An Architecture of GPT

Generative Pre-trained Transformer Architecture is a type of deep learning model that has been particularly effective for generative tasks like text generation, machine translation, and image generation. It's based on the Transformer architecture, which was originally introduced for sequence-to-sequence tasks like machine translation.

1. Input Embedding

• Input: The raw text input is tokenized into individual tokens (words or subwords).
• Embedding: Each token is converted into a dense vector representation using an embedding layer.

1. Positional Encoding: Since transformers do not inherently understand the order of tokens, positional encodings are added to the input embeddings to retain the sequence information.

2. Dropout Layer: A dropout layer is applied to the embeddings to prevent overfitting during training.

3. Transformer Blocks

• LayerNorm: Each transformer block starts with a layer normalization.
• Multi-Head Self-Attention: The core component, where the input passes through multiple attention heads.
• Add & Norm: The output of the attention mechanism is added back to the input (residual connection) and normalized again.
• Feed-Forward Network: A position-wise feed-forward network is applied, typically consisting of two linear transformations with a GeLU activation in between.
• Dropout: Dropout is applied to the feed-forward network output.

1. Layer Stack: The transformer blocks are stacked to form a deeper model, allowing the network to capture more complex patterns and dependencies in the input.

2. Final Layers

• LayerNorm: A final layer normalization is applied.
• Linear: The output is passed through a linear layer to map it to the vocabulary size.
• Softmax: A softmax layer is applied to produce the final probabilities for each token in the vocabulary.

How GPT Works:

• Input: The model receives an input sequence of tokens.
• Embedding: The tokens are converted into numerical representations (embeddings).
• Positional Encoding: Positional information is added to the embeddings.
• Decoding: The decoder generates the output sequence token by token, using self-attention to consider the previously generated tokens and the input sequence.
• Prediction: At each step, the model predicts the most likely next token based on the current context.

GPT models have been used for a variety of NLP tasks, including:

• Text Generation: Generating human-quality text, such as articles, poems, or scripts.
• Machine Translation: Translating text from one language to another.
• Question Answering: Answering questions based on a given text.
• Summarization: Summarizing long texts into shorter summaries.

The success of GPT models is largely due to their ability to capture long-range dependencies and generate coherent and informative text.