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kaust-generative-ai / local-deployment-llama-cpp

Project to help you get started working with LLMs locally with LLaMA C++.
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local-deplyoment-llama-cpp

Project to help you get started deploying Generative AI models locally using LLaMA C++. LLaMA C++ enables LLM inference with minimal setup and state-of-the-art performance on a wide variety of hardware - locally and in the cloud.

Installation

Miniforge, Conda, Mamba (Mac OS, Linux, Windows)

If you haven't already done so, install Miniforge. Miniforge provides minimal installers for Conda and Mamba specific to conda-forge, with the following features pre-configured:

Conda/mamba will be the primary package managers used to install the required Python dependencies. For convenience, a script is included that will download and install Miniforge, Conda, and Mamba. You can run the script using the following command.

./bin/install-miniforge.sh

Create the Conda environment

After adding any necessary dependencies that should be installed via conda/mamba to the environment.yml file and any dependencies that should be installed via pip to the requirements.txt file you create the Conda environment in a sub-directory ./envof your project directory by running the following shell script.

./bin/create-conda-env.sh

Support for Metal GPU acceleration (Mac M1+ only)

In order to support Metal GPU acceleration you need to install a few extra dependecies. These dependencies are added to the environment-metal-gpu.yml and requirements-metal-gpu.txt files. Create the Conda environment in a sub-directory ./envof your project directory by running the following shell script.

./bin/create-conda-env.sh environment-metal-gpu.yml

Support for NVIDIA GPU acceleration (Linux and Windows)

In order to support Metal GPU acceleration you need to install a few extra dependecies. These dependencies are added to the environment-nvidia-gpu.yml and requirements-nvidia-gpu.txt files. Create the Conda environment in a sub-directory ./envof your project directory by running the following shell script.

./bin/create-conda-env.sh environment-nvidia-gpu.yml

Install LLaMA C++

For convenience there is an installer scripts which can be used to download pre-compiled LLaMA C++ binaries for various OS and CPU architectures and install the binaries into the bin/ directory of the Conda environment. You can find the latest release for LLaMA C++ on GitHub and pass the link to the zip archive for you specific release to the script as a command line argument.

Mac ARM CPU

DOWNLOAD_URL=https://github.com/ggerganov/llama.cpp/releases/download/
TAG=b3868
RELEASE_ARCHIVE=lllama-b3868-bin-macos-arm64.zip
./bin/install-llama-cpp.sh "$DOWNLOAD_URL"/"$TAG"/"$RELEASE_ARCHIVE"

Mac Intel CPU

DOWNLOAD_URL=https://github.com/ggerganov/llama.cpp/releases/download/
TAG=b3868
RELEASE_ARCHIVE=llama-b3868-bin-macos-x64.zip
./bin/install-llama-cpp.sh "$DOWNLOAD_URL"/"$TAG"/"$RELEASE_ARCHIVE"

Ubuntu

DOWNLOAD_URL=https://github.com/ggerganov/llama.cpp/releases/download/
TAG=b3868
RELEASE_ARCHIVE=llama-b3868-bin-ubuntu-x64.zip
./bin/install-llama-cpp.sh "$DOWNLOAD_URL"/"$TAG"/"$RELEASE_ARCHIVE"

Windows

DOWNLOAD_URL=https://github.com/ggerganov/llama.cpp/releases/download/
TAG=b3868
RELEASE_ARCHIVE=llama-b3868-bin-win-avx512-x64.zip
./bin/install-llama-cpp.sh "$DOWNLOAD_URL"/"$TAG"/"$RELEASE_ARCHIVE"

Building LLaMA C++ from source

Install XCode and Command Line Tools (Mac OS)

Install XCode. Then run the following command to install XCode Command Line Tools.

xcode-select --install

Building LLaMA C++ with BLAS acceleration (Mac OS, Linux, Windows)

After creating the Conda environment you can build LLaMA C++ by running the following command.

conda run --prefix ./env --live-stream ./bin/build-llama-cpp.sh

This command does the following.

  1. Properly configures the Conda environment.
  2. Clones [LLaMA C++](https://github.com/ggerganov/llama.cpp into ./src/llama-cpp.
  3. Builds LLaMA C++ with support for CPU acceleration using OpenBlas in ./build/llama-cpp.
  4. Installs the binaries into the bin/ directory of the Conda environment.
  5. Removes the ./src/llama-cpp directory as it is no longer needed.
  6. Removes the ./build/llama-cpp directory as it is no longer needed.

Support for Metal GPU acceleration (Mac M1+ only)

After creating the Conda environment you can build LLaMA C++ by running the following command.

conda run --prefix ./env --live-stream ./bin/build-llama-cpp-metal-gpu.sh

Support for NVIDIA GPU acceleration (Linux and Windows)

After creating the Conda environment you can build LLaMA C++ by with support for GPU acceleration by running the following command.

conda run --prefix ./env --live-stream ./bin/build-llama-cpp-nvidia-gpu.sh

For a detailed discussion of additional NVIDIA GPU compilation options that might improve performance on particular GPU architectures see the LLaMA C++ build documentation.

Activating the Conda environment

Once the new environment has been created you can activate the environment with the following command.

conda activate ./env

Note that the ./env directory is not under version control as it can always be re-created as necessary.

Funding Acknowledgment

This project is supported by funding from King Abdullah University of Science and Technology (KAUST) - Center of Excellence for Generative AI, under award number 5940.