Summary LlamaBarn offers a streamlined macOS menu bar application for running local Large Language Models (LLMs), abstracting away technical complexities for both end-users and developers. It provides a curated model catalog, automatic hardware optimization, and dual interfaces—a web UI for direct chat and a REST API for application integration—making local LLM deployment accessible.

How It Works This compact, native macOS application, built in Swift, simplifies LLM management through a curated catalog. Users select a model, and LlamaBarn automatically configures it based on the Mac's hardware for optimal performance and stability. It integrates the llama.cpp server, exposing a familiar REST API for programmatic access and an embedded web UI for interactive chat.

Quick Start & Requirements

• Installation: Download the application from the Releases page.
• Running: Launch the menu bar app, select a model from the catalog to install, then select it again to run. LlamaBarn configures settings and starts a local server at http://localhost:2276.
• Prerequisites: macOS.
• Links: API reference details are available in the llama-server documentation.

Highlighted Details

• A tiny (~12 MB) native macOS application developed in Swift.
• Features a curated model catalog for simplified model discovery and installation.
• Employs automatic hardware configuration to ensure optimal performance and stability across different Mac models.
• Provides both an integrated web UI for direct chat and a REST API compatible with llama.cpp server endpoints for seamless developer integration.

Maintenance & Community Specific details regarding project maintainers, community channels (e.g., Discord, Slack), or a public roadmap are not provided in the README. The project is associated with the ggml-org organization.

Licensing & Compatibility The README does not specify the open-source license for LlamaBarn. Consequently, its compatibility for commercial use or integration within closed-source projects remains undetermined without explicit license information.

Limitations & Caveats The current implementation, as per the roadmap, does not support embedding models, completion models, running multiple models concurrently, or handling parallel requests. Vision capabilities for supported models are also pending.