Programming Ocean Academy

A Typical Large Language Model (LLM)

Understand the intricate workings of LLMs in an interactive and visually appealing way.

A Typical Large Language Model (LLM)

How LLMs Work

  • Input Embedding: Converts text tokens into dense vector representations for processing.
  • Positional Encoding: Adds positional information to embeddings to provide sequence context.
  • Transformer Encoder: - Multi-Head Attention: Captures relationships between tokens in the input sequence. - Add & Norm: Ensures stability during processing. - Feed Forward: Applies a fully connected layer to transform the data.
  • Transformer Decoder: - Masked Multi-Head Attention: Focuses on prior tokens during training for causal prediction. - Multi-Head Attention: Combines context from the encoder and decoder layers. - Add & Norm & Feed Forward: Enhances stability and feature extraction.
  • Output Layer: Maps decoder outputs to probabilities for each token using a Softmax function.
  • Applications: Powers tasks like text generation, translation, summarization, and conversational AI.

LLM Code Example

Here's how we can define the layers of a simplified Large Language Model:


import tensorflow as tf
from tensorflow.keras import layers

model = tf.keras.Sequential([
    layers.Input(shape=(None, 512)),  # Input Layer (sequence length = None, features = 512)
    layers.MultiHeadAttention(num_heads=8, key_dim=64),  # Self-Attention Layer
    layers.Dense(2048, activation="relu"),  # Feedforward Network Layer 1
    layers.Dense(512, activation="relu"),  # Feedforward Network Layer 2
    layers.Dense(10000, activation="softmax")  # Output Layer (vocabulary size = 10,000)
])