llama-cpp-python offers an OpenAI API compatible web server.

This web server can be used to serve local models and easily connect them to existing clients.

The server can be installed by running the following command:

pip install llama-cpp-python[server]

The server can then be started by running the following command:

python3 -m llama_cpp.server --model <model_path>

For a full list of options, run:

python3 -m llama_cpp.server --help

NOTE: All server options are also available as environment variables. For example, --model can be set by setting the MODEL environment variable.

llama-cpp-python supports code completion via GitHub Copilot.

NOTE: Without GPU acceleration this is unlikely to be fast enough to be usable.

You'll first need to download one of the available code completion models in GGUF format:

• replit-code-v1_5-GGUF

Then you'll need to run the OpenAI compatible web server with a increased context size substantially for GitHub Copilot requests:

python3 -m llama_cpp.server --model <model_path> --n_ctx 16192

Then just update your settings in .vscode/settings.json to point to your code completion server:

{
    // ...
    "github.copilot.advanced": {
        "debug.testOverrideProxyUrl": "http://<host>:<port>",
        "debug.overrideProxyUrl": "http://<host>:<port>"
    }
    // ...
}

llama-cpp-python supports structured function calling based on a JSON schema. Function calling is completely compatible with the OpenAI function calling API and can be used by connecting with the official OpenAI Python client.

You'll first need to download one of the available function calling models in GGUF format:

• functionary-7b-v1

Then when you run the server you'll need to also specify the functionary chat_format

python3 -m llama_cpp.server --model <model_path> --chat_format functionary

Check out this example notebook for a walkthrough of some interesting use cases for function calling.

llama-cpp-python supports the llava1.5 family of multi-modal models which allow the language model to read information from both text and images.

You'll first need to download one of the available multi-modal models in GGUF format:

• llava-v1.5-7b
• llava-v1.5-13b
• bakllava-1-7b

Then when you run the server you'll need to also specify the path to the clip model used for image embedding and the llava-1-5 chat_format

python3 -m llama_cpp.server --model <model_path> --clip_model_path <clip_model_path> --chat_format llava-1-5

Then you can just use the OpenAI API as normal

from openai import OpenAI

client = OpenAI(base_url="http://<host>:<port>/v1", api_key="sk-xxx")
response = client.chat.completions.create(
    model="gpt-4-vision-preview",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "<image_url>"
                    },
                },
                {"type": "text", "text": "What does the image say"},
            ],
        }
    ],
)
print(response)