This repository provides practical code implementations for the book "Large Language Models: From Theory to Practice," covering LLM fundamentals, pre-training, fine-tuning, RLHF, multimodal models, agents, RAG, optimization, and evaluation. It targets engineers and researchers seeking hands-on experience with LLM technologies, offering modular code and phased practices to deepen understanding.

How It Works

The project is structured into chapters corresponding to the book's content, with each chapter containing modular code projects. These projects implement key LLM concepts such as Transformer architecture, distributed training (data and pipeline parallelism), instruction tuning with LoRA, RLHF via PPO, multimodal alignment (MiniGPT-4), agent frameworks (LangChain), RAG, and efficiency optimizations. This phased, modular approach facilitates a step-by-step learning process.

Quick Start & Requirements

• Install: pip install -r requirements.txt
• Run: python main.py torchrun --nnodes 1 --nproc_per_node=4 tensor_parallel.py
• Prerequisites: Python 3.10+, PyTorch 2.3+ with CUDA 11.8, DeepSpeed 0.14+.
• Hardware: Minimum 1x NVIDIA A100 40GB GPU (for single-card inference), 64GB RAM, 1TB NVMe SSD. Linux (Ubuntu 22.04+ recommended) or Windows 11 WSL2.
• Docs: https://github.com/intro-llm/intro-llm-code

Highlighted Details

• Covers a comprehensive range of LLM topics from foundational theory to advanced applications.
• Includes practical implementations for distributed training, instruction tuning, and RLHF.
• Features modules for multimodal LLMs, LLM agents, and Retrieval Augmented Generation (RAG).
• Provides code for LLM efficiency optimization and evaluation methodologies.

Maintenance & Community

No specific information on contributors, sponsorships, or community channels (like Discord/Slack) is provided in the README.

Licensing & Compatibility

The repository does not explicitly state a license. Compatibility for commercial or closed-source use is not specified.

Limitations & Caveats

The project has significant hardware requirements, including a high-end GPU (NVIDIA A100 40GB) and substantial RAM/storage, which may limit accessibility for users without specialized hardware. The README does not specify the exact version of the book this code accompanies or if it's kept up-to-date with the latest LLM advancements.