Model optimization (distillation, quantization) in NLP - Model Pipeline Trace

Model Pipeline - Model optimization (distillation, quantization)

This pipeline shows how a large language model is made smaller and faster using two techniques: distillation and quantization. Distillation teaches a small model to copy a big model's knowledge. Quantization makes the model use fewer bits to store numbers, saving space and speeding up predictions.

Data Flow - 5 Stages

1Original dataset

10000 sentences x 50 tokens→Raw text data for training→10000 sentences x 50 tokens

"The cat sat on the mat."

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2Teacher model training

10000 sentences x 50 tokens→Train large model on dataset→Model with 110M parameters

Large transformer model trained to predict next words

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3Distillation data preparation

10000 sentences x 50 tokens→Generate soft labels (probabilities) from teacher→10000 sentences x 50 tokens with soft labels

Teacher outputs: {word1:0.7, word2:0.2, word3:0.1}

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4Student model training

10000 sentences x 50 tokens with soft labels→Train smaller model to mimic teacher outputs→Model with 10M parameters

Smaller transformer learns to predict teacher's soft labels

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5Quantization

Model with 10M parameters (float32)→Convert weights from 32-bit floats to 8-bit integers→Model with 10M parameters (int8)

Weights stored with less memory, faster computation

Training Trace - Epoch by Epoch

Loss
2.5 |****
2.0 |*** 
1.5 |**  
1.0 |*   
0.5 |    
    +------------
     1 3 5 7 10 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	2.3	0.30	Student model starts learning from teacher's soft labels
3	1.5	0.55	Loss decreases steadily, accuracy improves
5	1.0	0.70	Student model closely mimics teacher outputs
7	0.8	0.78	Training converges with good accuracy
10	0.7	0.82	Final student model ready for quantization

Prediction Trace - 4 Layers

Layer 1: Input token embedding

Layer 2: Student model forward pass

Layer 3: Softmax activation

Layer 4: Quantized model inference

Model Quiz - 3 Questions

What is the main goal of distillation in this pipeline?

A To train a smaller model to copy a larger model's knowledge

B To convert model weights to smaller numbers

C To increase the size of the model

D To add more training data

Key Insight

Model optimization through distillation and quantization helps create smaller, faster models that keep much of the original model's knowledge. Distillation transfers knowledge from a big model to a small one, while quantization reduces memory and speeds up predictions by using fewer bits.