🦙 Python Bindings for llama.cpp

Simple Python bindings for @ggerganov's llama.cpp library. This package provides:

• Low-level access to C API via ctypes interface.
• High-level Python API for text completion
  • OpenAI-like API
  • LangChain compatibility
• OpenAI compatible web server
  • Local Copilot replacement
  • Function Calling support
  • Vision API support

Documentation is available at https://llama-cpp-python.readthedocs.io/en/latest.

Installation from PyPI

Install from PyPI (requires a c compiler):

pip install llama-cpp-python

The above command will attempt to install the package and build llama.cpp from source. This is the recommended installation method as it ensures that llama.cpp is built with the available optimizations for your system.

If you have previously installed llama-cpp-python through pip and want to upgrade your version or rebuild the package with different compiler options, please add the following flags to ensure that the package is rebuilt correctly:

pip install llama-cpp-python --force-reinstall --upgrade --no-cache-dir

Note: If you are using Apple Silicon (M1) Mac, make sure you have installed a version of Python that supports arm64 architecture. For example:

wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh
bash Miniforge3-MacOSX-arm64.sh

Otherwise, while installing it will build the llama.cpp x86 version which will be 10x slower on Apple Silicon (M1) Mac.

Installation with Hardware Acceleration

llama.cpp supports multiple BLAS backends for faster processing.

To install with OpenBLAS, set the LLAMA_BLAS and LLAMA_BLAS_VENDOR environment variables before installing:

CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python

To install with cuBLAS, set the LLAMA_CUBLAS=1 environment variable before installing:

CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python

To install with CLBlast, set the LLAMA_CLBLAST=1 environment variable before installing:

CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python

To install with Metal (MPS), set the LLAMA_METAL=on environment variable before installing:

CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python

To install with hipBLAS / ROCm support for AMD cards, set the LLAMA_HIPBLAS=on environment variable before installing:

CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python

Windows remarks

To set the variables CMAKE_ARGSin PowerShell, follow the next steps (Example using, OpenBLAS):

$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"

Then, call pip after setting the variables:

pip install llama-cpp-python

If you run into issues where it complains it can't find 'nmake' '?' or CMAKE_C_COMPILER, you can extract w64devkit as mentioned in llama.cpp repo and add those manually to CMAKE_ARGS before running pip install:

$env:CMAKE_GENERATOR = "MinGW Makefiles"
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on -DCMAKE_C_COMPILER=C:/w64devkit/bin/gcc.exe -DCMAKE_CXX_COMPILER=C:/w64devkit/bin/g++.exe" 

See the above instructions and set CMAKE_ARGS to the BLAS backend you want to use.

MacOS remarks

Detailed MacOS Metal GPU install documentation is available at docs/install/macos.md

High-level API

The high-level API provides a simple managed interface through the Llama class.

Below is a short example demonstrating how to use the high-level API to for basic text completion:

>>> from llama_cpp import Llama
>>> llm = Llama(model_path="./models/7B/llama-model.gguf")
>>> output = llm(
      "Q: Name the planets in the solar system? A: ", # Prompt
      max_tokens=32, # Generate up to 32 tokens
      stop=["Q:", "\n"], # Stop generating just before the model would generate a new question
      echo=True # Echo the prompt back in the output
) # Generate a completion, can also call create_completion
>>> print(output)
{
  "id": "cmpl-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx",
  "object": "text_completion",
  "created": 1679561337,
  "model": "./models/7B/llama-model.gguf",
  "choices": [
    {
      "text": "Q: Name the planets in the solar system? A: Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, Neptune and Pluto.",
      "index": 0,
      "logprobs": None,
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 14,
    "completion_tokens": 28,
    "total_tokens": 42
  }
}

Chat Completion

The high-level API also provides a simple interface for chat completion.

Note that chat_format option must be set for the particular model you are using.

>>> from llama_cpp import Llama
>>> llm = Llama(model_path="path/to/llama-2/llama-model.gguf", chat_format="llama-2")
>>> llm.create_chat_completion(
      messages = [
          {"role": "system", "content": "You are an assistant who perfectly describes images."},
          {
              "role": "user",
              "content": "Describe this image in detail please."
          }
      ]
)

Function Calling

The high-level API also provides a simple interface for function calling.

Note that the only model that supports full function calling at this time is "functionary". The gguf-converted files for this model can be found here: functionary-7b-v1

>>> from llama_cpp import Llama
>>> llm = Llama(model_path="path/to/functionary/llama-model.gguf", chat_format="functionary")
>>> llm.create_chat_completion(
      messages = [
        {
          "role": "system",
          "content": "A chat between a curious user and an artificial intelligence assitant. The assistant gives helpful, detailed, and polite answers to the user's questions. The assistant callse functions with appropriate input when necessary"
        },
        {
          "role": "user",
          "content": "Extract Jason is 25 years old"
        }
      ],
      tools=[{
        "type": "function",
        "function": {
          "name": "UserDetail",
          "parameters": {
            "type": "object"
            "title": "UserDetail",
            "properties": {
              "name": {
                "title": "Name",
                "type": "string"
              },
              "age": {
                "title": "Age",
                "type": "integer"
              }
            },
            "required": [ "name", "age" ]
          }
        }
      }],
      tool_choices=[{
        "type": "function",
        "function": {
          "name": "UserDetail"
        }
      }]
)

Multi-modal Models

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 you'll need to use a custom chat handler to load the clip model and process the chat messages and images.

>>> from llama_cpp import Llama
>>> from llama_cpp.llama_chat_format import Llava15ChatHandler
>>> chat_handler = Llava15ChatHandler(clip_model_path="path/to/llava/mmproj.bin")
>>> llm = Llama(
  model_path="./path/to/llava/llama-model.gguf",
  chat_handler=chat_handler,
  n_ctx=2048 # n_ctx should be increased to accomodate the image embedding
)
>>> llm.create_chat_completion(
    messages = [
        {"role": "system", "content": "You are an assistant who perfectly describes images."},
        {
            "role": "user",
            "content": [
                {"type": "image_url", "image_url": {"url": "https://.../image.png"}},
                {"type" : "text", "text": "Describe this image in detail please."}
            ]
        }
    ]
)

Adjusting the Context Window

The context window of the Llama models determines the maximum number of tokens that can be processed at once. By default, this is set to 512 tokens, but can be adjusted based on your requirements.

For instance, if you want to work with larger contexts, you can expand the context window by setting the n_ctx parameter when initializing the Llama object:

llm = Llama(model_path="./models/7B/llama-model.gguf", n_ctx=2048)

Web Server

llama-cpp-python offers a web server which aims to act as a drop-in replacement for the OpenAI API. This allows you to use llama.cpp compatible models with any OpenAI compatible client (language libraries, services, etc).

To install the server package and get started:

pip install llama-cpp-python[server]
python3 -m llama_cpp.server --model models/7B/llama-model.gguf

Similar to Hardware Acceleration section above, you can also install with GPU (cuBLAS) support like this:

CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python[server]
python3 -m llama_cpp.server --model models/7B/llama-model.gguf --n_gpu_layers 35

Navigate to http://localhost:8000/docs to see the OpenAPI documentation.

To bind to 0.0.0.0 to enable remote connections, use python3 -m llama_cpp.server --host 0.0.0.0. Similarly, to change the port (default is 8000), use --port.

You probably also want to set the prompt format. For chatml, use

python3 -m llama_cpp.server --model models/7B/llama-model.gguf --chat_format chatml

That will format the prompt according to how model expects it. You can find the prompt format in the model card. For possible options, see llama_cpp/llama_chat_format.py and look for lines starting with "@register_chat_format".

Docker image

A Docker image is available on GHCR. To run the server:

docker run --rm -it -p 8000:8000 -v /path/to/models:/models -e MODEL=/models/llama-model.gguf ghcr.io/abetlen/llama-cpp-python:latest

Docker on termux (requires root) is currently the only known way to run this on phones, see termux support issue

Low-level API

The low-level API is a direct ctypes binding to the C API provided by llama.cpp. The entire low-level API can be found in llama_cpp/llama_cpp.py and directly mirrors the C API in llama.h.

Below is a short example demonstrating how to use the low-level API to tokenize a prompt:

>>> import llama_cpp
>>> import ctypes
>>> llama_cpp.llama_backend_init(numa=False) # Must be called once at the start of each program
>>> params = llama_cpp.llama_context_default_params()
# use bytes for char * params
>>> model = llama_cpp.llama_load_model_from_file(b"./models/7b/llama-model.gguf", params)
>>> ctx = llama_cpp.llama_new_context_with_model(model, params)
>>> max_tokens = params.n_ctx
# use ctypes arrays for array params
>>> tokens = (llama_cpp.llama_token * int(max_tokens))()
>>> n_tokens = llama_cpp.llama_tokenize(ctx, b"Q: Name the planets in the solar system? A: ", tokens, max_tokens, add_bos=llama_cpp.c_bool(True))
>>> llama_cpp.llama_free(ctx)

Check out the examples folder for more examples of using the low-level API.

Documentation

Documentation is available via https://llama-cpp-python.readthedocs.io/. If you find any issues with the documentation, please open an issue or submit a PR.

Development

This package is under active development and I welcome any contributions.

To get started, clone the repository and install the package in editable / development mode:

git clone --recurse-submodules https://github.com/abetlen/llama-cpp-python.git
cd llama-cpp-python

# Upgrade pip (required for editable mode)
pip install --upgrade pip

# Install with pip
pip install -e .

# if you want to use the fastapi / openapi server
pip install -e .[server]

# to install all optional dependencies
pip install -e .[all]

# to clear the local build cache
make clean

How does this compare to other Python bindings of llama.cpp?

I originally wrote this package for my own use with two goals in mind:

• Provide a simple process to install llama.cpp and access the full C API in llama.h from Python
• Provide a high-level Python API that can be used as a drop-in replacement for the OpenAI API so existing apps can be easily ported to use llama.cpp

Any contributions and changes to this package will be made with these goals in mind.

License

This project is licensed under the terms of the MIT license.