Step3-VL-10B

3.2 구성 팁

• 단일 GPU 배포: Step3-VL-10B는 BF16 정밀도로 단일 GPU에 여유롭게 맞으며 텐서 병렬 처리가 필요 없어요.
• 메모리 관리: 필요하면 --context-length을 낮춰 메모리를 절약하세요. 32768 값이 대부분의 시나리오에 충분해요.
• FP8 양자화: FP8 양자화를 사용해 품질을 유지하면서 메모리 사용을 더 줄이세요.

4. 모델 호출

4.1 기본 사용

기본 API 사용과 요청 예제는 다음을 참조하세요:

• SGLang Basic Usage Guide
• SGLang OpenAI Vision API Guide

4.2 고급 사용

4.2.1 멀티모달 입력

Step3-VL-10B는 이미지 입력을 지원해요. 이미지 입력이 있는 기본 예제:

from openai import OpenAI

client = OpenAI(
    api_key="EMPTY",
    base_url="http://localhost:30000/v1",
    timeout=3600
)

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": "Read all the text in the image."
            }
        ]
    }
]

start = time.time()
response = client.chat.completions.create(
    model="stepfun-ai/Step3-VL-10B",
    messages=messages,
    max_tokens=2048,
    extra_body={"top_k": -1}
)
print(f"Response costs: {time.time() - start:.2f}s")
print(f"Generated text: {response.choices[0].message.content}")

예제 출력:

Response costs: 5.89s
Generated text: Auntie Anne's

CINNAMON SUGAR
1 × 17,000                17,000

SUB TOTAL                    17,000

GRAND TOTAL                 17,000

CASH IDR                    20,000

CHANGE DUE                 3,000

다중 이미지 입력 예제:

Step3-VL-10B는 비교나 분석을 위해 단일 요청에서 여러 이미지를 처리할 수 있어요:

from openai import OpenAI

client = OpenAI(
    api_key="EMPTY",
    base_url="http://localhost:30000/v1",
    timeout=3600
)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://www.civitatis.com/f/china/hong-kong/guia/taxi.jpg"
                }
            },
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://cdn.cheapoguides.com/wp-content/uploads/sites/7/2025/05/GettyImages-509614603-1280x600.jpg"
                }
            },
            {
                "type": "text",
                "text": "Compare these two images and describe the differences in 100 words or less."
            }
        ]
    }
]

start = time.time()
response = client.chat.completions.create(
    model="stepfun-ai/Step3-VL-10B",
    messages=messages,
    max_tokens=2048,
    extra_body={"top_k": -1}
)
print(f"Response costs: {time.time() - start:.2f}s")
print(f"Generated text: {response.choices[0].message.content}")

예제 출력:

Response costs: 3.24s
Generated text: First image: Single red Hong Kong taxi close - up, clear license plate (RX 5004), "4 SEATS" sticker, urban street with shops behind. Second image: Aerial view of many taxis (red, green) on a highway with a viaduct, some hoods open, dense arrangement. Differences: Scale (single vs many), perspective (close - up vs aerial), context (street shops vs highway), and taxi conditions (normal vs some open hoods).

4.2.2 Reasoning Parser

Step3-VL-10B는 reasoning 모드를 지원해요. 배포 시 reasoning parser를 활성화해 thinking과 content 섹션을 분리하세요:

python -m sglang.launch_server \
  --model stepfun-ai/Step3-VL-10B \
  --reasoning-parser deepseek-r1 \
  --host 0.0.0.0 \
  --port 30000 \
  --trust-remote-code

Thinking 프로세스가 있는 스트리밍:

from openai import OpenAI

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

# Enable streaming to see the thinking process in real-time
response = client.chat.completions.create(
    model="stepfun-ai/Step3-VL-10B",
    messages=[
        {"role": "user", "content": "Solve this problem step by step: What is 15% of 240?"}
    ],
    temperature=0.7,
    max_tokens=2048,
    stream=True,
    extra_body={"top_k": -1}
)

# Process the stream
has_thinking = False
has_answer = False
thinking_started = False

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

        # Print thinking process
        if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
            if not thinking_started:
                print("=============== Thinking =================", flush=True)
                thinking_started = True
            has_thinking = True
            print(delta.reasoning_content, end="", flush=True)

        # Print answer content
        if delta.content:
            # Close thinking section and add content header
            if has_thinking and not has_answer:
                print("\n=============== Content =================", flush=True)
                has_answer = True
            print(delta.content, end="", flush=True)

print()

예제 출력:

=============== Thinking =================
Okay, let's see. The problem is asking for 15% of 240. Hmm, I need to remember how to calculate percentages. So, percentage means "per hundred," right? So, 15% is the same as 15 per 100, or 15/100. To find a percentage of a number, I think you convert the percentage to a decimal and then multiply it by the number. Let me check that.

First, 15% as a decimal. To convert a percentage to a decimal, you divide by 100. So 15 divided by 100 is 0.15. Yeah, that's right. So 15% is 0.15 in decimal form. Then, to find 15% of 24
0, I need to multiply 0.15 by 240. Let me do that calculation.

Let me write it out: 0.15 * 240. Let's compute that. Maybe break it down. 0.1 is 10%, and 0.05 is 5%, so 10% of 240 is 24, and 5% of 240 is 12. Then 10% + 5% is 15%, so 24 + 12 is 36. Oh,
 that's a good way to check. So 15% is 10% plus 5%, which adds up to 36. Let me verify with the decimal method. 0.15 * 240. Let's multiply 240 by 0.1 first: 24. Then 240 by 0.05: 12. Adding them gives 36. Yep, that matches. Alternatively, 240 * 15 = 3600, and then divide by 100 (since it's per hundred), so 3600 / 100 = 36. That's another way. So 15% of 240 is 36. Let me make sure I didn't make a mistake. Let's check with another method. 10% of 240 is 24, 20% would be 48, so 15% is halfway between 10% and 20%, which is (24 + 48)/2 = 36. Yep, that works too
. So all methods point to 36. I think that's the answer.

=============== Content =================

To solve the problem "What is 15% of 240?" step by step:

---

### **Step 1: Understand the concept of percentage**
A percentage represents a portion of a whole. Specifically, "percent" means "per hundred." So, 15% means **15 out of 100**, or **15/100**.

---

### **Step 2: Convert the percentage to a decimal**
To use percentages in calculations, convert them to decimals by dividing by 100:
$$
15\% = \frac{15}{100} = 0.15
$$

---

### **Step 3: Multiply the decimal by the given number**
Now, multiply 0.15 (the decimal form of 15%) by 240:
$$
0.15 \times 240 = 36
$$

---

### **Alternative Verification Methods**

#### **Method A: Break into parts**
- 10% of 240 = $ 0.10 \times 240 = 24 $
- 5% of 240 = $ 0.05 \times 240 = 12 $
- Add them: $ 24 + 12 = 36 $

#### **Method B: Use direct multiplication**
- $ 15\% \text{ of } 240 = \frac{15}{100} \times 240 = \frac{3600}{100} = 36 $

#### **Method C: Estimate using known percentages**
- 20% of 240 = $ 0.20 \times 240 = 48 $
- 10% of 240 = $ 0.10 \times 240 = 24 $
- 15% is halfway between 10% and 20%: $ \frac{24 + 48}{2} = 36 $

---

### **Final Answer**
$$
\boxed{36}
$$

참고: reasoning parser는 모델의 단계별 thinking 프로세스를 캡처해 모델이 결론에 어떻게 도달하는지 볼 수 있게 해줘요.

4.2.3 Tool Calling

Step3-VL-10B는 툴 호출 능력을 지원해요. 툴 호출 파서를 활성화하세요:

python -m sglang.launch_server \
  --model stepfun-ai/Step3-VL-10B \
  --reasoning-parser deepseek-r1 \
  --tool-call-parser hermes \
  --host 0.0.0.0 \
  --port 30000 \
  --trust-remote-code

Python 예제 (Thinking 프로세스 포함):

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"]
            }
        }
    }
]

# Make request with streaming to see thinking process
response = client.chat.completions.create(
    model="stepfun-ai/Step3-VL-10B",
    messages=[
        {"role": "user", "content": "What's the weather in Beijing?"}
    ],
    tools=tools,
    temperature=0.7,
    stream=True,
    extra_body={"top_k": -1}
)

# Process streaming response
thinking_started = False
has_thinking = False
tool_calls_accumulator = {}

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

        # Print thinking process
        if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
            if not thinking_started:
                print("=============== Thinking =================", flush=True)
                thinking_started = True
            has_thinking = True
            print(delta.reasoning_content, end="", flush=True)

        # Accumulate tool calls
        if hasattr(delta, 'tool_calls') and delta.tool_calls:
            # Close thinking section if needed
            if has_thinking and thinking_started:
                print("\n=============== Content =================\n", flush=True)
                thinking_started = False

            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

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

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

print()

예제 출력:

=============== Thinking =================
The user is asking about the weather in Beijing. I have a function called "get_weather" that can provide weather information for a location. Let me check the parameters:

- location: required (string) - "Beijing"
- unit: optional (string, enum: ["celsius", "fahrenheit"]) - not specified by the user, so I won't include it

I should call the function with location="Beijing".

<tool_calls>

=============== Content =================

</tool_calls>Tool Call: get_weather
   Arguments: {"location": "Beijing"}

툴 호출 결과 처리:

# After getting the tool call, execute the function
def get_weather(location, unit="celsius"):
    # Your actual weather API call here
    return f"The weather in {location} is 22°{unit[0].upper()} and sunny."

# 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": get_weather("Beijing", "celsius")
    }
]

final_response = client.chat.completions.create(
    model="stepfun-ai/Step3-VL-10B",
    messages=messages,
    temperature=0.7,
    extra_body={"top_k": -1}
)

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

참고:

• reasoning parser는 모델이 툴을 어떻게 사용하기로 결정하는지 보여줘요.
• 툴 호출은 함수 이름과 인자로 명확히 표시돼요.
• 그런 다음 함수를 실행하고 결과를 다시 보내 대화를 계속할 수 있어요.

5. 벤치마크

5.1 속도 벤치마크

테스트 환경:

• 하드웨어: NVIDIA B200 GPU (1x)
• 모델: stepfun-ai/Step3-VL-10B
• Tensor Parallelism: 1
• sglang 버전: 0.5.8+

무작위 이미지로 SGLang의 내장 벤치마킹 도구를 사용해 성능 평가를 수행해요.

5.1.1 지연 시간 민감 벤치마크

• 모델 배포 명령:

python -m sglang.launch_server \
  --model stepfun-ai/Step3-VL-10B \
  --host 0.0.0.0 \
  --port 30000 \
  --trust-remote-code

• 벤치마크 명령:

python3 -m sglang.bench_serving \
  --backend sglang-oai-chat \
  --host 127.0.0.1 \
  --port 30000 \
  --model stepfun-ai/Step3-VL-10B \
  --dataset-name image \
  --image-count 2 \
  --image-resolution 720p \
  --random-input-len 128 \
  --random-output-len 1024 \
  --num-prompts 10 \
  --max-concurrency 1

• 결과:

============ Serving Benchmark Result ============
Backend:                                 sglang-oai-chat
Traffic request rate:                    inf
Max request concurrency:                 1
Successful requests:                     10
Benchmark duration (s):                  30.85
Total input tokens:                      14120
Total input text tokens:                 720
Total input vision tokens:               13400
Total generated tokens:                  4220
Total generated tokens (retokenized):    4217
Request throughput (req/s):              0.32
Input token throughput (tok/s):          457.71
Output token throughput (tok/s):         136.79
Peak output token throughput (tok/s):    240.00
Peak concurrent requests:                2
Total token throughput (tok/s):          594.50
Concurrency:                             1.00
----------------End-to-End Latency----------------
Mean E2E Latency (ms):                   3083.40
Median E2E Latency (ms):                 2747.00
P90 E2E Latency (ms):                    4574.50
P99 E2E Latency (ms):                    5462.49
---------------Time to First Token----------------
Mean TTFT (ms):                          1327.69
Median TTFT (ms):                        1341.01
P99 TTFT (ms):                           1486.11
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          4.16
Median TPOT (ms):                        4.17
P99 TPOT (ms):                           4.18
---------------Inter-Token Latency----------------
Mean ITL (ms):                           4.17
Median ITL (ms):                         4.18
P95 ITL (ms):                            4.30
P99 ITL (ms):                            4.38
Max ITL (ms):                            8.24
==================================================

5.1.2 처리량 민감 벤치마크

• 벤치마크 명령:

python3 -m sglang.bench_serving \
  --backend sglang-oai-chat \
  --host 127.0.0.1 \
  --port 30000 \
  --model stepfun-ai/Step3-VL-10B \
  --dataset-name image \
  --image-count 2 \
  --image-resolution 720p \
  --random-input-len 128 \
  --random-output-len 1024 \
  --num-prompts 1000 \
  --max-concurrency 100

• 결과:

============ Serving Benchmark Result ============
Backend:                                 sglang-oai-chat
Traffic request rate:                    inf
Max request concurrency:                 100
Successful requests:                     1000
Benchmark duration (s):                  976.52
Total input tokens:                      1416949
Total input text tokens:                 76949
Total input vision tokens:               1340000
Total generated tokens:                  510855
Total generated tokens (retokenized):    510526
Request throughput (req/s):              1.02
Input token throughput (tok/s):          1451.02
Output token throughput (tok/s):         523.14
Peak output token throughput (tok/s):    20429.00
Peak concurrent requests:                103
Total token throughput (tok/s):          1974.16
Concurrency:                             99.81
----------------End-to-End Latency----------------
Mean E2E Latency (ms):                   97463.22
Median E2E Latency (ms):                 91872.75
P90 E2E Latency (ms):                    118553.42
P99 E2E Latency (ms):                    198445.56
---------------Time to First Token----------------
Mean TTFT (ms):                          94379.07
Median TTFT (ms):                        87163.09
P99 TTFT (ms):                           194871.41
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          5.89
Median TPOT (ms):                        5.72
P99 TPOT (ms):                           23.58
---------------Inter-Token Latency----------------
Mean ITL (ms):                           6.05
Median ITL (ms):                         0.13
P95 ITL (ms):                            0.56
P99 ITL (ms):                            3.99
Max ITL (ms):                            97551.06
==================================================

5.2 정확도 벤치마크

5.2.1 MMMU 벤치마크

MMMU 데이터셋으로 모델의 정확도를 평가할 수 있어요:

• 모델 배포 명령:

python -m sglang.launch_server \
  --model stepfun-ai/Step3-VL-10B \
  --host 0.0.0.0 \
  --port 30000 \
  --trust-remote-code

• 벤치마크 명령:

python3 benchmark/mmmu/bench_sglang.py \
    --port 30000 \
    --concurrency 64

• 결과:

Benchmark time: 934.6179109360091
answers saved to: ./answer_sglang.json
Evaluating...
answers saved to: ./answer_sglang.json
{'Accounting': {'acc': 0.667, 'num': 30},
 'Agriculture': {'acc': 0.367, 'num': 30},
 'Architecture_and_Engineering': {'acc': 0.4, 'num': 30},
 'Art': {'acc': 0.467, 'num': 30},
 'Art_Theory': {'acc': 0.5, 'num': 30},
 'Basic_Medical_Science': {'acc': 0.367, 'num': 30},
 'Biology': {'acc': 0.3, 'num': 30},
 'Chemistry': {'acc': 0.467, 'num': 30},
 'Clinical_Medicine': {'acc': 0.567, 'num': 30},
 'Computer_Science': {'acc': 0.467, 'num': 30},
 'Design': {'acc': 0.567, 'num': 30},
 'Diagnostics_and_Laboratory_Medicine': {'acc': 0.3, 'num': 30},
 'Economics': {'acc': 0.6, 'num': 30},
 'Electronics': {'acc': 0.567, 'num': 30},
 'Energy_and_Power': {'acc': 0.633, 'num': 30},
 'Finance': {'acc': 0.733, 'num': 30},
 'Geography': {'acc': 0.333, 'num': 30},
 'History': {'acc': 0.533, 'num': 30},
 'Literature': {'acc': 0.533, 'num': 30},
 'Manage': {'acc': 0.6, 'num': 30},
 'Marketing': {'acc': 0.767, 'num': 30},
 'Materials': {'acc': 0.6, 'num': 30},
 'Math': {'acc': 0.7, 'num': 30},
 'Mechanical_Engineering': {'acc': 0.333, 'num': 30},
 'Music': {'acc': 0.4, 'num': 30},
 'Overall': {'acc': 0.523, 'num': 900},
 'Overall-Art and Design': {'acc': 0.483, 'num': 120},
 'Overall-Business': {'acc': 0.673, 'num': 150},
 'Overall-Health and Medicine': {'acc': 0.513, 'num': 150},
 'Overall-Humanities and Social Science': {'acc': 0.492, 'num': 120},
 'Overall-Science': {'acc': 0.5, 'num': 150},
 'Overall-Tech and Engineering': {'acc': 0.481, 'num': 210},
 'Pharmacy': {'acc': 0.6, 'num': 30},
 'Physics': {'acc': 0.7, 'num': 30},
 'Psychology': {'acc': 0.467, 'num': 30},
 'Public_Health': {'acc': 0.733, 'num': 30},
 'Sociology': {'acc': 0.433, 'num': 30}}
eval out saved to ./val_sglang.json
Overall accuracy: 0.523

더 알아보기 (Learn more)

• Step3-VL-10B
• SGLang 설치 가이드