Building a Website Using LLama.cpp and Docker

in

Now, before you start rolling your eyes at me for suggesting that a language model can somehow magically create a website out of thin air, hear me out. With LLama.cpp and Docker, we’re not exactly building websites from scratch but rather using pre-trained models to generate content based on prompts provided by the user.

So how does it work? Well, first you need to download LLama.cpp (which is a lightweight version of the popular GPT-3 language model) and install Docker (if you haven’t already). Then, using your favorite text editor or command line tool, create a new file called `Dockerfile` in your project directory with the following contents:

# Use a base image from Google Container Registry (gcr.io) with a C++ compiler
FROM gcr.io/distroless/cc-base:latest

# Update package manager and install python3-pip
RUN apt update && apt install -y python3-pip

# Copy all files from current directory to /app directory in the container
COPY . /app

# Set working directory to /app
WORKDIR /app

# Install llama-cpp version 0.12.4 without caching
RUN pip install --no-cache-dir llama-cpp==0.12.4

# Set command to run when container is started
CMD ["python", "llama_cuda.py"]

This Dockerfile sets up a base image with Python 3 and then copies your project files into the container, installs LLama.cpp using pip, and runs `llama_cuda.py`. You can customize this script to suit your needs for example, you might want to add some additional arguments or modify the output format.

Once you’ve saved your Dockerfile, build a new image by running:

# This script builds a Docker image named "my-llama-image" using the current directory as the build context.
# The image will contain all project files and dependencies needed to run LLama.cpp and llama_cuda.py.

# Build the Docker image using the current directory as the build context and tag it as "my-llama-image".
docker build . -t my-llama-image

This will create an image called `my-llama-image` based on your project directory (which should contain the `Dockerfile` and any other necessary files).

Now, let’s say you want to generate some content for a new website. You could run:

# This command runs a docker container with access to all GPUs and mounts the models directory to the container's /models directory.
docker run --gpus all -v /path/to/models:/models my-llama-image:latest \
# This flag specifies the model to be used for generating content.
-m models/7B/ggml-model-q4_0.gguf \
# This flag specifies the prompt or starting text for the content generation.
-p "Building a website can be done in 10 simple steps:" \
# This flag specifies the number of words to generate.
-n 512 \
# This flag specifies the number of layers to use for GPU acceleration.
--n-gpu-layers 1

This will start a new container using your `my-llama-image`, mount the directory containing your pre-trained models (which should be located at `/path/to/models`) to the container’s `/models` directory, and pass in some arguments for LLama.cpp:

– `-m` specifies the path to the model file (in this case, a 7B GGML model). `-p` provides an initial prompt or question that LLama.cpp will use as input. In this example, we’re asking it to generate content about building websites in 10 simple steps. `-n` sets the number of tokens (words) that LLama.cpp should generate for each output line. `–n-gpu-layers` specifies how many layers of GPU acceleration to use when generating text. In this case, we’re using just one layer since our model is relatively small and doesn’t require much memory or processing power.

Once LLama.cpp has finished running (which should only take a few seconds), you can view the output by checking the container logs:

# This script is used to view the output of LLama.cpp by checking the container logs.
# The container ID is passed as an argument to the docker logs command.

# The following line uses the docker logs command to view the output of the container with the specified ID.
docker logs $1

This will display any text that was generated during the run, as well as any errors or warnings that may have occurred.

And there you have it building websites using LLama.cpp and Docker! It’s not exactly rocket science (or AI), but it can be a fun way to experiment with language models and generate some interesting content for your next project.

SICORPS