This repository provides detailed, step-by-step instructions for installing and configuring the Vicuna large language model (LLM), specifically the 13B and 7B parameter versions. It targets users who want to run Vicuna locally on their own hardware, offering a streamlined process for setup and usage with the llama.cpp framework.

How It Works

The guide leverages the llama.cpp project, a C/C++ implementation of LLaMA inference, to enable efficient local execution of Vicuna models. It focuses on using GGUF quantized model formats, which are optimized for CPU inference and reduced memory footprint. The instructions detail cloning the llama.cpp repository, compiling it, and then downloading specific Vicuna GGUF model files from Hugging Face.

Quick Start & Requirements

• Install:
  • One-line script for 13B: git clone https://github.com/fredi-python/llama.cpp.git && cd llama.cpp && make -j && cd models && wget -c https://huggingface.co/TheBloke/vicuna-13B-v1.5-GGUF/resolve/main/vicuna-13b-v1.5.Q4_K_M.gguf
  • One-line script for 7B: git clone https://github.com/fredi-python/llama.cpp.git && cd llama.cpp && make -j && cd models && wget -c https://huggingface.co/TheBloke/vicuna-7B-v1.5-GGUF/resolve/main/vicuna-7b-v1.5.Q4_K_M.gguf
• Requirements:
  • Vicuna 13B: ~10GB CPU RAM (swap can be increased if needed).
  • git and wget packages.
  • Unix-based operating system recommended.
• Links:
  • Increase swapfile on Linux: https://arcolinux.com/how-to-increase-the-size-of-your-swapfile/

Highlighted Details

• Provides specific commands for both Vicuna 13B and 7B models.
• Utilizes GGUF quantized model formats for efficient CPU inference.
• Includes example usage command for interactive chat with the model.
• References a fork of llama.cpp (fredi-python/llama.cpp.git).

Maintenance & Community

No specific information on maintainers, community channels, or roadmap is provided in the README.

Licensing & Compatibility

The README does not explicitly state the license for the installation guide itself. It directs users to download Vicuna models from Hugging Face, whose licensing terms would apply to the models. Compatibility for commercial use depends on the underlying Vicuna model license and llama.cpp license.

Limitations & Caveats

The guide is focused solely on installation and basic usage via llama.cpp, and does not cover advanced configuration, fine-tuning, or alternative inference engines. It relies on a specific fork of llama.cpp.