How to Implement a Decoder-Only Transformer in TensorFlow

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Large Language Models are all the rage! Remember ChatGPT, GPT-4, and Bard? These are just a few examples of these powerful tools, all powered by a special “brain” called the transformer. This design, introduced by Google in 2017, lets these bots predict the next word in a sentence, like a super fancy autocomplete. Not all language models use this tech, but big names like GPT-3, ChatGPT, GPT-4, and LaMDa rely on it to understand and respond to your prompts.

Decoder-only Transformers

Decoder-only transformer is a special type of neural network architecture used for tasks like text generation and translation. Unlike the standard Transformer model, which has both an encoder and a decoder, this version only uses the decoder component. Let’s break it down:

Traditional Transformer:

• Encoder: Processes an input sequence (e.g., a sentence) to capture its meaning.
• Decoder: Uses the encoded information to generate a new output sequence (e.g., a translated sentence).

Decoder-only Transformer:

• No Encoder: No information about the original input sequence is explicitly provided.
• Masked Self-Attention: Used to process the previously generated sequence, allowing the…