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A self contained distributable from Concedo that exposes llama.cpp function bindings, allowing it to be used via a simulated Kobold API endpoint. A self contained distributable from Concedo that exposes llama.cpp function bindings, allowing it to be used via a simulated Kobold API endpoint.
What does it mean? You get llama.cpp with a fancy UI, persistent stories, editing tools, save formats, memory, world info, author's note, characters, scenarios and everything Kobold and Kobold Lite have to offer. In a tiny package under 1 MB in size, excluding model weights.
![Preview](preview.png) ![Preview](preview.png)
## Usage
- Windows binaries are provided in the form of **llamacpp.dll** but if you feel worried go ahead and rebuild it yourself.
- Weights are not included, you can use the llama.cpp quantize.exe to generate them from your official weight files (or download them from...places).
- To run, simply clone the repo and run `llama_for_kobold.py [ggml_quant_model.bin] [port]`, and then connect with Kobold or Kobold Lite.
- By default, you can connect to http://localhost:5001 (you can also use https://lite.koboldai.net/?local=1&port=5001).
## Considerations ## Considerations
- Don't want to use pybind11 due to dependencies on MSVCC - Don't want to use pybind11 due to dependencies on MSVCC
- ZERO or MINIMAL changes as possible to main.cpp - do not move their function declarations elsewhere! - ZERO or MINIMAL changes as possible to main.cpp - do not move their function declarations elsewhere!
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- No dynamic memory allocation! Setup structs with FIXED (known) shapes and sizes for ALL output fields. Python will ALWAYS provide the memory, we just write to it. - No dynamic memory allocation! Setup structs with FIXED (known) shapes and sizes for ALL output fields. Python will ALWAYS provide the memory, we just write to it.
- No external libraries or dependencies. That means no Flask, Pybind and whatever. All You Need Is Python. - No external libraries or dependencies. That means no Flask, Pybind and whatever. All You Need Is Python.
## Usage
- Windows binaries are provided in the form of **llamacpp.dll** but if you feel worried go ahead and rebuild it yourself.
- Weights are not included, you can use the llama.cpp quantize.exe to generate them from your official weight files (or download them from...places).
- To run, simply clone the repo and run `llama_for_kobold.py [ggml_quant_model.bin] [port]`, and then connect with Kobold or Kobold Lite.
- By default, you can connect to http://localhost:5001 (you can also use https://lite.koboldai.net/?local=1&port=5001).
## License ## License
- The original GGML library and llama.cpp by ggerganov are licensed under the MIT License - The original GGML library and llama.cpp by ggerganov are licensed under the MIT License
- However, Kobold Lite is licensed under the AGPL v3.0 License - However, Kobold Lite is licensed under the AGPL v3.0 License