llama-cpp

Name: llama-cpp
Availability: InStock
Author: Julius

Run local GGUF inference with llama.cpp models from Hugging Face Hub.

Updated Apr 15, 2026

Search Tags:#quantization #Hugging Face Hub #llama.cpp #model inference #GGUF inference

Ready to run

npx skills add https://github.com/juliuss1907/knowledge-base --skill llama-cpp-juliuss1907

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: llama-cpp
Source: https://github.com/juliuss1907/knowledge-base/tree/main/.hermes/skills/mlops/inference/llama-cpp
Command: npx skills add https://github.com/juliuss1907/knowledge-base --skill llama-cpp-juliuss1907

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires llama-cpp-python>=0.2.0, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables local GGUF inference and discovery of llama.cpp models directly from the Hugging Face Hub, facilitating quick and efficient access to model resources.

Core Features & Use Cases

Local GGUF Inference: Perform inference using llama.cpp models on CPU, Apple Silicon, CUDA, ROCm, or Intel GPUs.
Hugging Face Hub Integration: Directly search and utilize llama.cpp models from the Hugging Face Hub.
Model Discovery Workflow: Streamlined process to search for, view, and use llama.cpp models, including building server and CLI commands.
Quantization and Model Selection: Facilitates selecting the right quantization for your hardware profile and model size requirements.

Quick Start

To run the llama.cpp model with a specific quantization level, use the following command:

llama-server -hf <repo>:<QUANT>

Frequently Asked Questions about llama-cpp

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I run local GGUF inference using a model from the Hugging Face Hub?▼

To run local GGUF inference, you can deploy a llama.cpp model directly from the Hugging Face Hub by executing `llama-server -hf <repo>:<QUANT>`, replacing the repo and quantization level with your specific requirements.

What is the best way to select a GGUF quantization format for my hardware?▼

Selecting a GGUF quantization format requires matching the model size and quantization level to your specific hardware profile, ensuring optimal inference performance across CPU, Apple Silicon, CUDA, ROCm, or Intel GPUs.

Can I use llama.cpp models for local inference on Apple Silicon or CUDA GPUs?▼

Yes, llama.cpp models support local GGUF inference across multiple hardware platforms, allowing you to execute model inference natively on CPU, Apple Silicon, CUDA, ROCm, and Intel GPU environments.

Does the Hugging Face Hub integration support searching for specific llama.cpp models?▼

Yes, Hugging Face Hub integration provides a streamlined model discovery workflow, allowing you to directly search for, view, and utilize specific llama.cpp models for your deployment needs.

Do I need Python to build server and CLI commands for llama.cpp models?▼

You need the `llama-cpp-python` dependency installed to facilitate the model discovery workflow and build the necessary server and CLI commands for deploying llama.cpp models locally.

Why should I use GGUF formats for local model inference instead of other formats?▼

GGUF formats enable efficient local model inference by supporting quantization, which reduces the computational resources required to run large language models directly on consumer hardware platforms.