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@ -64,23 +64,7 @@ when you can't use the precompiled binary directly, we provide an automated buil
- Note: Many OSX users have found that the using Accelerate is actually faster than OpenBLAS. To try, you may wish to run with `--noblas` and compare speeds.
### Arch Linux Packages
There are 4 community made AUR packages (Maintained by @AlpinDale) available: [CPU-only](https://aur.archlinux.org/packages/koboldcpp-cpu), [CLBlast](https://aur.archlinux.org/packages/koboldcpp-clblast), [CUBLAS](https://aur.archlinux.org/packages/koboldcpp-cuda), and [HIPBLAS](https://aur.archlinux.org/packages/koboldcpp-hipblas). They are, respectively, for users with no GPU, users with a GPU (vendor-agnostic), users with NVIDIA GPUs, and users with a supported AMD GPU.
The recommended installation method is through an AUR helper such as [paru](https://aur.archlinux.org/packages/paru) or [yay](https://aur.archlinux.org/packages/yay):
```sh
paru -S koboldcpp-cpu
```
Alternatively, you can manually install, though it's not recommended (since the build depends on [customtkinter](https://aur.archlinux.org/packages/customtkinter)):
```sh
git clone https://aur.archlinux.org/koboldcpp-cpu.git && cd koboldcpp-cpu
makepkg -si
```
You can then run koboldcpp anywhere from the terminal by running `koboldcpp` to spawn the GUI, or `koboldcpp --help` to view the list of commands for commandline execution (in case the GUI does not work).
There are some community made AUR packages (Maintained by @AlpinDale) available: [CUBLAS](https://aur.archlinux.org/packages/koboldcpp-cuda), and [HIPBLAS](https://aur.archlinux.org/packages/koboldcpp-hipblas). They are intended for users with NVIDIA GPUs, and users with a supported AMD GPU. Note that these packages may be outdated, and it's probably better to use official KoboldCpp binaries.
## Compiling on Windows
- You're encouraged to use the .exe released, but if you want to compile your binaries from source at Windows, the easiest way is: