Explain LLM tuning and transformer basics

Respond to the machine learning questions below:

• Walk through a project in which you fine-tuned a large language model or another large foundation model. Cover the task, how the dataset was built, the labeling approach, the adaptation method (full fine-tuning versus parameter-efficient tuning), the loss function, evaluation metrics, deployment constraints, and how you would handle overfitting or hallucination.
• Explain regularization and contrast common variants, including L1, L2/weight decay, dropout, early stopping, and data augmentation.
• Compare widely used optimizers such as SGD, Momentum, Adam, and AdamW, and describe when each would be appropriate.
• Explain self-attention and multi-head attention, along with the key equations.
• Describe the Transformer architecture from end to end and write out the principal mathematical steps in a decoder-style Transformer block. Overview: This question assesses skills in tuning large language models and in Transformer fundamentals, spanning fine-tuning strategies, dataset construction and labeling, model adaptation choices, loss functions and evaluation metrics, regularization techniques, optimizer selection, self-attention and multi-head attention, and the end-to-end mathematical steps of a Transformer decoder. It appears often in Machine Learning interviews because it tests both conceptual understanding and practical application—looking at architectural trade-offs, optimization and regularization decisions, deployment constraints, and reasoning about failure modes such as overfitting and hallucination in the Machine Learning domain.