This repository provides a practical, hands-on guide to building and deploying Large Language Models (LLMs). It targets engineers and researchers looking to understand core LLM technologies through a series of video tutorials and accompanying code examples, covering fine-tuning, deployment, RAG, and related AI workflows.

How It Works

The project is structured as a collection of modular code repositories and video tutorials, each focusing on a specific LLM-related task. It covers a broad spectrum of LLM lifecycle management, from data preparation and fine-tuning (e.g., using llama-factory) to efficient deployment (e.g., llama.cpp, vllm, ollama) and advanced applications like Retrieval-Augmented Generation (RAG) with llama-index and graph-rag.

Quick Start & Requirements

• Installation and usage vary per module; refer to individual module documentation.
• Prerequisites typically include Python, with specific modules potentially requiring CUDA, Docker, or specific LLM model weights.
• Links to video tutorials and code documentation are provided within the README for each module.

Highlighted Details

• Comprehensive coverage of LLM ecosystem tools: llama-factory, llama.cpp, ollama, vllm, llama-index, graph-rag, mkdocs, label-studio, dify.
• Practical application examples including RAG, function calling, RPA automation, and agent-based workflows.
• Dedicated series on Kaggle LLM competitions and Gradio for AI application building.
• "Unconventional AI Projects" section showcases creative LLM applications like a multi-person podcast tool and an automated news robot.

Maintenance & Community

• The project appears to be actively maintained, with recent commits indicated.
• A community discussion channel is available via a provided link for troubleshooting and topic exchange.

Licensing & Compatibility

• The repository's licensing is not explicitly stated in the provided README content.

Limitations & Caveats

• The README does not specify a license, which may impact commercial use or redistribution.
• Setup complexity and resource requirements will vary significantly across the different modules presented.