- name
- model-deploy
- description
- Use this skill when users request to deploy LLMs (Qwen, DeepSeek, etc.) on specified GPU servers and start the model service. This skill can Download models using ModelScope; Start the vLLM inference service.
Model Deploy
Deploy large language models on GPU servers using vLLM. NOTE: only ModelScope plateform and vLLM inference engine is supported currently.
Please ensure that the server where your OpenClaw is located has passwordless login access to the GPU servers. You can achieve this using ssh-copy-id command on your OpenClaw server.
This skill assumes that Miniconda is already installed on your server and is used to manage Python environments. You can use the following command to create the vllm environment with Miniconda:
conda create -n vllm python=3.10 -y
conda activate vllm
pip install vllmQuick Start
On the ModelScope platform, models are uniquely identified by <MODEL_ORG>/<MODEL_NAME>. For example, for Qwen/Qwen3.5-0.8B, MODEL_ORG is Qwen and MODEL_NAME is Qwen3.5-0.8B.
Deploying Qwen Family Models
To deploy Qwen-Family models, use the deployment script scripts/deploy.sh. The usage of the script is as follows:
Usage: [ENV_VARS] deploy.sh <model_name>
Example:
PORT=8001 \
GPU_COUNT=4 \
./deploy.sh Qwen3.5-0.8B
Environment Variables:
ENV_NAME conda environment name (default: vllm)
PORT service port (default: 8000)
GPU_COUNT number of GPUs for tensor parallelism (default: 1)
PROXY proxy address (default: http://{proxyaddress}:{port})
MODEL_BASE_PATH local path to store models (default: /home/work/models)| Variable | Description | Default |
|---|---|---|
| MODEL_ORG | model organization | Qwen |
| MODEL_NAME | model name | Qwen3.5-0.8B |
| ENV_NAME | conda environment | vllm |
| PORT | model service port | 8000 |
| GPU_COUNT | number of GPUs for tensor parallelism | 1 |
| PROXY | proxy address | http://{proxyaddress}:{port} |
| MODEL_BASE_PATH | local storage path for models | /home/work/models |
Deployment Steps
- Extract required information from the user request: model name (MODEL_NAME), model organization (MODEL_ORG), target server address (TARGET_HOST), deployment user (TARGET_USER), and other necessary parameters.
- Copy
./skills/model-deploy/scripts/deploy.shto the specified path on the target server, e.g.,$HOME/wangwei1237. - Grant execute permission to the deployment script on the target server.
- Run the deployment script on the target server using the following format:
ssh ${TARGET_USER}@${TARGET_HOST} "cd $HOME/wangwei1237 && PORT=8001 && ./deploy.sh Qwen3.5-0.8B"- After deployment, test whether the model service has started successfully on the target server by running:
curl -X POST http://127.0.0.1:8001/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"messages": [{"role": "user", "content": "你好"}],
"max_tokens": 512
}'Constraints
- Commands on the target server must be executed in this format:
ssh ${TARGET_USER}@${TARGET_HOST} "${CMD}"
Troubleshooting
- Port occupied: Check with
netstat -tlnp | grep <port> - Version issues: Run
pip install vllm --upgrade - Network issues: Set proxy with
export https_proxy="http://{proxyaddress}:{port}" - Insufficient GPU memory: Check GPU usage with
nvidia-smi, find a suitable GPU indexGPU_FAN, setexport CUDA_VISIBLE_DEVICES=$GPU_FANto specify the GPU, then rerun the deployment script.