KoboldCpp is an open-source project designed to provide an easy-to-use interface for running AI text-generation models.

  • Here are the key features and functionalities of KoboldCpp:
    • Simple Setup: Offers a single, self-contained package that simplifies the deployment of complex AI models, minimizing the need for extensive configuration.
    • Support for GGML and GGUF: Compatible with a range of models based on Google’s Generic Generative Modeling Library (GGML) and Generative Unidirectional Transformer (GGUF), allowing flexibility in model selection for text generation.
    • Integration with KoboldAI UI: Enhances user experience by integrating with the KoboldAI user interface, which includes features like persistent stories, editing tools, and world info to aid in crafting interactive narratives.
    • Additional Features: Extends functionality beyond text generation to include support for Stable Diffusion image generation and multiple streaming options.

These capabilities make KoboldCpp a versatile tool for developers and creators looking to leverage advanced AI models for text and image generation projects.

The KoboldCPP Project

KoboldCpp, an easy-to-use AI text-generation software for GGML and GGUF models.

Installing koboldcpp

Check latest releases of KoboldCpp here.

For example, the KoboldCpp v1.58:

wget https://github.com/LostRuins/koboldcpp/releases/download/v1.58/koboldcpp-linux-x64
#curl -fLo koboldcpp https://github.com/LostRuins/koboldcpp/releases/latest/download/koboldcpp-linux-x64 && chmod +x koboldcpp-linux-x64


Select the model, for example you can download: https://huggingface.co/eachadea/ggml-vicuna-7b-1.1/tree/main or https://huggingface.co/TheBloke/dolphin-2.5-mixtral-8x7b-GGUF/tree/main

KoboldCpp will interact via web browser at: http://localhost:5001

Trying a MoE LLM

mixtral-8x7b - The original one is dolphin.

you can read more at: https://mistral.ai/news/mixtral-of-experts/



Why Cpp and not Python?

  • Performance: C++ typically offers better performance than Python due to its lower-level nature and more direct control over hardware resources. For computationally intensive AI tasks, especially those involving large datasets or complex algorithms, C++ can provide significant speed advantages.

  • Development Time: Python is often favored for its simplicity and ease of development. It offers concise syntax, dynamic typing, and extensive libraries (such as TensorFlow, PyTorch, and scikit-learn) that make it convenient for prototyping and experimenting with AI models. In contrast, C++ development may require more time and effort due to its stricter syntax and manual memory management.

  • Portability: Python’s high-level nature and platform independence make it more portable than C++, which is typically compiled to machine code specific to the target platform. Python code can run on various platforms without modification, whereas C++ code may need to be recompiled for different platforms.