Llama Cpp
LlamaCpp enables running Meta’s LLaMA language model locally using C++, offering efficient inference and privacy without internet dependency.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add orchestra-research-llama-cpp npx -- -y @trustedskills/orchestra-research-llama-cpp
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"orchestra-research-llama-cpp": {
"command": "npx",
"args": [
"-y",
"@trustedskills/orchestra-research-llama-cpp"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
This skill allows AI agents to leverage the llama.cpp library for running large language models locally. It provides capabilities for efficient inference, quantization support, and compatibility with various hardware configurations. The agent can utilize this skill to process text data and generate responses without relying on external APIs or cloud services.
When to use it
- Offline Processing: When internet connectivity is unavailable, the agent needs to analyze documents or respond to prompts.
- Privacy-Sensitive Tasks: For scenarios requiring local data processing and preventing information from leaving a secure environment.
- Resource-Constrained Environments: To deploy language models on devices with limited computational resources.
- Custom Model Integration: When you need to use specific, locally stored LLM models not accessible through standard APIs.
Key capabilities
- llama.cpp library integration
- Local Large Language Model (LLM) inference
- Quantization support for efficient resource usage
- Hardware compatibility across various configurations
Example prompts
- "Analyze this document and summarize the key findings." (followed by providing a text file)
- "Generate a creative story based on these keywords: [keyword1], [keyword2], [keyword3]."
- "Translate this paragraph into French:" (followed by providing a paragraph of text).
Tips & gotchas
- Ensure the necessary llama.cpp dependencies are installed and configured correctly before using this skill.
- Performance will depend on available hardware resources; quantization can help optimize for lower-powered devices.
Tags
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Security Audits
| Gen Agent Trust Hub | Pass |
| Socket | Pass |
| Snyk | Pass |
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