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@ -8,10 +8,10 @@ What does it mean? You get llama.cpp with a fancy UI, persistent stories, editin
## Usage ## Usage
- [Download the latest release here](https://github.com/LostRuins/llamacpp-for-kobold/releases/latest) or clone the repo. - [Download the latest release here](https://github.com/LostRuins/llamacpp-for-kobold/releases/latest) or clone the repo.
- Windows binaries are provided in the form of **llamacpp.dll** but if you feel worried go ahead and rebuild it yourself. - Windows binaries are provided in the form of **llamacpp-for-kobold.exe**, which is a pyinstaller wrapper for **llamacpp.dll** and **llama-for-kobold.py**. If you feel concerned, you may prefer to rebuild it yourself with the provided makefiles and scripts.
- 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). - Weights are not included, you can use the `quantize.exe` to generate them from your official weight files (or download them from other places).
- To run, execute the script providing the model as a parameter `llama_for_kobold.py [ggml_quant_model.bin] [port]`, and then connect with Kobold or Kobold Lite. - To run, execute **llamacpp-for-kobold.exe** or drag and drop your quantized ggml model.bin file onto the .exe, 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). - By default, you can connect to http://localhost: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