MiniCPM5-2B

from openai import OpenAI

client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")

# Thinking on (chat-template default).
resp = client.chat.completions.create(
    model="openbmb/MiniCPM5-2B",
    messages=[{"role": "user", "content": "What is 15% of 240?"}],
    extra_body={"chat_template_kwargs": {"enable_thinking": True}},
)
msg = resp.choices[0].message
print("Reasoning:", getattr(msg, "reasoning_content", None))
print("Answer:", msg.content)

# Thinking off — a direct answer, no deliberation segment.
resp = client.chat.completions.create(
    model="openbmb/MiniCPM5-2B",
    messages=[{"role": "user", "content": "What is 15% of 240?"}],
    extra_body={"chat_template_kwargs": {"enable_thinking": False}},
)
print("Without thinking:", resp.choices[0].message.content)

Reasoning: We are asked: "What is 15% of 240?" This is a simple percentage calculation. To find 15% of 240, we multiply 240 by 15% (which is 0.15). So: 240 × 0.15 = 36. Alternatively, we can think of it as (15/100) × 240 = (15 × 240)/100 = 3600/100 = 36. So the answer is 36.

We need to respond in a helpful way. The user might be testing or seeking quick answer. Let's provide a clear response with explanation if needed, but since it's straightforward, we can just give the answer directly or briefly explain.

Answer:

15% of 240 is **36**.

To calculate:
\( 240 \times 0.15 = 36 \)
Reasoning: None
Answer: To find 15% of 240, multiply 240 by 15% (which is 0.15):

\[
240 \times 0.15 = 36
\]

So, 15% of 240 is **36**.

from openai import OpenAI

client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get the current weather for a city.",
        "parameters": {
            "type": "object",
            "properties": {
                "city": {"type": "string", "description": "City name"},
                "date": {"type": "string", "description": "YYYY-MM-DD"},
            },
            "required": ["city"],
        },
    },
}]

resp = client.chat.completions.create(
    model="openbmb/MiniCPM5-2B",
    messages=[{"role": "user", "content": "What is the weather in Beijing today?"}],
    tools=tools,
)

msg = resp.choices[0].message
# With the qwen3 reasoning parser on, the thinking segment lands in
# `reasoning_content` and `content` may be empty on a tool-call turn.
print("Reasoning:", getattr(msg, "reasoning_content", None))
print("Content:", msg.content)
for call in (msg.tool_calls or []):
    print("Tool:", call.function.name, call.function.arguments)

Reasoning:
The user is asking about the weather in Beijing today. I have access to a get_weather function that can help with this. Let me call it to get the current weather for Beijing.

Content:


Tool: get_weather {"city": "Beijing"}