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How to Use an AI Model in C++

696 words 4 minutes C++ AI Memoria Forge

How to Use an AI Model in C++

In this article, we'll see how to integrate a local AI model into a C++ project using MemoriaForge, a lightweight library that simplifies the use of GGUF models without requiring direct interaction with the llama.cpp API.

Why Use Local AI?

Running a language model locally offers several advantages:

  • It does not depend on external services.
  • It works without an Internet connection.
  • Data remains on the user's machine.
  • There are no costs per query or subscription fees.
  • It allows distributing fully autonomous intelligent applications.

This is especially useful for personal assistants, productivity tools, video games, automation, and business software.

The Problem with Integrating llama.cpp

llama.cpp is one of the most popular projects for running language models on conventional hardware. However, its API is designed to provide flexibility and low-level access.

For many applications, this means implementing repetitive tasks such as:

  • Loading GGUF models.
  • Managing conversations.
  • Maintaining context between messages.
  • Configuring generation parameters.
  • Saving and restoring states.

When the goal is simply to add AI to an application, much of this work can be unnecessary.

MemoriaForge

MemoriaForge is a lightweight wrapper for llama.cpp designed to provide a simple API that is easy to integrate into C++ projects.

Its features include:

  • C++17-compatible API.
  • Support for GGUF models.
  • Conversation persistence.
  • Custom context injection.
  • Configurable generation parameters.
  • Precompiled binaries for 32-bit and 64-bit Windows.
  • llama.cpp statically integrated into the library.

Thanks to this, there is no need to distribute additional llama.cpp libraries alongside the application.

First Example

Suppose we want to create a program capable of answering questions using a local model.

The required code is surprisingly small:

#include <iostream>
#include "MemoriaForge.h"

int main() {

    MemoriaForge::LLMSession ai(
        "Qwen3-0.6B-Q8_0.gguf"
    );

    std::cout << ai.chat("Hello!") << std::endl;

    return 0;
}

When the program runs, the model is loaded and responds to our message.

From the developer's perspective, working with AI is as simple as calling a method.

Compilation

If we use MinGW, we can compile the project with:

g++ main.cpp -Iinclude -Llib -lMemoriaForge -o app.exe

For execution, simply place MemoriaForge.dll next to the executable or in a location accessible through the system PATH variable.

Persistent Conversations

One of the most interesting aspects of working with language models is the ability to maintain conversations.

Instead of treating each query as an independent interaction, MemoriaForge allows message history to be preserved so that the model remembers the conversation context.

This makes it easier to create:

  • Virtual assistants.
  • Support systems.
  • Intelligent NPCs for video games.
  • AI-powered productivity tools.

Context Injection

Another useful feature is the ability to provide additional information to the model before generating a response.

For example:

  • Technical documentation.
  • Text files.
  • Knowledge base data.
  • User-specific information.

This allows the model to respond using information relevant to the application without requiring retraining.

Generation Configuration

MemoriaForge also allows adjusting generation parameters such as:

temperature = 0.7
min_p       = 0.05
seed        = 0

These values control aspects such as creativity, diversity, and reproducibility of responses.

The default parameters are optimized for Qwen-family models and provide a good balance between coherence and variability.

Use Cases

Once the library is integrated, it is possible to build applications such as:

  • Local chatbots.
  • Personal assistants.
  • Programming assistance tools.
  • Document query systems.
  • Conversational interfaces for existing software.
  • Video games with AI-driven characters.

All of this without relying on external services or sending information to third parties.

Download

You can download the MemoriaForge library from these links:

You can also download the source code from the official GitHub repository:

Conclusion

Integrating language models into C++ applications no longer requires dealing with complex APIs or building the entire infrastructure from scratch.

Thanks to libraries such as MemoriaForge, it is possible to load a GGUF model, start a conversation, and obtain responses with just a few lines of code.

If you are developing software in C++ and want to experiment with local AI, tools like this allow you to focus on the application rather than the internal details of the inference engine.

The barrier to entry for incorporating artificial intelligence into native projects has never been lower.