Intern-S1 Usage Guide¶

Intern-S1 is a vision-language model that is developed by Shanghai AI Laboratory. Latest vLLM already supports Intern-S1. You can install it using the following method:

Installing vLLM¶

uv venv
source .venv/bin/activate
uv pip install -U vllm --torch-backend auto

Installing vLLM (For AMD ROCm: MI300x/MI325x/MI355x)¶

uv pip install vllm --extra-index-url https://wheels.vllm.ai/rocm/0.14.1/rocm700

⚠️ The vLLM wheel for ROCm is compatible with Python 3.12, ROCm 7.0, and glibc >= 2.35. If your environment is incompatible, please use docker flow in vLLM

Launching Intern-S1 with vLLM¶

Serving BF16 Model on 8xH800 GPUs (80GB × 8)¶

vllm serve internlm/Intern-S1 \
  --trust-remote-code \
  --tensor-parallel-size 8 \
  --enable-auto-tool-choice \
  --reasoning-parser deepseek_r1 \
  --tool-call-parser internlm

Serving FP8 Model on 4xH800 GPUs (80GB × 4)¶

vllm serve internlm/Intern-S1-FP8 \
  --trust-remote-code \
  --tensor-parallel-size 4 \
  --enable-auto-tool-choice \
  --reasoning-parser deepseek_r1 \
  --tool-call-parser internlm

Serving FP8 Model on 8xMI300x/MI325x¶

export VLLM_ROCM_USE_AITER=1
export VLLM_ROCM_USE_AITER_MOE=0
vllm serve internlm/Intern-S1-FP8 \
  --trust-remote-code \
  --tensor-parallel-size 8 \
  --enable-auto-tool-choice \
  --reasoning-parser deepseek_r1 \
  --tool-call-parser internlm

* You can also reduce the nubmer of GPUs to 4 with --tensor-parallel-size 4 * You can set export VLLM_ROCM_USE_AITER=1 for Better Performance on AMD GPUs. The default is export VLLM_ROCM_USE_AITER=0 * Please turn off AITER MOE on MI300x/MI325x by export VLLM_ROCM_USE_AITER_MOE=0

Serving FP8 Model on 8xMI355x¶

export VLLM_ROCM_USE_AITER=1
vllm serve internlm/Intern-S1-FP8 \
  --trust-remote-code \
  --tensor-parallel-size 8 \
  --enable-auto-tool-choice \
  --reasoning-parser deepseek_r1 \
  --tool-call-parser internlm

* You can also reduce the nubmer of GPUs to 4 with --tensor-parallel-size 4 * You can set export VLLM_ROCM_USE_AITER=1 for Better Performance on AMD GPUs. The default is export VLLM_ROCM_USE_AITER=0

Advanced Usage¶

Switching Between Thinking and Non-Thinking Modes¶

Configure through

extra_body={
    "chat_template_kwargs": {"enable_thinking": False}
}

Sample code

from openai import OpenAI
client = OpenAI(api_key='YOUR_API_KEY', base_url='http://0.0.0.0:8000/v1')
model_name = client.models.list().data[0].id

response = client.chat.completions.create(
    model=model_name,
    messages=[{
        'role':
        'user',
        'content': [{
            'type': 'text',
            'text': '9.11 and 9.8, which is greater?',
        }],
    }],
    temperature=0.8,
    top_p=0.8,
    extra_body={
        "chat_template_kwargs": {"enable_thinking": False}
    }
)
print(response)

Using Tips¶

If you encounter ValueError: No available memory for the cache blocks., try adding the --gpu-memory-utilization 0.95 flag to your vllm serve command.