Model optimization strategies

Most of the LLMs used nowadays, like GPT or Llama, are powered by a decoder-only Transformer architecture. The decoder-only architecture is designed for text-generation tasks. It predicts the next word in a sequence based on preceding words, making it effective for generating contextually appropriate text continuations.

In contrast, an encoder-only architecture, like BERT, focuses on understanding and representing the input text with detailed embeddings. It excels in tasks that require comprehensive context understanding, such as text classification and named entity recognition. Finally, the encoder-decoder architecture, like T5, combines both functionalities. The encoder processes the input text to generate a context-rich representation, which the decoder then uses to produce the output text. This dual structure is particularly powerful for sequence-to-sequence tasks like translation and summarization, where understanding the input context and generating...