LLM Algorithm Practice Lab

This repository offers a practical LLM algorithm practice lab designed for individuals from beginners to advanced learners, including job seekers and AI practitioners. It bridges the gap between theoretical knowledge and practical implementation by providing runnable, verifiable Jupyter Notebook exercises, enabling users to move from understanding to writing, debugging, and optimizing LLM algorithms.

How It Works

The project transforms LLM concepts into hands-on, test-driven Jupyter Notebook exercises. It focuses on core LLM areas like Transformers, MoE, quantization, inference acceleration, and VRAM optimization. Core operators and system logic are implemented using PyTorch, Triton, or native CUDA C++, with each exercise accompanied by local test cases and performance validation for a repeatable learning path.

Quick Start & Requirements

• Online Reading: Access the interactive tutorial at https://datawhalechina.github.io/llm-algo-leetcode/.
• Local Development: Clone the repository, create and activate a Conda environment using environment.yml, and launch Jupyter Lab.
• Dependencies: Requires Python, deep learning fundamentals, and PyTorch. Advanced sections necessitate C++/CUDA knowledge. Additional GPU/Triton dependencies can be installed via requirements/gpu.txt.

Highlighted Details

• Highly Vertical Focus: Concentrates on Transformers, Mixture-of-Experts (MoE), quantization, inference acceleration, and VRAM optimization within LLMs.
• Engineering-Oriented Implementation: Utilizes PyTorch, Triton, or native CUDA C++ for core operators and system logic.
• Test-Driven Learning: Each exercise includes industrial-aligned tests and performance validation.
• Comprehensive Coverage: Progresses from fundamental prerequisites and system-level theory to PyTorch-based algorithm practice and low-level CUDA C++/Triton kernel development.

Maintenance & Community

The project is currently in a beta public testing phase, with ongoing refinement of documentation and comments. Recent updates (June 2026) focus on content organization and integrating GitHub Discussions for feedback. lynn_jingjing is listed as the project initiator.

Licensing & Compatibility

The project is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. This license restricts usage to non-commercial purposes.

Limitations & Caveats

This is a beta release, with ongoing documentation and comment optimization. The scope is strictly limited to the Large Language Model (LLM) domain, excluding Diffusion and multimodal content. While core sections are marked as complete, further expansion is planned for all parts.