Attention allows tokens to focus on relevant parts of the sequence, regardless of distance.
Most generative large language models utilize a Decoder-only Transformer structure. Unlike the original encoder-decoder setup designed for translation, a decoder-only model predicts the next token in a sequence based strictly on the preceding tokens. Tokenization and Embedding Build A Large Language Model -from Scratch- Pdf -2021
The input prompt is tokenized and passed through the model. Attention allows tokens to focus on relevant parts
Unlike RNNs, Transformers process tokens in parallel. Positional encodings must be added to embeddings to give the model information about the order of words in a sentence. D. The Transformer Block Tokenization and Embedding The input prompt is tokenized
The Scaled Dot-Product Attention is the heart of the model. It computes:
Developed by Microsoft, ZeRO shards optimizer states, gradients, and model parameters across data-parallel nodes, paving the way for training massive systems without massive infrastructure. Summary of 2021 Reference Architecture
You cannot build an LLM on a single GPU in 2021. A "from scratch" PDF implicitly required you to learn distributed computing.