default	Description
temperature	0.7	Randomness (0.0 - 2.0)
max_tokens	-	Response length limit
top_p	0.9	Nucleus sampling (0.0 - 1.0)
top_k	20	Vocabulary sampling
frequency_penalty	0.0	Repetition penalty
stop	-	Stop sequences