This repository, LLM-PowerHouse, serves as a comprehensive guide for individuals looking to leverage Large Language Models (LLMs) for custom training and inference. It targets developers, researchers, and enthusiasts, offering curated tutorials, best practices, and ready-to-use code to build advanced natural language understanding applications.

How It Works

The guide is structured into several key areas: Foundations of LLMs, The Art of LLM Science, Building Production-Ready LLM Applications, In-Depth Articles, Model Compression, Evaluation Metrics, Open LLMs, Cost Analysis, Codebase Mastery, LLM PlayLab, and LLM Datasets. It covers foundational concepts in mathematics, Python, and NLP, delves into LLM architectures like Transformers, and explores training methodologies including pre-training, supervised fine-tuning (SFT), and Reinforcement Learning from Human Feedback (RLHF) or Direct Preference Optimization (DPO). Practical aspects like quantization, inference optimization, deployment strategies, and security are also detailed.

Quick Start & Requirements

This is a guide and resource repository, not a runnable application. Users will need Python and relevant libraries (e.g., PyTorch, Hugging Face Transformers) to follow the tutorials and run the provided code examples. Specific hardware requirements (e.g., GPUs with significant VRAM) are mentioned for fine-tuning and running larger models.

Highlighted Details

• Comprehensive coverage from foundational math and NLP to advanced LLM training and deployment.
• Detailed explanations of various fine-tuning techniques like LoRA, QLoRA, and RLHF/DPO.
• Practical guidance on quantization methods (GGUF, GPTQ, AWQ) for efficient inference.
• Exploration of emerging trends like Mixture of Experts (MoE) and multimodal models.

Maintenance & Community

The repository is maintained by Sunil Ghimire. Contributions are welcome via pull requests. Links to further resources and related projects are provided throughout the documentation.

Licensing & Compatibility

The project is licensed under the MIT License, permitting commercial use and modification.

Limitations & Caveats

The repository is a guide, not a deployable system. Users must have a strong understanding of Python and machine learning concepts to utilize its content effectively. Specific hardware requirements for training and inference are significant. The author notes that prompt engineering requires continuous adaptation as models evolve.