Llamafile is a single-file executable format developed by Mozilla that allows you to run Large Language Models locally on various operating systems including macOS, Windows, and Linux.

Can I use llamafile for offline AI development?

Absolutely. Llamafile is ideal for air-gapped or offline environments as it runs entirely on your local hardware once the binary and model are downloaded.

Which model format does llamafile use?

Llamafile uses the GGUF model format, and this skill provides recommendations for several optimized models like Gemma, Qwen, and Mistral.

Is the llamafile API compatible with OpenAI tools?

Yes, llamafile serves an OpenAI-compatible HTTP API, allowing you to use it as a drop-in replacement for any tool that supports the OpenAI SDK or LiteLLM.

Does this skill support GPU acceleration?

Yes, the skill provides specific configuration patterns to enable GPU offloading for NVIDIA (CUDA), Apple (Metal), and AMD (Vulkan) to significantly speed up inference.

Llamafile Local LLM

Name: Llamafile Local LLM
Author: Jamie-BitFlight

byJamie-BitFlight

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Data Science & ML

Configures and manages local LLM inference using Mozilla's llamafile to provide offline, OpenAI-compatible AI capabilities.

About

This skill enables Claude to set up and manage Mozilla llamafile, a cross-platform distribution format for running large language models locally without cloud dependencies. It facilitates the installation of llamafile binaries, downloading various GGUF models, and configuring high-performance local servers with GPU acceleration. It is particularly useful for developers building air-gapped tools, troubleshooting local inference connections, or integrating local models with existing OpenAI-compatible SDKs and LiteLLM workflows for cost-effective or private AI development.

Key Features

Optimized server configuration for CPU and GPU acceleration (CUDA, Metal, Vulkan)
Background process management and health monitoring utilities
10 GitHub stars
Integration guidance for LiteLLM and OpenAI Python SDKs
Comprehensive troubleshooting for API connectivity and performance issues
Automated setup for llamafile binaries and GGUF model downloads

Use Cases

Building air-gapped or offline-first AI developer tools
Setting up private local models for sensitive code review and data processing
Reducing API costs by offloading specific tasks to local inference servers

About

Key Features

Optimized server configuration for CPU and GPU acceleration (CUDA, Metal, Vulkan)
Background process management and health monitoring utilities
10 GitHub stars
Integration guidance for LiteLLM and OpenAI Python SDKs
Comprehensive troubleshooting for API connectivity and performance issues
Automated setup for llamafile binaries and GGUF model downloads

Use Cases

Building air-gapped or offline-first AI developer tools
Setting up private local models for sensitive code review and data processing
Reducing API costs by offloading specific tasks to local inference servers