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Show Notes
Large Language Models (LLMs) through a question-and-answer format, covering fundamental concepts and advanced techniques. It explains tokenization, LoRA/QLoRA, beam search, and temperature, along with masked language modeling and sequence-to-sequence models. The text further explores model training methodologies, including autoregressive versus masked models, embeddings, next sentence prediction, and sampling strategies. It discusses prompt engineering, catastrophic forgetting mitigation, model distillation, and handling out-of-vocabulary words. Finally, the resource highlights advanced topics such as attention mechanisms, optimization techniques, and the challenges associated with using LLMs, including bias, computational cost and resources.