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cuda_simple | ||
open_llama | ||
openblas_simple | ||
simple | ||
README.md |
Install Docker Server
Important
This was tested with Docker running on Linux.
If you can get it working on Windows or MacOS, please update thisREADME.md
with a PR!
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 -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 runningdocker build ...