llama-cpp-kt

The Kotlin wrapper of llama.cpp, powered by JNA.

Setup

First, you need to build your own libllama.so from llama.cpp, using cmake:

mkdir build
cd build
cmake .. -DBUILD_SHARED_LIBS=ON <other flags>
cmake --build . --config Release

You need the -DBUILD_SHARED_LIBS=ON to build shared lib (.so), otherwise it will build the static library (.a), which cannot be loaded by JNA.

Then see jitpack document for how to use the build artifacts in your maven or gradle project.

Usage

First to set the JNA library path:

init {
    System.setProperty("jna.library.path", "./")
    lib = LibLLaMa.LIB
}

This will load lib llama by default, aka the JNA will search for libllama.so or llama.dll. If you have a different file name, you may use Native.load("llama", LibLLaMa::class.java) as LibLLaMa to get your own instance. But do notice that the code requires the default instance to work, since some constant are decided at runtime (for example the LLAMA_MAX_DEVICES is 1 when using CPU but will be 16 when using cuda).

This is a low level binding, which means you get the full control of the C functions, which looks like this:

• lib.llama_model_quantize_default_params()
• lib.llama_load_session_file(ctx, sessionPath, tokens, size, pInt)

There are also some high level helper functions like:

• lib.initLLaMaBackend()
• lib.getContextParams(contextSize=1024, rmsNormEps=1e-5f)

You can check the example subproject to see how to use it. I implemented the original quantize.cpp, simple.cpp and the main.cpp. There are also a simple multi-session chat server which can serve multiple session at once over HTTP (in the example I use single thread computation since I don't have enough ram to do parallel computing).

Roadmap

Currently this repo is still very new and I don't have that many ideas on which path to go. So discussions and contributions are welcome. JVM is a fantastic platform but apparently it is underestimated during the machine learning rush. Despite Python and C++ can create powerful and fast computing deep learning models, I still believe JVM is the best platform to develop complex business logic. Here I choose Kotlin because it's much better than Java yet maintained a good interoperability with Java (however I don't think you can use this lib in Java).

One clear objective is get rid of JNA when Foreign Function & Memory API is in stable release (Maybe JDK 21?).

Another objective is make the grammar working. Currently the grammar feature is missing (Now you can use grammar related call, but the parser is not there. So you have to grow your own tree).