This extensive tutorial provides a systematic introduction to large language model technologies, organized into seven core modules. The pre-training section covers model frameworks, optimizers (SGD, Momentum, RMSprop), activation functions, attention mechanisms, position encoding, tokenizers, and distributed training strategies.
Deployment and inference sections explain private deployment options, DevOps/AIOps/MLOps/LLMOps concepts, and inference frameworks including vLLM. The fine-tuning module explores prompt tuning, prefix tuning, P-Tuning, multi-task prompt tuning, and LoRA series techniques including QLoRA and AdaLoRA, plus frameworks like LLaMA-Factory.
Advanced topics include model quantization, memory optimization, prompt engineering, Agent design with MCP and LangChain, RAG implementation with vector databases and GraphRAG, and evaluation metrics for real-world applications. The tutorial concludes with mathematical foundations including linear algebra, calculus, and probability theory relevant to AI development.[citation:2]