gguf connector core built on llama.cpp
Project description
llama-core
This is also a solo llama connector; being able to work independently.
install via (pip/pip3):
pip install llama-core
run it by (python/python3):
python -m llama_core
Prompt to user interface selection menu above; while chosen, GGUF file(s) in the current directory will be searched and detected (if any) as below.
remark(s)
Other functions are same as llama-cpp-python; for CUDA(GPU, Nvida) and Metal(M1/M2, Apple) supported settings, please specify CMAKE_ARGS
following Abetlen's repo below; if you want to install it by source file (under releases), you should opt to do it by .tar.gz file (then build your machine-customized installable package) rather than .whl (wheel; a pre-built binary package) with an appropriate cmake tag(s).
references
build from llama-core-(version).tar.gz (examples below are for CPU)
According to the latest note inside vs code, msys64 is recommended by Microsoft; or you can opt w64devkit or etc. as source of your gcc and g++ compilers.
for windows user(s):
$env:CMAKE_GENERATOR = "MinGW Makefiles"
$env:CMAKE_ARGS = "-DCMAKE_C_COMPILER=C:/msys64/mingw64/bin/gcc.exe -DCMAKE_CXX_COMPILER=C:/msys64/mingw64/bin/g++.exe"
pip install llama-core-(version).tar.gz
In mac, xcode command line tools are recommended by Apple for dealing all coding related issue(s); or you can bypass it for your own good/preference.
for mac user(s):
pip3 install llama-core-(version).tar.gz
Make sure your gcc and g++ are >=11; you can check it by: gcc --version and g++ --version; other settings include: typing-extensions>=4.5.0, numpy>=1.20.0, diskcache>=5.6.1, jinja2>=2.11.3, cmake>=3.21, etc.; however, if you opt to install it by the pre-built wheel (.whl) file then you don't need to worry about that.
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