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How run LLM on a low cost Intel Arc A770 graphic card ?
First, you need to install OneAPI.
Find the two following directories and keep note of them:
- \Intel\oneAPI\compiler\2023.2.0\windows\include\sycl
- \Intel\oneAPI\intelpython\python3.9\envs\2023.2.0\Library\lib\OpenCL.lib
How to Install Install CLBlast accelerated with Intel arc A770 ?
Clone locally CLBlast
Create a build directory
cmake .. -DOPENCL_INCLUDE_DIRS=”C:\Program Files (x86)\Intel\oneAPI\compiler\2023.2.0\windows\include\sycl” -DOPENCL_LIBRARIES=”C:\Program Files (x86)\Intel\oneAPI\intelpython\python3.9\envs\2023.2.0\Library\lib\OpenCL.lib”
Dans CLBlastConfig.cmake on ajoute les deux lignes suivantes:
set(CLBlast_INCLUDE_DIRS= “${CMAKE_CURRENT_LIST_DIR}/../include”)
set(CLBlast_LIBRARIES= “${CMAKE_CURRENT_LIST_DIR}/../lib/clblast.lib”)
cmake –build . –config Release
cmake –install . –prefix C:/CLBlast
This will install openblast compiled file in C:/CLBlast.
How to install Llama.cpp and compile llama.cpp with CLBlast accelerated with Intel arc A770 ?
Clone locally llama.cpp
Create a build directory
Dans CMakeLists, dans la partie CLBlast (if (LLAMA_CLBLAST), on ajoute 2 lignes et on commente une ligne:
#HP Ajouter
include_directories(“E:\OneDrive – UBI\IA_Pytorch\CLBlast\include” “C:\Program Files (x86)\Intel\oneAPI\compiler\2023.2.0\windows\include\sycl”)
#HP Ajouter
link_libraries(“E:\OneDrive – UBI\IA_Pytorch\CLBlast\lib” “C:\Program Files (x86)\Intel\oneAPI\intelpython\python3.9\envs\2023.2.0\Library\lib”)
#HP commenter set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} clblast)
cmake .. -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=”C:\CLBlast”
cmake –build . –config Release
How to run your LLM by example Llama2 using low octs accelerated Intel Arc A770 graphic card ?
set GGML_OPENCL_PLATFORM=”Intel(R) OpenCL Graphics”
set GGML_OPENCL_DEVICE=”Intel(R) Arc(TM) A770 Graphics (ALL | GPU)”
./main -t 10 -ngl 100 -m “E:\OneDrive – UBI\IA_Pytorch\Models\llama-2-13b-chat.Q8_0.gguf” -c 2048 –temp 0.7 –repeat_penalty 1.1 –color –instruct –temp 0.8 –top_k 40 –top_p 0.95 –n_predict -1 –keep -1 -r “USER:” -p “
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