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

./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

With KoboldCpp you can run the latest Mix of Experts model, like: Mixtral-8x7b - The original one is dolphin.

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

FAQ

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.

GGML vs. GGUF:

This content explains the evolution of file formats used for large language models, specifically focusing on GGML (Generic Generative Modeling Library) and its successor, GGUF (Generic Generative Unified Format).

GGML was the initial format for storing these models, as seen in projects like KoboldCpp. However, it had limitations.

GGUF is the improved version that addresses these shortcomings.

It offers several key advantages:

  • More Information: GGUF stores additional metadata about the model, making it more robust and adaptable for future use.
  • Wider Support: It can accommodate a broader range of model architectures.
  • Improved User Experience: Features like prompt templates are included, simplifying model usage.
  • Extensibility: GGUF is designed to be easily expanded to support new functionalities.

In essence, GGUF is a superior and more flexible format, and as a result, GGML is no longer actively supported.