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Shimmy: Lightweight Local AI Model Serving Solution for Zero-Configuration Deployment

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What is Shimmy?

Shimmy is an ultra-lightweight tool weighing only 5.1MB that provides fully OpenAI-compatible AI model services on your local computer. This means you can use existing AI tools and applications by simply pointing their API endpoints to Shimmy, enabling you to run large language models locally and privately without any code changes.

Unlike other solutions that require substantial resources and complex configurations, Shimmy features a minimalist design with startup times under 100 milliseconds and memory usage of approximately 50MB. It automatically discovers GGUF model files in your system and provides complete OpenAI-compatible endpoints, allowing various AI tools to work seamlessly.

Why Choose Shimmy?

As artificial intelligence applications become increasingly prevalent, many developers and organizations want to run models locally rather than relying on cloud API services. This approach offers multiple benefits:

  • Data Privacy: Your code and data never leave your local machine
  • Cost Control: No need to pay per-token API fees
  • Response Speed: Local inference provides sub-second response times
  • Reliability: No rate limits or service downtime

However, traditional local model deployment solutions often require massive software packages (like Ollama’s 680MB binary) and complex configuration processes. Shimmy addresses these issues by providing an extremely lightweight, zero-configuration solution.

Key Features

Complete OpenAI API Compatibility

Shimmy provides 100% OpenAI-compatible interfaces, including:

  • GET /v1/models – List available models
  • POST /v1/chat/completions – Chat completion functionality
  • POST /v1/completions – Text completion functionality
  • GET /health – Health check endpoint

This means any tool supporting OpenAI API can work directly with Shimmy without modifications.

Automatic Model Discovery

Shimmy automatically discovers model files from multiple locations:

  • Hugging Face cache directory: ~/.cache/huggingface/hub/
  • Ollama model directory: ~/.ollama/models/
  • Local directory: ./models/
  • Environment variable specified path: SHIMMY_BASE_GGUF=path/to/model.gguf

Zero-Configuration Design

  • Automatic port allocation to avoid conflicts
  • Automatic LoRA adapter detection for specialized model support
  • No configuration files or setup wizards required

Cross-Platform Support

Shimmy supports Windows, macOS, and Linux systems. On macOS, it also supports Metal GPU acceleration for both Intel and Apple Silicon chips.

Installation and Usage

Quick Installation

Windows Systems:

Precompiled binaries are recommended to avoid build dependency issues:

curl -L https://github.com/Michael-A-Kuykendall/shimmy/releases/latest/download/shimmy.exe -o shimmy.exe

Alternatively, install from source (requires LLVM/Clang first):

winget install LLVM.LLVM
cargo install shimmy --features huggingface

macOS and Linux Systems:

cargo install shimmy --features huggingface

Obtaining Models

Shimmy uses GGUF format model files. You can download models using huggingface-cli:

huggingface-cli download microsoft/Phi-3-mini-4k-instruct-gguf --local-dir ./models/
huggingface-cli download bartowski/Llama-3.2-1B-Instruct-GGUF --local-dir ./models/

Starting the Service

Starting the Shimmy server is straightforward:

# Automatic port allocation
shimmy serve

# Or manually specify port
shimmy serve --bind 127.0.0.1:11435

Once started, simply point your AI tools to the displayed port to begin using them.

Docker Deployment

For users who prefer containerized solutions, Shimmy also offers Docker support.

Quick Start

  1. Create a models directory:
mkdir models
  1. Download model files:
curl -L "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf/resolve/main/Phi-3-mini-4k-instruct-q4.gguf" -o models/phi-3-mini.gguf
  1. Start Shimmy:
docker-compose up -d
  1. Test the API:
curl http://localhost:11434/v1/models

Environment Configuration

Docker deployment supports the following environment variables:

Variable Default Value Description
SHIMMY_PORT 11434 Server port
SHIMMY_HOST 0.0.0.0 Listening address
SHIMMY_BASE_GGUF /app/models Models directory

GPU Support

For NVIDIA GPU users, Docker deployment supports automatic GPU configuration. Ensure:

Integration Examples

VSCode Copilot

Add to VSCode settings:

{
  "github.copilot.advanced": {
    "serverUrl": "http://localhost:11435"
  }
}

Continue.dev

Add to Continue.dev configuration:

{
  "models": [{
    "title": "Local Shimmy",
    "provider": "openai", 
    "model": "your-model-name",
    "apiBase": "http://localhost:11435/v1"
  }]
}

Cursor IDE

Works out of the box – simply point to http://localhost:11435/v1

Performance Comparison

Here’s how Shimmy compares to other popular tools:

Tool Binary Size Startup Time Memory Usage OpenAI API Compatibility
Shimmy 5.1MB <100ms 50MB 100%
Ollama 680MB 5-10 seconds 200MB+ Partial
llama.cpp 89MB 1-2 seconds 100MB None

Technical Architecture

Shimmy is built on the following technologies:

  • Rust + Tokio: Provides memory safety and asynchronous high performance
  • llama.cpp backend: Industry-standard GGUF inference engine
  • OpenAI API compatibility: Complete replacement solution
  • Dynamic port management: Zero conflicts, automatic allocation
  • Zero-configuration auto-discovery: Works out of the box

Developer Certificate of Origin (DCO)

Shimmy uses the Developer Certificate of Origin (DCO) to ensure all contributions are properly licensed and that contributors have the right to submit their code.

What is DCO?

DCO is a lightweight way for contributors to certify that they wrote or otherwise have the right to submit their contribution. It’s an industry-standard alternative to Contributor License Agreements (CLAs) used by projects like Linux kernel, Docker, and GitLab.

How to Sign Your Commits

Option 1: Automatic Sign-off (Recommended)

Configure git to automatically sign off your commits:

git config user.name "Your Name"
git config user.email "your.email@example.com"
git config format.signoff true

Then commit normally:

git commit -m "Add new feature"

Option 2: Manual Sign-off

Use the -s flag for git commit:

git commit -s -m "Add new feature"

This adds a sign-off line to your commit message:

Add new feature

Signed-off-by: Your Name <your.email@example.com>

Option 3: Amend Existing Commits

If you forgot to sign off a commit:

# For the last commit
git commit --amend --signoff

# For multiple commits
git rebase --signoff HEAD~3  # Last 3 commits

Frequently Asked Questions

Is Shimmy free?

Yes, Shimmy will always be free. It uses the MIT license and promises never to become a paid product.

Can I use it if I work for a company?

Yes. Use your work email and ensure your employer allows you to contribute to open source projects.

What if I already contributed code without signing?

No problem! Simply amend your commits to add sign-off and force push the branch.

Is this legally binding?

Yes, the DCO is a legal certification that you have the right to contribute your code.

What about code I copied from elsewhere?

Only contribute code you wrote or have proper licensing rights to. When in doubt, ask the maintainer.

Can I sign someone else’s commit?

No, each contributor must sign off their own commits with their own identity.

What model formats are supported?

Shimmy supports GGUF format model files, which is currently one of the most popular quantized model formats.

Is GPU acceleration supported?

Yes, Shimmy supports GPU acceleration. On macOS, it supports Metal GPU acceleration, and on Linux and Windows, it supports CUDA.

How do I update Shimmy?

If installed via cargo, use cargo install shimmy --features huggingface --force to update. If using precompiled binaries, simply download the latest version and replace.

Technical Support and Community

  • Issue reporting: Submit via GitHub Issues
  • Discussions: Participate in GitHub Discussions
  • Documentation: Check the docs/ directory for detailed documentation
  • Sponsorship: Support project development through GitHub Sponsors

Quality and Reliability

Shimmy maintains high code quality through comprehensive testing:

  • Comprehensive test suite with property-based testing
  • Automated CI/CD pipeline with quality gates
  • Runtime invariant checking for critical operations
  • Cross-platform compatibility testing

Conclusion

Shimmy is a revolutionary lightweight tool that makes local AI model deployment simple and efficient. Whether you’re a developer, researcher, or enterprise user, you can benefit from its clean design, powerful features, and zero-configuration experience.

Due to its minimal resource footprint and fully compatible API design, Shimmy is an ideal alternative to bulky and complex model service solutions. Most importantly, its commitment to being forever free and open-source provides a solid foundation for widespread adoption.

If you’re looking for a simple, efficient, and reliable local AI model service solution, Shimmy is worth trying.

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