Understanding how the LLM model works?

This article dives into the backend workings of a Large Language Model (LLM) on a Transformer architecture like GPT and explains how it processes and generates text. Based on my understanding and learning, I’ve kept the concepts simple and straightforward, ensuring that they’re easy to grasp for anyone curious about how these models work.

By breaking the process into clear steps, we’ll explore everything from tokenization to decoding and detokenization, shedding light on the fascinating mechanisms that power LLMs. Let's begin!

1. Input Processing (Tokenization)

LLMs don’t directly process raw text like “The bird ate the worm.” Instead, they break it down into smaller units called tokens. A token could represent:

• A single word (e.g., “bird”).
• A subword (e.g., “bir” and “d”).
• Even a single character or punctuation mark.

Why Tokenization? Tokenization standardizes input, ensuring the model can handle any sentence structure or unfamiliar words. For example:

• Sentence: “The bird ate the worm.”
• Tokens: [The, bird, ate, the, worm]

?While tokens don’t always directly correspond to individual words, for simplicity, we can assume each word in a sentence represents a token in this example. Each token is converted into a unique numerical ID in the model’s vocabulary (e.g., [142, 5123, 85, 142, 4325]), which becomes the input that the model processes. This allows the model to handle everything from short sentences to complex paragraphs.

2. Embedding Tokens

Once tokens are identified, the model converts them into embedding which are nothing but a coordinates in a high-dimensional vectors (e.g., [0.2, -0.1, 0.8, …]) and which gives the relative meaning of each tokens to the model for the further process. OpenAI's GPT-3 model reportedly has an embedding dimensional size of?12,288, ?a scale far beyond what the human mind can intuitively grasp.

What Happens Here?

• Embeddings capture relationships between words. For instance, “bird” and “sparrow” might have similar embeddings because they share meaning.
• Words with different meanings in different contexts (e.g., “bank” as a financial institution vs. a riverbank) are handled dynamically by the model as it processes the sentence.

This embedding step ensures the model "understands" the meaning of each token, not just its surface form and also that the model begins with a rich, meaningful representation of each token.

3. Transformer Layers: The Brain of the Model

This is where the magic happens. The tokens and their embeddings are processed through multiple transformer layers (e.g., 12 layers in GPT-2 Small, 96 in GPT-3). Each layer refines the model’s understanding by capturing relationships and patterns between words.

Each transformer layer has two main components:

a) Self-Attention

• Self-attention allows each word to “look at” all other words in the sentence and figure out which ones are most relevant.
• For example, in “The bird ate the worm,” the word “ate” focuses on “bird” (the subject) and “worm” (the object) to understand the context of the action.

b) Feedforward Neural Network

• After self-attention, each word’s meaning is further refined through a small neural network that adjusts its representation.
• This step captures complex relationships, like grammatical roles or abstract meanings.

Layer Stacking:

• Early Layers focus on simple patterns (e.g., direct relationships like subject and object).
• Middle Layers build an understanding of sentence structure and grammar.
• Deeper Layers capture abstract meanings, tone, and intent.

By stacking multiple layers, the model builds a rich, nuanced understanding of the entire sentence. Transformers use parallel processing, meaning that computations within each layer happen simultaneously and which makes sense on why the model needs high speed GPUs to handle this heavy lifting computation processing.

4. Final Output Layer (Decoding)

After processing through all transformer layers, each word’s refined vector representation is mapped to the model’s vocabulary (e.g., 50,257 possible tokens). This step involves two parts:

a) Linear Transformation and Softmax

• Each word’s vector is transformed into a score for every possible word in the vocabulary.
• A softmax function converts these scores into probabilities, representing how likely each word is to come next.

b) Probability Example

For the input “The bird ate,” the model might predict:

• “the” (50% probability)
• “worm” (40% probability)
• “quickly” (10% probability)

5. Decoding (Choosing the Next Word)

Once probabilities are calculated, the model needs to pick the next word. Different strategies determine how the word is selected:

Decoding Strategies:

• Greedy Decoding: Selects the word with the highest probability (e.g., “worm”).
• Top-p (Nucleus) Sampling: Selects from the smallest set of words whose combined probability exceeds a threshold (e.g., 90%). This adds variety while keeping coherence.
• Top-k Sampling: Limits choices to the top kkk most likely words, adding controlled randomness.
• Temperature Adjustment: Controls the “creativity” of the output. Lower values (e.g., 0.2) make responses more deterministic, while higher values (e.g., 0.8) introduce more randomness.

Iterative Process:

Once a word is chosen, it’s added to the sequence, and the process repeats:

• Input: “The bird ate”
• Output: “The bird ate the”
• Input (updated): “The bird ate the”
• Output: “The bird ate the worm”

This continues until a stopping condition is met (e.g., end of sentence). In ChatGPT models, including GPT-3.5 and GPT-4, the primary decoding strategy used is a form of Top-p (Nucleus) Sampling.

6. Detokenization

Finally, the tokens generated by the model (e.g., [The, bird, ate, the, worm]) are converted back into a human-readable string:

• Tokens: [The, bird, ate, the, worm]
• Detokenized Output: “The bird ate the worm.”

This step ensures the output is smooth, readable, and natural.

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Summarize:

·???????? Tokenization breaks text into manageable units for processing.

·???????? Embedding Tokens captures the meaning of each token in a high-dimensional space.

·???????? Transformer Layers progressively refine understanding using self-attention and feedforward networks.

·???????? Final Output Layer maps refined token representations to probabilities for the next word.

·???????? Decoding selects the next word based on probabilities and a chosen strategy.

·???????? Detokenization converts tokens back into human-readable text.

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