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Llama-cpp-python TOC

  1. Overview
  2. Create a custom Docker image with CUDA support and server.config
  3. Create a custom Docker image with server.config
  4. Run the docker image (CUDA or CPU version)
  5. Using Llama-cpp server
  6. Server Configuration using a server.conf file

Llama-cpp-python Server

Executive Summary

Reference: CoPilot

llama-cpp-python is a Python package that provides bindings for the llama.cpp library, which implements Meta’s LLaMA (Large Language Model Meta AI) architecture in efficient C++. This integration allows developers to leverage the speed and efficiency of C++ within the flexible and widely-used Python environment

Key Features of llama-cpp-python

  1. Low-Level Access: Provides low-level access to the C API via a ctypes interface.
  2. High-Level Python API: Offers a high-level Python API for text completion, similar to OpenAI’s API.
  3. Compatibility: Compatible with LangChain and LlamaIndex, making it easier to integrate into existing workflows.
  4. Web Server: Includes an OpenAI-compatible web server, allowing it to serve as a local Copilot replacement.
  5. Multiple Models: Supports multiple models, function calling, and vision API23.

Benefits of Using llama-cpp-python

  1. Efficiency: By leveraging C++ for core computations, llama-cpp-python provides high performance and efficiency, which is crucial for handling large language models.
  2. Portability: Designed to run on consumer-grade hardware, including personal computers and laptops, without requiring high-end GPUs or specialized hardware4.
  3. Flexibility: Combines the computational efficiency of C++ with the ease of use of Python, making it suitable for a wide range of applications5.
  4. Universal Compatibility: Its CPU-first design ensures less complexity and seamless integration into various programming environments1.
  5. Focused Optimization: Optimized for the LLaMA models, enabling precise and effective improvements in performance1.

Instructions

Steps to run and use Llama-cpp.server:

  1. Define your server configuration (server.config)
  2. Startup the server in docker
  3. Access lama-cpp.server from your code

If you need to modify the docker container, then see:

  1. Create a custom Docker image with CUDA support and server.config
  2. Create a custom Docker image with server.config

[!NOTE] this is a note

An alternate to llama-cpp-python is the base llama-cpp in docker

However, this is not covered in this project.