PyTorch Zero-to-Hero: Mastering LLMs from Tensors to Deployment
As an expert AI and PyTorch engineer, this comprehensive tutorial takes developers from zero PyTorch knowledge to hero-level proficiency in building, training, fine-tuning, and deploying large language models (LLMs). You’ll discover why PyTorch dominates LLM research, master core concepts, implement practical code examples, and learn production-grade best practices with Hugging Face, DeepSpeed, and Accelerate.[1][5] Why PyTorch Leads LLM Research and Deployment PyTorch is the gold standard for LLM development due to its dynamic computation graph, which enables rapid experimentation—crucial for research where architectures evolve iteratively. Unlike static-graph frameworks, PyTorch’s eager execution mirrors Python’s flexibility, making debugging intuitive and prototyping lightning-fast.[5][6] ...