#llama.cpp

This Windows-compatible project provides a user-friendly interface for AI models like Alpaca, eliminating command line needs and expensive hardware requirements. Using llama.cpp, it runs efficiently on CPUs, enabling offline operation. With an intuitive UI and future features like chat history and GPU support planned, it caters to those seeking a robust AI chat solution.

Paddler provides a stateful load balancing solution tailored for llama.cpp, focusing on efficient slot-based request distribution. Features include flexible autoscaling, AWS compatibility, and real-time monitoring via a built-in dashboard. Paddler aims to optimize server performance and cost-effectiveness without unnecessary extraneous adjectives.

The gguf-tools library facilitates the manipulation and documentation of GGUF files, essential in the local machine learning landscape. The tools include utilities for displaying detailed GGUF information, analyzing tensor differences between models, and examining tensor weights. Although in active development, the project provides real-world applications, with experimental features still under refinement. While user-friendly and well-documented, its current limitations include missing quantization formats. Discover its potential applications in 'llama.cpp' and other machine learning projects.

Explore an effective toolkit for air-gapped LLMs featuring LangChain and Qdrant. It supports local data processing and query handling without internet requirement, facilitating diverse dataset ingestion and robust GUI interaction. Includes GPU support and model conversion from GGML for optimal use.

Discover how llama_ros enables the integration of llama.cpp's optimization features into ROS 2 projects. It supports GGUF-based LLMs, VLMs, real-time LoRA changes, and GBNF grammars, enhancing robotic applications. The repository includes detailed installation guides, Docker options, and usage examples. Enhance ROS 2 functionality with CUDA support and other tools offered by llama_ros, suitable for expanding project capabilities with LangChain and related demos.

Discover efficient Python bindings for llama.cpp, providing C API access and high-level APIs for text completion. Features include OpenAI-compatible servers, model variety, and integration with LangChain and LlamaIndex. The package is installable on Python 3.8+ with any C compiler, supporting hardware acceleration. Comprehensive documentation aids integration, making it ideal for both standard and complex AI applications.

This API server interfaces with the llama.cpp implementation of LLaVA, providing seamless interaction through a customizable server setup. Users can download model files, adjust server configurations, and interact via a /llava endpoint with support for image and text prompts. Compatible with any llama.cpp-supported system, initially tested on macOS, it's easily customizable for different hosts and ports. Enhance your application's capabilities with this readily deployable solution.