LLM_B2E: A Complete Learning Path to Master Large Language Models Systematically from Scratch

LLM_B2E is an open-source tutorial covering full-stack large language model (LLM) technologies. It provides a structured learning path with 19 core topics, including basic inference, pre-training, fine-tuning, alignment, long-text processing, etc. It is suitable for developers who want to systematically and deeply understand LLMs. Maintained by community developers, it adopts a progressive teaching approach to help learners gradually master LLM technologies from beginners to experts.

Large language model technology is evolving rapidly, but developers often feel overwhelmed by the numerous papers and code repositories. LLM_B2E (Large Language Models: From Beginner to Expert) was created to address this pain point, providing a structured learning path covering core aspects from basic inference to pre-training, fine-tuning, alignment, etc. Maintained by community developer jilan1990, it is broken down into 19 independent yet interconnected modules, suitable for Transformer beginners and researchers conducting in-depth studies.

LLM_B2E covers the entire lifecycle of LLM technologies and is divided into four major modules:

1. Basic Introduction Module: Model inference, basic pre-training practices, building an intuitive understanding of the workflow;
2. Core Technology Module: GPU memory management, data preparation, tokenizer design, word embedding mechanism, decoder layer details—these are the cornerstones for understanding architecture and optimization;
3. Training and Optimization Module: Supervised Fine-Tuning (SFT), Parameter-Efficient Fine-Tuning (PEFT), model alignment, including pre-training and inference practices for the LLaMA architecture;
4. Advanced Topic Module: Cutting-edge topics such as long-text processing and LLM-as-a-Judge, combined with application scenario thinking.

LLM_B2E emphasizes hands-on practice, with runnable code examples and step-by-step instructions in each chapter. It focuses on engineering details:

• GPU memory management: Explains training techniques under limited VRAM (gradient accumulation, mixed precision, model parallelism);
• Data preparation and Tokenizer design: Helps understand that "data determines the upper limit of the model", and teaches how to build high-quality datasets, design tokenization strategies, and handle noise and bias.

LLM_B2E bridges the gap between theory and application:

• Model alignment: Introduces technologies like RLHF to make model outputs align with human values;
• Long-text processing: Discusses engineering challenges such as positional encoding and context window expansion;
• LLM-as-a-Judge: Uses LLMs as automatic evaluation tools to solve the problem that traditional metrics struggle to capture semantic quality, which has been applied in mainstream evaluation systems.

Target Audience:

• Students/researchers: Build an overall understanding of the LLM field and lay the foundation for in-depth research;
• Algorithm engineers/developers: Engineering practice chapters and code can be directly applied to projects;
• Technical managers/product managers: Understand core components and trends to assist decision-making. Learning Suggestions: Read the preface and table of contents to build awareness, dive deep in chapter order, verify with code experiments, and combine practice with theory using classic papers.

LLM_B2E adopts an open-source model, embodying the spirit of knowledge sharing, lowering the learning threshold for LLMs, and allowing more people to access this world-changing technology. As LLMs are widely applied, mastering core technologies has become a competitive edge for AI practitioners. This project provides valuable resources for global learners, promoting industry knowledge popularization and technological progress.