Transformer architecture overview in Prompt Engineering / GenAI - Model Pipeline Trace

Model Pipeline - Transformer architecture overview

The Transformer model processes input data by first converting words into numbers, then learning relationships between words using attention. It trains by adjusting to reduce errors and finally predicts outputs like translated sentences or answers.

Data Flow - 6 Stages

1Input tokens

1 sentence x 10 words→Convert words to token IDs using vocabulary→1 sentence x 10 tokens

["I", "love", "cats"] -> [101, 2023, 1234]

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2Embedding layer

1 sentence x 10 tokens→Map tokens to vectors of size 512→1 sentence x 10 tokens x 512 features

[101, 2023, 1234] -> [[0.1, 0.3, ...], [0.5, 0.2, ...], [0.4, 0.7, ...]]

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3Positional encoding

1 sentence x 10 tokens x 512 features→Add position info to embeddings→1 sentence x 10 tokens x 512 features

Embedding vector + position vector for each token

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4Multi-head self-attention

1 sentence x 10 tokens x 512 features→Calculate attention scores and weighted sums→1 sentence x 10 tokens x 512 features

Each token attends to others to gather context

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5Feed-forward network

1 sentence x 10 tokens x 512 features→Apply two linear layers with ReLU in between→1 sentence x 10 tokens x 512 features

Transform features to capture complex patterns

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6Output layer

1 sentence x 10 tokens x 512 features→Project to vocabulary size and apply softmax→1 sentence x 10 tokens x 10000 classes

Predict probability for each word in vocabulary

Training Trace - Epoch by Epoch

Epoch 1: *****
Epoch 2: ****
Epoch 3: ***
Epoch 4: **
Epoch 5: *
Epoch 6: *
(Loss decreasing over epochs)

Epoch	Loss ↓	Accuracy ↑	Observation
1	5.2	0.12	High loss and low accuracy at start
2	3.8	0.35	Loss decreased, accuracy improved
3	2.7	0.52	Model learning meaningful patterns
4	1.9	0.68	Good progress, loss dropping steadily
5	1.3	0.78	Model converging with better accuracy
6	0.9	0.85	Loss low, accuracy high, training stable

Prediction Trace - 5 Layers

Layer 1: Tokenization

Layer 2: Embedding + Positional Encoding

Layer 3: Multi-head Self-Attention

Layer 4: Feed-forward Network

Layer 5: Output Projection + Softmax

Model Quiz - 3 Questions

What does the embedding layer do in the Transformer?

A Splits sentences into words

B Converts tokens into vectors with meaning

C Calculates attention scores

D Applies softmax to output

Key Insight

The Transformer uses attention to understand relationships between words, allowing it to learn context effectively. Training reduces errors steadily, improving prediction accuracy.