Is it useful for advanced features like LoRA?

Yes, the skill includes API references and workflows for managing LoRA adapters and custom sampling strategies like DRY and XTC.

Does it support the latest GGUF model features?

Yes, it covers GGUF model loading, metadata detection, and advanced architecture support including encoder-decoder and recurrent models.

How does it handle deprecated llama.cpp functions?

The skill focuses exclusively on the modern API and provides clear mapping instructions for developers looking to update old code to the current non-deprecated standards.

What specifically does the llamacpp skill provide?

It provides a structured guide to the llama.cpp C API, including initialization, model loading, tokenization, and inference workflows with code examples.

Can this skill help with performance optimization?

Absolutely. It includes best practices for thread management, GPU offloading (n_gpu_layers), and efficient batch processing to maximize inference speed.

Llama.cpp C/C++ Integration

Name: Llama.cpp C/C++ Integration
Author: datathings

bydatathings

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Data Science & ML

Provides a comprehensive C/C++ API reference and implementation patterns for high-performance local LLM inference using llama.cpp.

About

This skill equips developers with an exhaustive reference for the llama.cpp C API, facilitating the integration of state-of-the-art local AI inference into C and C++ applications. It covers the entire lifecycle of LLM interaction, from backend initialization and GGUF model loading to advanced batching, KV cache management, and complex sampling strategies. By providing curated workflows, non-deprecated function lookups, and troubleshooting guidance, it simplifies the process of building efficient, low-dependency AI tools on diverse hardware.

Key Features

Complete reference for 170+ non-deprecated llama.cpp API functions
4 GitHub stars
Detailed implementation patterns for text generation, embeddings, and chat
Optimized workflows for GGUF model loading and GPU/CPU performance tuning
Guidance for KV cache management, state saving/loading, and LoRA adapters
Documentation for over 25 sampling strategies including adaptive-p and XTC

Use Cases

Integrating local LLM inference into native C/C++ desktop or embedded applications
Building custom AI inference engines with specialized sampling or batching requirements
Migrating legacy llama.cpp implementations to the latest standardized API

About

Key Features

Complete reference for 170+ non-deprecated llama.cpp API functions
4 GitHub stars
Detailed implementation patterns for text generation, embeddings, and chat
Optimized workflows for GGUF model loading and GPU/CPU performance tuning
Guidance for KV cache management, state saving/loading, and LoRA adapters
Documentation for over 25 sampling strategies including adaptive-p and XTC

Use Cases

Integrating local LLM inference into native C/C++ desktop or embedded applications
Building custom AI inference engines with specialized sampling or batching requirements
Migrating legacy llama.cpp implementations to the latest standardized API