Pre-training and fine-tuning concept in Prompt Engineering / GenAI - Model Pipeline Trace

Model Pipeline - Pre-training and fine-tuning concept

This pipeline shows how a large model first learns general knowledge from a big dataset (pre-training), then adapts to a specific task with a smaller dataset (fine-tuning).

Data Flow - 5 Stages

1Raw Data Collection

N/A→Gather large diverse text data→1000000 samples x variable length text

"The sun rises in the east."

↓

2Preprocessing

1000000 samples x variable length text→Clean text, tokenize into words or subwords→1000000 samples x 50 tokens

["The", "sun", "rises", "in", "the", "east", "."]

↓

3Pre-training

1000000 samples x 50 tokens→Train large model to predict missing words→Model with learned general language patterns

Model predicts missing word 'east' in sentence

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4Fine-tuning Data Preparation

10000 samples x 50 tokens→Prepare smaller task-specific dataset→10000 samples x 50 tokens

"Is this review positive?" with label "Yes"

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5Fine-tuning

10000 samples x 50 tokens→Train model further on task data→Model adapted to specific task

Model learns to classify sentiment correctly

Training Trace - Epoch by Epoch

Loss
2.3 |*       
1.5 |  *     
0.9 |    *   
0.6 |      * 
0.5 |       *
0.3 |        *
     ----------------
     1  5  10 15 16 20
     Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	2.3	0.10	High loss, low accuracy as model starts learning
5	1.5	0.45	Loss decreasing, accuracy improving steadily
10	0.9	0.75	Model captures general language patterns well
15	0.6	0.85	Pre-training converging with good accuracy
16	0.5	0.88	Fine-tuning starts on task-specific data
20	0.3	0.95	Fine-tuning improves task accuracy significantly

Prediction Trace - 5 Layers

Layer 1: Input Tokenization

Layer 2: Embedding Layer

Layer 3: Pre-trained Transformer Layers

Layer 4: Fine-tuned Classification Head

Layer 5: Output Decision

Model Quiz - 3 Questions

What is the main purpose of pre-training in this pipeline?

A Learn general language patterns from large data

B Train on small task-specific dataset

C Clean and tokenize the text data

D Make final predictions on new sentences

Key Insight

Pre-training helps the model learn broad knowledge from large data, making it ready to quickly adapt during fine-tuning on smaller, specific tasks. This two-step approach saves time and improves accuracy.