Have you built any large language model (LLM) architecture? If not, do you have an understanding of it?

Large language models (LLMs) are built on the transformer architecture, which uses self‑attention to capture long‑range dependencies in text. An LLM typically consists of an embedding layer, multiple transformer blocks (each with multi‑head self‑attention and feed‑forward sub‑layers), and a final linear layer that maps hidden states to vocabulary logits. The model is trained with a causal language modeling objective, predicting the next token given all previous tokens. Scaling up an LLM involves increasing the number of layers, hidden size, and attention heads, as well as training on massive corpora. During training, techniques such as mixed‑precision, gradient checkpointing, and distributed data parallelism are used to manage memory and speed. After pre‑training, the model can be fine‑tuned on downstream tasks by adding task‑specific heads or adapters. Deployment often requires model compression (pruning, quantization) or knowledge distillation to fit resource constraints. Understanding LLM architecture also means being aware of tokenization (e.g., Byte‑Pair Encoding or SentencePiece), positional encodings, and the trade‑offs between model size, latency, and accuracy. In practice, building an LLM from scratch is rarely feasible; instead, engineers fine‑tune open‑source checkpoints (like GPT‑2, GPT‑3, or LLaMA) to meet specific business needs.