Kimi-K2.7-Code

from openai import OpenAI

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

response = client.chat.completions.create(
    model="moonshotai/Kimi-K2.7-Code",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://ofasys-multimodal-wlcb-3-toshanghai.oss-accelerate.aliyuncs.com/wpf272043/keepme/image/receipt.png"
                    }
                },
                {
                    "type": "text",
                    "text": "What is in this image? Describe it in detail."
                }
            ]
        }
    ]
)

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

This image shows a **paper receipt from Auntie Anne's**, the pretzel chain restaurant. Here's a detailed breakdown:

## Header
- At the top left is the Auntie Anne's logo (a pretzel with a halo)
- The store name "**Auntie Anne's**" is printed prominently at the top
- Some text below the store name appears blurred/redacted (likely store location, address, or transaction details)

## Purchase Details
- **Item**: CINNAMON SUGAR
- **Quantity & Price**: 1 × 17,000
- **Item Total**: 17,000

## Financial Summary
- **SUB TOTAL**: 17,000
- **GRAND TOTAL**: 17,000
- **CASH IDR**: 20,000 (customer paid 20,000 Indonesian Rupiah)
- **CHANGE DUE**: 3,000

## Physical Description
- The receipt is printed on white thermal paper
- Some information in the middle section and toward the bottom is intentionally blurred/obscured
- The paper appears slightly curved/wrinkled and is placed on a dark brown surface (likely a table or counter)

The transaction is in **Indonesian Rupiah (IDR)**, indicating this purchase was made at an Auntie Anne's location in Indonesia. The customer bought one Cinnamon Sugar pretzel for 17,000 IDR and received 3,000 IDR in change after paying with 20,000 IDR cash.

from openai import OpenAI

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

response = client.chat.completions.create(
    model="moonshotai/Kimi-K2.7-Code",
    messages=[
        {"role": "user", "content": "Which one is bigger, 9.11 or 9.9? Think carefully."}
    ]
)

print("====== Reasoning Content (Thinking Mode) ======")
print(response.choices[0].message.reasoning_content)
print("====== Response (Thinking Mode) ======")
print(response.choices[0].message.content)

====== Reasoning Content (Thinking Mode) ======
The user is asking which number is bigger: 9.11 or 9.9. This seems straightforward, but there's a viral internet debate about this due to decimal confusion.

Let me think carefully:
- 9.11 means 9 + 11/100 = 9.11
- 9.9 means 9 + 9/10 = 9.90

So 9.9 = 9.90, and 9.90 > 9.11 because 0.90 > 0.11.

The confusion often comes from people thinking of software versioning (where 9.11 comes after 9.9) or comparing the numbers after the decimal as whole numbers (11 vs 9, thinking 11 > 9).

So mathematically, 9.9 is clearly bigger. 9.9 - 9.11 = 0.79.

I should explain this clearly and address the common misconception.
====== Response (Thinking Mode) ======
Mathematically, **9.9 is bigger**.

Here's why:

**9.9 = 9.90**

When comparing decimals, you need to look at the same place values:
- 9.11 = 9 ones, 1 tenth, and 1 hundredth
- 9.9 = 9 ones, 9 tenths, and 0 hundredths (9.90)

Since **0.90 > 0.11**, it follows that **9.9 > 9.11**.

The difference is:
9.9 - 9.11 = 0.79

**Why people get confused:** Many mistakenly treat the decimals like whole numbers (thinking "11 is bigger than 9") or confuse this with software version numbering (where version 9.11 comes after version 9.9). But in standard mathematics, 9.9 is definitively larger.

from openai import OpenAI

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

messages = [
    {
        "role": "user",
        "content": "Tell me three random numbers."
    },
    {
        "role": "assistant",
        "reasoning_content": "I'll start by listing five numbers: 473, 921, 235, 215, 222, and I'll tell you the first three.",
        "content": "473, 921, 235"
    },
    {
        "role": "user",
        "content": "What are the other two numbers you have in mind?"
    }
]

response = client.chat.completions.create(
    model="moonshotai/Kimi-K2.7-Code",
    messages=messages,
    stream=False,
    max_tokens=4096,
)

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

from openai import OpenAI

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

# Define available tools
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"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "Temperature unit"
                    }
                },
                "required": ["location"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="moonshotai/Kimi-K2.7-Code",
    messages=[
        {"role": "user", "content": "What's the weather in Beijing?"}
    ],
    tools=tools,
    stream=True
)

# Process streaming response
tool_calls_accumulator = {}

for chunk in response:
    if chunk.choices and len(chunk.choices) > 0:
        delta = chunk.choices[0].delta

        if hasattr(delta, 'tool_calls') and delta.tool_calls:
            for tool_call in delta.tool_calls:
                index = tool_call.index
                if index not in tool_calls_accumulator:
                    tool_calls_accumulator[index] = {'name': None, 'arguments': ''}
                if tool_call.function:
                    if tool_call.function.name:
                        tool_calls_accumulator[index]['name'] = tool_call.function.name
                    if tool_call.function.arguments:
                        tool_calls_accumulator[index]['arguments'] += tool_call.function.arguments

        if delta.content:
            print(delta.content, end="", flush=True)

for index, tool_call in sorted(tool_calls_accumulator.items()):
    print(f"Tool Call: {tool_call['name']}")
    print(f"  Arguments: {tool_call['arguments']}")

Tool Call: get_weather
  Arguments: {"location": "Beijing"}

# Send tool result back to the model
messages = [
    {"role": "user", "content": "What's the weather in Beijing?"},
    {
        "role": "assistant",
        "content": None,
        "tool_calls": [{
            "id": "call_123",
            "type": "function",
            "function": {
                "name": "get_weather",
                "arguments": '{"location": "Beijing", "unit": "celsius"}'
            }
        }]
    },
    {
        "role": "tool",
        "tool_call_id": "call_123",
        "content": "The weather in Beijing is 22°C and sunny."
    }
]

final_response = client.chat.completions.create(
    model="moonshotai/Kimi-K2.7-Code",
    messages=messages
)

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

The weather in Beijing is currently **22°C and sunny**. ☀️

It's a nice, warm day there—great for being outdoors!

from openai import OpenAI

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

tools = [
    {
        "type": "function",
        "function": {
            "name": "search_product",
            "description": "Search for a product by name or description",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "The product name or description to search for"
                    }
                },
                "required": ["query"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="moonshotai/Kimi-K2.7-Code",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://ofasys-multimodal-wlcb-3-toshanghai.oss-accelerate.aliyuncs.com/wpf272043/keepme/image/receipt.png"
                    }
                },
                {
                    "type": "text",
                    "text": "Can you identify this product and search for similar items?"
                }
            ]
        }
    ],
    tools=tools
)

msg = response.choices[0].message

# Print reasoning process
if msg.reasoning_content:
    print("=== Reasoning ===")
    print(msg.reasoning_content)

# Print response content
if msg.content:
    print("=== Content ===")
    print(msg.content)

# Print tool calls
if msg.tool_calls:
    print("=== Tool Calls ===")
    for tc in msg.tool_calls:
        print(f"  Function: {tc.function.name}")
        print(f"  Arguments: {tc.function.arguments}")

=== Reasoning ===
The user wants me to identify the product from the receipt and search for similar items. Looking at the receipt, it's from Auntie Anne's and the item purchased is "CINNAMON SUGAR" for 17,000 IDR. This is likely a Cinnamon Sugar Pretzel from Auntie Anne's, which is a popular pretzel chain.

I should search for this product using the search_product function. The query should be something like "Auntie Anne's Cinnamon Sugar Pretzel" or just "Cinnamon Sugar Pretzel" to find similar items.
=== Content ===
Based on the receipt, the product is a **Cinnamon Sugar Pretzel** from **Auntie Anne's** (a popular pretzel bakery chain). The receipt shows it was purchased for 17,000 Indonesian Rupiah (IDR).

Let me search for this product and similar items for you.
=== Tool Calls ===
  Function: search_product
  Arguments: {"query":"Auntie Anne's Cinnamon Sugar Pretzel"}

sglang serve \
  --model-path moonshotai/Kimi-K2.7-Code \
  --tp 8 \
  --reasoning-parser kimi_k2 \
  --tool-call-parser kimi_k2 \
  --trust-remote-code \
  --host 0.0.0.0 \
  --port 30000

SGLANG_USE_AITER=1 \
HIP_FORCE_DEV_KERNARG=1 \
SGLANG_EXPERT_PARALLEL_SIZE=1 \
SGLANG_USE_DYNAMIC_MXFP4_LINEAR=0 \
TORCH_BLAS_PREFER_HIPBLASLT=1 \
TENSILE_STREAMK_DYNAMIC_GRID=6 \
AITER_QUICK_REDUCE_QUANTIZATION=INT4 \
AITER_USE_FLYDSL_MOE_SORTING=1 \
AITER_AR_1STAGE_MAX_KB=512 \
AITER_MXFP4_INTERMEDIATE=1 \
ROCM_QUICK_REDUCE_QUANTIZATION=INT4 \
sglang serve \
  --model-path amd/Kimi-K2.7-Code-MXFP4 \
  --tp 4 \
  --trust-remote-code \
  --attention-backend aiter \
  --mem-fraction-static 0.90 \
  --kv-cache-dtype fp8_e4m3 \
  --disable-radix-cache \
  --enable-aiter-allreduce-fusion \
  --host 0.0.0.0 \
  --port 30000

python3 benchmark/gsm8k/bench_sglang.py \
  --num-questions 2000 \
  --parallel <PARALLEL> \
  --port 30000

python3 -m sglang.bench_serving \
  --backend sglang \
  --model amd/Kimi-K2.7-Code-MXFP4 \
  --dataset-name random \
  --random-input-len 8192 \
  --random-output-len 1024 \
  --random-range-ratio 0.8 \
  --max-concurrency <C> \
  --num-prompts <C x 5> \
  --host 0.0.0.0 \
  --port 30000