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LLM Training Masterclass | Build SOTA Models

LLM Training Masterclass | Build SOTA Models








[ LLM TRAINING MASTERCLASS ]

Build State-of-the-Art Language Models | From Theory to Production








INTRO
ARCHITECTURE
DATASETS
TRAINING
OPTIMIZATION
EVALUATION
PAPERS
IMPLEMENTATION
DEPLOYMENT






[ SYSTEM OVERVIEW ]

this is your complete guide to building state-of-the-art large language models. from foundational transformer architecture to deployment at scale. everything you need to go from research papers to production models.


what you'll master


transformer architecture internals (attention, ffn, normalization)

dataset curation and preprocessing at scale

pre-training, instruction tuning, RLHF, and DPO

scaling laws and compute-optimal training (chinchilla)

distributed training (FSDP, DeepSpeed ZeRO)

mixed precision, quantization, LoRA

evaluation frameworks and benchmarks

production deployment strategies



key resources


HuggingFace Transformers Docs

ArXiv cs.CL (Computation & Language)

Awesome-LLM GitHub

DeepSpeed Documentation




RECOMMENDED PATH: follow modules in order. each builds on previous concepts. start small (1-7B params), reproduce papers, then scale up.…