Inkling

from openai import OpenAI

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

resp = client.chat.completions.create(
    model="thinkingmachines/Inkling-NVFP4",
    messages=[{"role": "user", "content": "What is 17 times 24?"}],
    extra_body={"chat_template_kwargs": {"thinking": True}},
)
msg = resp.choices[0].message
print("Reasoning:", getattr(msg, "reasoning_content", None))
print("Answer:", msg.content)

Reasoning: The user is asking for the product of 17 and 24. Let me calculate that.

17 × 24

I can break this down:
17 × 20 = 340
17 × 4 = 68
340 + 68 = 408

Alternatively:
24 × 10 = 240
24 × 7 = 168
240 + 168 = 408

So the answer is 408.
Answer: 17 times 24 is **408**.

Here's a quick breakdown:
- 17 × 20 = 340
- 17 × 4 = 68
- 340 + 68 = **408**

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 location",
            "parameters": {
                "type": "object",
                "properties": {"location": {"type": "string", "description": "The city name"}},
                "required": ["location"],
            },
        },
    }
]

resp = client.chat.completions.create(
    model="thinkingmachines/Inkling-NVFP4",
    messages=[{"role": "user", "content": "What's the weather in Beijing?"}],
    tools=tools,
)
msg = resp.choices[0].message
print("Reasoning:", getattr(msg, "reasoning_content", None))
print("Content:", msg.content)
print("Tool calls:", msg.tool_calls)

Reasoning: The user is asking for the weather in Beijing. I have a tool called `get_weather` that can get the current weather for a location. Let me call it with "Beijing" as the location.
Content:
Tool calls: [ChatCompletionMessageFunctionToolCall(id='call_98f772f3a0044f45b80c5ba5', function=Function(arguments='{"location": "Beijing"}', name='get_weather'), type='function', index=0)]

import base64
from openai import OpenAI

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

with open("image.png", "rb") as f:
    image_b64 = base64.b64encode(f.read()).decode()
with open("audio.wav", "rb") as f:
    audio_b64 = base64.b64encode(f.read()).decode()

resp = client.chat.completions.create(
    model="thinkingmachines/Inkling-NVFP4",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_b64}"}},
                {"type": "audio_url", "audio_url": {"url": f"data:audio/wav;base64,{audio_b64}"}},
                {"type": "text", "text": "Describe the image, then transcribe the audio."},
            ],
        }
    ],
    max_tokens=1024,
)
print(resp.choices[0].message.content)

from openai import OpenAI

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

# Option A (recommended): "<model>:<adapter-name>" in the model field
resp = client.chat.completions.create(
    model="thinkingmachines/Inkling-NVFP4:lora0",
    messages=[{"role": "user", "content": "Summarize the changelog."}],
)

# Option B: explicit lora_path via extra_body
resp = client.chat.completions.create(
    model="thinkingmachines/Inkling-NVFP4",
    messages=[{"role": "user", "content": "Summarize the changelog."}],
    extra_body={"lora_path": "lora0"},
)

print(resp.choices[0].message.content)