Model Deploy Skill

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:

bash

conda create -n vllm python=3.10 -y
conda activate vllm
pip install vllm

Quick 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:

bash

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.sh to 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:

bash

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:

bash

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 index GPU_FAN, set export CUDA_VISIBLE_DEVICES=$GPU_FAN to specify the GPU, then rerun the deployment script.