Can I use this for OpenAI-compatible projects?

Absolutely. The skill includes implementation patterns for using the OpenAI Python and JavaScript libraries with Ollama's local endpoints.

Does this skill help with GPU configuration for Ollama?

Yes, it provides specific commands like 'ollama ps' to check GPU loading and offers solutions for VRAM issues and driver compatibility.

Can I manage remote or Dockerized Ollama servers with this?

Yes, it includes configuration guides for OLLAMA_HOST, OLLAMA_ORIGINS, and Docker setup to enable remote access and bypass CORS issues.

Does it support vision and multimodal models?

Yes, it covers implementation details and code samples for vision models like LLaVA, including how to pass base64 image data to the API.

Ollama Local AI Assistant

Name: Ollama Local AI Assistant
Author: rawveg

byrawveg

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Data Science & ML

Simplifies local AI model development by providing comprehensive documentation and code patterns for Ollama APIs, configurations, and model management.

About

The Ollama skill empowers developers to run and interact with large language models locally through the Ollama runtime. It provides domain-specific guidance for building applications with chat completions, embeddings, and vision capabilities while offering detailed troubleshooting for GPU optimization and server configurations. Whether you are implementing OpenAI-compatible libraries or managing complex model deployments in Docker, this skill ensures seamless integration of local AI into your development workflow by providing instant access to best practices and API references.

Key Features

Full API reference for chat, generation, and embedding endpoints
Structured JSON output and tool-calling implementation patterns
GPU utilization monitoring and performance troubleshooting guides
Multi-platform server configuration including Docker and proxy setups
OpenAI-compatible library configuration for Python and JavaScript
8 GitHub stars

Use Cases

Generating vector embeddings locally for RAG (Retrieval-Augmented Generation) systems
Building privacy-focused chatbots and applications using local LLMs
Automating image analysis tasks with local vision models like LLaVA

About

Key Features

Full API reference for chat, generation, and embedding endpoints
Structured JSON output and tool-calling implementation patterns
GPU utilization monitoring and performance troubleshooting guides
Multi-platform server configuration including Docker and proxy setups
OpenAI-compatible library configuration for Python and JavaScript
8 GitHub stars

Use Cases

Generating vector embeddings locally for RAG (Retrieval-Augmented Generation) systems
Building privacy-focused chatbots and applications using local LLMs
Automating image analysis tasks with local vision models like LLaVA