Can I use local models in my CI/CD pipeline?

Yes, this skill includes specific patterns for integrating Ollama into GitHub Actions and self-hosted runners, allowing you to run automated tests with local inference.

Which models are recommended for local coding tasks?

For coding-specific tasks, the Qwen2.5-coder (32b) is highly recommended as it performs exceptionally well on coding benchmarks and runs efficiently on modern local hardware.

Is performance tuning available for Mac users?

Yes, the skill includes specific optimizations for Apple Silicon, such as setting optimal context windows (num_ctx=32768) and managing memory usage for loaded models.

What are the benefits of using Ollama with Claude Code?

Using Ollama allows for significant cost reduction (up to 93% savings), improved data privacy, and the ability to develop AI-powered features offline using high-performance local models like DeepSeek-R1.

How do I switch between local and cloud LLMs automatically?

This skill provides a Provider Factory pattern in Python that uses environment variables to automatically toggle between Ollama (local) and cloud providers like OpenAI or Anthropic.

Ollama Local Inference

Name: Ollama Local Inference
Author: yonatangross

byyonatangross

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데이터 과학 및 ML

Enables high-performance local LLM execution for cost-effective, private, and offline AI-powered development.

소개

This skill provides a comprehensive framework for integrating local Large Language Models (LLMs) via Ollama into your development environment, offering up to 93% cost savings and enhanced privacy compared to cloud APIs. It guides developers through expert model selection (such as DeepSeek-R1 and Qwen2.5-Coder), performance tuning for Apple Silicon, and seamless LangChain integration. Whether you are setting up CI/CD pipelines with local inference or building robust provider factories that intelligently switch between local and cloud models, this skill ensures production-ready patterns for efficient, offline-capable AI development.

주요 기능

Optimized model selection for Reasoning, Coding, and Embeddings tasks
CI/CD integration patterns for self-hosted local inference runners
Performance-tuned configurations specifically for Apple Silicon (M4 Max) hardware
Seamless LangChain integration with support for tool calling and structured output
29 GitHub stars
Automated provider factory patterns for cloud/local LLM switching

사용 사례

Replacing expensive cloud API calls with local models to reduce development overhead costs
Enabling fully offline AI-assisted development and automated testing pipelines
Building privacy-first applications where sensitive source code must remain on-premises

소개

주요 기능

Optimized model selection for Reasoning, Coding, and Embeddings tasks
CI/CD integration patterns for self-hosted local inference runners
Performance-tuned configurations specifically for Apple Silicon (M4 Max) hardware
Seamless LangChain integration with support for tool calling and structured output
29 GitHub stars
Automated provider factory patterns for cloud/local LLM switching

사용 사례

Replacing expensive cloud API calls with local models to reduce development overhead costs
Enabling fully offline AI-assisted development and automated testing pipelines
Building privacy-first applications where sensitive source code must remain on-premises