LLM scaling laws in Prompt Engineering / GenAI - Model Pipeline Trace

Model Pipeline - LLM scaling laws

This pipeline shows how increasing the size of a large language model (LLM) and its training data affects its learning performance. It tracks data preparation, model training with different sizes, and how loss improves as the model scales.

Data Flow - 6 Stages

1Raw Text Data

100 million tokens→Collect and clean text data from books, websites, and articles→100 million tokens

"The quick brown fox jumps over the lazy dog."

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2Tokenization

100 million tokens→Convert text into tokens (words or subwords)→100 million tokens

["The", "quick", "brown", "fox", "jumps", "over", "the", "lazy", "dog", "."]

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3Train/Test Split

100 million tokens→Split tokens into 90% training and 10% testing sets→90 million tokens (train), 10 million tokens (test)

Train: first 90 million tokens, Test: last 10 million tokens

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4Model Initialization

Model size varies (e.g., 100M, 1B, 10B parameters)→Create transformer model with specified number of parameters→Initialized model with given parameter count

Model with 1 billion parameters

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5Model Training

90 million tokens, model parameters→Train model on training tokens using gradient descent→Trained model with updated parameters

Model learns to predict next token accurately

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6Evaluation

10 million tokens, trained model→Calculate loss and accuracy on test tokens→Loss and accuracy metrics

Loss = 2.5, Accuracy = 40%

Training Trace - Epoch by Epoch

5.0 |***************
4.0 |**********
3.0 |*******
2.0 |****
1.0 |*
    +----------------
     1  5 10 15 20 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	5.0	10%	Model starts with high loss and low accuracy
5	3.2	25%	Loss decreases and accuracy improves as model learns
10	2.5	40%	Model shows steady improvement
15	2.1	50%	Loss continues to decrease, accuracy rises
20	1.9	55%	Training converges with better performance

Prediction Trace - 4 Layers

Layer 1: Input Embedding

Layer 2: Transformer Layers

Layer 3: Output Layer (Softmax)

Layer 4: Prediction

Model Quiz - 3 Questions

What happens to the loss as the LLM trains over epochs?

A Loss stays the same

B Loss decreases steadily

C Loss increases steadily

D Loss randomly jumps up and down

Key Insight

LLM scaling laws show that as we increase model size and training data, the model learns better, reducing loss and improving accuracy. This helps us understand how bigger models can perform more complex language tasks.