LlamaGPT

What is the project about?

LlamaGPT is a self-hosted, offline, ChatGPT-like chatbot powered by Llama 2 and Code Llama models. It's designed to be 100% private, with no data leaving the user's device.

What problem does it solve?

It provides a private and secure alternative to cloud-based chatbot services like ChatGPT, addressing privacy concerns by keeping all data local. It also offers offline functionality.

What are the features of the project?

Self-hosted: Runs on the user's own hardware (umbrelOS home server, M1/M2 Mac, any system with Docker, or Kubernetes).
Offline: Works without an internet connection after the initial model download.
Private: No data leaves the user's device.
Multiple Models: Supports various Llama 2 and Code Llama models (7B, 13B, 34B, 70B) with different sizes and memory requirements.
Chatbot UI: Uses a user-friendly interface based on Chatbot UI.
OpenAI-compatible API: Offers an API that mimics OpenAI's, making it easy to integrate with other applications.
Nvidia GPU support: Can utilize Nvidia GPUs for accelerated performance.
Metal Support: Support for running on Apple silicon (M1/M2 Macs).

What are the technologies used in the project?

Llama 2 and Code Llama: The underlying language models.
llama.cpp: A C++ implementation for running Llama models.
llama-cpp-python: Python bindings for llama.cpp.
Docker: For containerization and deployment.
Kubernetes: For container orchestration (optional deployment method).
Chatbot UI: The frontend user interface.
GGML/GGUF: Model file formats.

What are the benefits of the project?

Privacy: Complete control over data, as it never leaves the user's device.
Security: Reduced risk of data breaches compared to cloud services.
Offline Access: Usable even without an internet connection.
Cost-effective: Potentially cheaper than subscription-based AI services, especially for heavy usage.
Customization: The roadmap includes plans for supporting custom models.
Open Source: Transparent and community-driven.

What are the use cases of the project?

Private Chatbot: For personal use where privacy is paramount.
Local Development: Testing and developing applications that use language models without relying on external APIs.
Offline Applications: Building applications that require chatbot functionality in environments with limited or no internet connectivity.
Secure Environments: Deploying chatbots in sensitive environments where data security is critical.
Code Generation/Assistance: Utilizing Code Llama models for coding tasks.
Integration with other applications: Using the OpenAI-compatible API.