├── .gitignore ├── README.md ├── slides ├── Lecture 1 - Introduction and Logistics.pdf ├── Lecture 10 - Prompt Engineering and Inference Scaling.pdf ├── Lecture 11 - RAG and LLM Agent.pdf ├── Lecture 12 - PEFT and RL Alignment.pdf ├── Lecture 13 - LLM Evaluation.pdf ├── Lecture 14 - Final Review.pdf ├── Lecture 2 - Stochastic Gradient Descent.pdf ├── Lecture 3 - Automatic Differentiation.pdf ├── Lecture 4 - Nvidia GPU Computation and Communication.pdf ├── Lecture 5 - LLM Pretraining.pdf ├── Lecture 6 - Data- Pipeline- Optimizer- Parallel Training.pdf ├── Lecture 7 - Tensor Model- MoE- Sequence- Parallelel Training.pdf ├── Lecture 8 - Generative Inference Overview.pdf └── Lecture 9 - Generative Inference Optimization.pdf ├── topics.md └── topics_paper_allocation.md /.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store 2 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Relaxed-System-Lab/HKUST-COMP6211J-2025fall/HEAD/README.md -------------------------------------------------------------------------------- /slides/Lecture 1 - 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