llama.cpp/docker

Install Docker Server

Important

This was tested with Docker running on Linux.
If you can get it working on Windows or MacOS, please update this README.md with a PR!

Install Docker Engine

Simple Dockerfiles for building the llama-cpp-python server with external model bin files

openblas_simple

A simple Dockerfile for non-GPU OpenBLAS, where the model is located outside the Docker image:

cd ./openblas_simple
docker build -t openblas_simple .
docker run --cap-add SYS_RESOURCE -e USE_MLOCK=0 -e MODEL=/var/model/<model-path> -v <model-root-path>:/var/model -t openblas_simple

where <model-root-path>/<model-path> is the full path to the model file on the Docker host system.

cuda_simple

Warning

Nvidia GPU CuBLAS support requires an Nvidia GPU with sufficient VRAM (approximately as much as the size in the table below) and Docker Nvidia support (see container-toolkit/install-guide)

A simple Dockerfile for CUDA-accelerated CuBLAS, where the model is located outside the Docker image:

cd ./cuda_simple
docker build -t cuda_simple .
docker run --gpus=all --cap-add SYS_RESOURCE -e USE_MLOCK=0 -e MODEL=/var/model/<model-path> -v <model-root-path>:/var/model -t cuda_simple

where <model-root-path>/<model-path> is the full path to the model file on the Docker host system.

---

"Open-Llama-in-a-box"

Download an Apache V2.0 licensed 3B params Open LLaMA model and install into a Docker image that runs an OpenBLAS-enabled llama-cpp-python server:

$ cd ./open_llama
./build.sh
./start.sh

Manually choose your own Llama model from Hugging Face

python3 ./hug_model.py -a TheBloke -t llama You should now have a model in the current directory and model.bin symlinked to it for the subsequent Docker build and copy step. e.g.

docker $ ls -lh *.bin
-rw-rw-r-- 1 user user 4.8G May 23 18:30 <downloaded-model-file>q5_1.bin
lrwxrwxrwx 1 user user   24 May 23 18:30 model.bin -> <downloaded-model-file>q5_1.bin

Note

Make sure you have enough disk space to download the model. As the model is then copied into the image you will need at least TWICE as much disk space as the size of the model:

Model	Quantized size
3B	3 GB
7B	5 GB
13B	10 GB
33B	25 GB
65B	50 GB

Note

If you want to pass or tune additional parameters, customise ./start_server.sh before running docker build ...