docker-model-runner

Agent Building

Skills for using Docker Model Runner to run local LLM inference

QUICK START

How to use this skill

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Copy the prompt below and paste it into your agent.
Review the proposed files and risks before you approve installation.

Prompt to paste

I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/docker/model-runner/blob/HEAD/cmd/cli/commands/skills/docker-model-runner/SKILL.md

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/docker-model-runner/. Do not write files or run scripts until I approve.

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Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

Docker Model Runner

Docker Model Runner (DMR) makes it easy to run AI models locally using Docker. This skill helps you effectively use Docker Model Runner for local LLM inference in your development workflow.

Workflow

When helping users with local LLM inference using Docker Model Runner:

Check if Docker Model Runner is available by running docker model version
List available models with docker model list to see what's already pulled
Search for models on Docker Hub or HuggingFace:
- docker model search <query> to find models
- Popular models include: ai/gemma3, ai/llama3.2, ai/smollm2, ai/qwen3
Pull models before running: docker model pull <model>
Run models for inference:
- One-time prompt: docker model run ai/smollm2 "Your prompt here"
- Interactive chat: docker model run ai/smollm2
- Pre-load model: docker model run --detach ai/smollm2
Use the OpenAI-compatible API for programmatic access:
- Endpoint: http://localhost:12434/engines/llama.cpp/v1/chat/completions
- This is compatible with OpenAI client libraries

API Usage

Docker Model Runner exposes an OpenAI-compatible REST API:

# Chat completions
curl http://localhost:12434/engines/llama.cpp/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "ai/smollm2",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Hello!"}
    ]
  }'

For Python with the OpenAI library:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:12434/engines/llama.cpp/v1",
    api_key="not-needed"  # API key not required for local inference
)

response = client.chat.completions.create(
    model="ai/smollm2",
    messages=[{"role": "user", "content": "Hello!"}]
)

Key Commands

Command	Description
`docker model run <model> [prompt]`	Run a model with optional prompt
`docker model pull <model>`	Pull a model from registry
`docker model list`	List downloaded models
`docker model search <query>`	Search for models
`docker model ps`	Show running models
`docker model rm <model>`	Remove a model
`docker model inspect <model>`	Show model details

Best Practices

Use smaller models (like ai/smollm2) for faster responses during development
Pre-load models with --detach for better performance in scripts
Models stay loaded until another model is requested or timeout (5 min)
Use the OpenAI-compatible API for integration with existing tools

References

See references/docker-model-guide.md for detailed documentation.