llm-action is a knowledge repository that shares large language model technical principles and practical engineering experience.
The project addresses the need for comprehensive, organized learning resources covering the full lifecycle of large language model development and deployment. It organizes content across training, inference, compression, evaluation, data engineering, and application development, providing both theoretical foundations and hands-on implementation guidance. The repository serves as a centralized reference that connects multiple aspects of LLM engineering, from foundational concepts through production optimization.
This resource suits practitioners building or deploying large language models who need structured access to techniques spanning the entire workflow. It is particularly valuable for engineers working on parameter-efficient fine-tuning methods like LoRA and QLoRA, distributed training strategies, inference optimization, model compression through quantization and pruning, and reinforcement learning from human feedback. The breadth of coverage makes it useful for teams navigating the full spectrum of LLM engineering challenges rather than those seeking depth in a single specialized area.
The project maintains an extensive, well-organized collection of topics with clear categorization across training methodologies, inference frameworks, compression techniques, evaluation approaches, and operational infrastructure. Content spans from distributed training parallelization and network communication through LLMOps, model evaluation, and prompt engineering. The repository includes practical implementation tutorials covering model scales from six billion to sixty-five billion parameters and various fine-tuning approaches, alongside foundational material on algorithm architectures and interview preparation resources.
