Note #2: NVidia GPU CuBLAS support requires a NVidia GPU with sufficient VRAM (approximately as much as the size above) and Docker NVidia support (see container-toolkit/install-guide)

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

./openblas_simple/Dockerfile - 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 -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/Dockerfile - 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 -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 a MIT licensed 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 #1: 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 #2: If you want to pass or tune additional parameters, customise ./start_server.sh before running docker build ...

Use OpenBLAS

Use if you don't have a NVidia GPU. Defaults to python:3-slim-bullseye Docker base image and OpenBLAS:

Build:

docker build -t openblas .

Run:

docker run --cap-add SYS_RESOURCE -t openblas

Use CuBLAS

Build:

docker build --build-arg IMAGE=nvidia/cuda:12.1.1-devel-ubuntu22.04 -t cublas .

Run:

docker run --cap-add SYS_RESOURCE -t cublas