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**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](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html))
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**Note #2:** NVidia GPU CuBLAS support requires a NVidia GPU with sufficient VRAM (approximately as much as the size in the table below) and Docker NVidia support (see [container-toolkit/install-guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html))
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# Simple Dockerfiles for building the llama-cpp-python server with external model bin files
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## openblas_simple - a simple Dockerfile for non-GPU OpenBLAS, where the model is located outside the Docker image
where `<model-root-path>/<model-path>` is the full path to the model file on the Docker host system.
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# "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
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# "Open-Llama-in-a-box"
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## Download an Apache V2.0 licensed 3B paramter Open Llama model and install into a Docker image that runs an OpenBLAS-enabled llama-cpp-python server
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```
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$ cd ./open_llama
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./build.sh
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./start.sh
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```
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# Manually choose your own Llama model from Hugging Face
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-`python3 ./hug_model.py -a TheBloke -t llama`
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-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.
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`python3 ./hug_model.py -a TheBloke -t llama`
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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.
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```
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docker $ ls -lh *.bin
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-rw-rw-r-- 1 user user 4.8G May 23 18:30 <downloaded-model-file>q5_1.bin
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