Is Llamafile compatible with the OpenAI Python SDK?

Yes, Llamafile serves an OpenAI-compatible API at the /v1 endpoint, allowing you to use existing SDKs simply by changing the base_url to your local host.

What model formats does Llamafile support?

Llamafile is designed to work with the GGUF format, which is the industry standard for efficient, quantized local LLM inference.

Does Llamafile require an internet connection?

No, once the binary and model are downloaded, Llamafile runs entirely offline, making it ideal for air-gapped environments and privacy-sensitive projects.

Which port does Llamafile use by default?

Llamafile typically defaults to port 8080 for its server mode, though the skill helps you customize this using the --port flag if needed.

Can I use GPU acceleration with this skill?

Yes, the skill provides instructions for configuration flags like --n-gpu-layers to offload inference to CUDA, Metal, or Vulkan compatible GPUs for better performance.

Llamafile Local LLM

Name: Llamafile Local LLM
Author: Jamie-BitFlight

byJamie-BitFlight

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데이터 과학 및 ML

Simplifies the installation, configuration, and management of Mozilla Llamafile for running local, OpenAI-compatible LLMs.

소개

Llamafile is a specialized skill for Claude Code that enables developers to run large language models locally using Mozilla's cross-platform executable format. It provides comprehensive guidance on downloading GGUF models, configuring high-performance server settings with GPU acceleration, and integrating local inference with standard tools like LiteLLM and the OpenAI SDK. This skill is essential for building privacy-focused applications, working in air-gapped environments, or reducing cloud API costs by utilizing local hardware for development and testing tasks.

주요 기능

GPU acceleration configuration for CUDA, Metal, and Vulkan
Seamless integration with LiteLLM and OpenAI Python SDK
Local LLM inference via GGUF models
11 GitHub stars
OpenAI-compatible API server management
Automated installation and performance troubleshooting

사용 사례

Running local LLMs to reduce cloud API costs and latency
Setting up local embedding servers for RAG applications
Building offline or air-gapped AI developer tools

소개

주요 기능

GPU acceleration configuration for CUDA, Metal, and Vulkan
Seamless integration with LiteLLM and OpenAI Python SDK
Local LLM inference via GGUF models
11 GitHub stars
OpenAI-compatible API server management
Automated installation and performance troubleshooting

사용 사례

Running local LLMs to reduce cloud API costs and latency
Setting up local embedding servers for RAG applications
Building offline or air-gapped AI developer tools