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Koboldcpp AI Tool Features, Use Cases & Alternatives

Koboldcpp

KoboldCpp: Streamline your AI model runs with a one-file solution for GGML and GGUF models. Easy, efficient, and open-source.

Koboldcpp Details

Koboldcpp Info

Organization:
Koboldcpp
Type:
  1. AI Coding
Marked As:
SFW (Safe for Work)
Platform:
  1. Website
Pricing:
  1. Freemium
From $4
Rating:
4.5
(22 Likes)

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KoboldCpp is a comprehensive AI text-generation software designed to enhance the capabilities of GGML and GGUF models. Developed by Concedo, it is an evolution of llama.cpp, offering a robust Kobold API endpoint, support for additional formats, and the integration of Stable Diffusion for image generation. Its user-friendly interface boasts persistent stories, editing tools, and a variety of save formats, alongside features like memory, world info, author’s note, characters, and scenarios, all derived from the original KoboldAI and KoboldAI Lite.

The software is distributed as a single, self-contained package, ensuring ease of use and backward compatibility. For detailed guidance, users are encouraged to utilize the –help flag or consult the wiki. Linux users benefit from a prebuilt koboldcpp-linux-x64-cuda1150 PyInstaller binary available on the releases page, catering to modern systems with a straightforward download and run approach.

For those preferring a manual installation, KoboldCpp can be installed in the current directory via a terminal command, followed by launching the software using ./koboldcpp. The build script option offers an automated solution for creating a pyinstaller binary, leveraging conda to manage dependencies. This method is particularly useful when precompiled binaries are not compatible, although it is advised against on systems with existing conda installations.

The GUI version, launched with ./koboldcpp.sh, simplifies configuration and usage, requiring X11. Terminal commands are accessible through the same script, providing a versatile experience akin to the python script and binaries. The rebuild command is essential for updating KoboldCpp, ensuring all components function correctly.

Lastly, community contributions have led to the creation of AUR packages like CUBLAS and HIPBLAS, targeting NVIDIA and AMD GPU users, respectively. However, users are recommended to opt for official KoboldCpp binaries for the most reliable performance.

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