LoRA and QLoRA concepts in Prompt Engineering / GenAI - Model Pipeline Trace

Model Pipeline - LoRA and QLoRA concepts

This pipeline shows how LoRA and QLoRA help train large AI models efficiently by reducing the number of trainable parameters and using quantization to save memory. It makes training faster and cheaper while keeping good accuracy.

Data Flow - 5 Stages

1Original Model Parameters

100 million parameters→Start with a large pretrained model→100 million parameters

A big language model with 100M weights

↓

2Apply LoRA

100 million parameters→Add low-rank matrices to model layers to reduce trainable parameters→100 million parameters (only ~1 million trainable)

Instead of updating all weights, only update small low-rank matrices

↓

3Quantize Model (QLoRA)

100 million parameters→Convert model weights to 4-bit precision to save memory→100 million parameters in 4-bit format

Weights stored with fewer bits, reducing memory use by ~4x

↓

4Train with LoRA + QLoRA

100 million parameters (4-bit), ~1 million trainable→Train only low-rank matrices on quantized model→Updated low-rank matrices, quantized model

Fine-tune model efficiently with less memory and compute

↓

5Make Predictions

New input text→Use fine-tuned quantized model with LoRA updates to predict→Predicted output text

Model generates text based on learned patterns

Training Trace - Epoch by Epoch

Loss
0.5 |****
0.4 |***
0.3 |**
0.2 |*
0.1 | 
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.45	0.60	Initial training with LoRA on quantized model starts with moderate loss and accuracy
2	0.30	0.75	Loss decreases and accuracy improves as low-rank matrices learn useful updates
3	0.20	0.85	Training converges well with efficient parameter updates
4	0.15	0.90	Further fine-tuning improves accuracy with stable loss
5	0.12	0.92	Training stabilizes with good accuracy and low loss

Prediction Trace - 5 Layers

Layer 1: Input Token Embedding

Layer 2: LoRA Low-Rank Update

Layer 3: Quantized Model Forward Pass

Layer 4: Output Layer with Softmax

Layer 5: Prediction

Model Quiz - 3 Questions

What is the main benefit of using LoRA in training large models?

A Convert model weights to 8-bit precision

B Increase the total number of model parameters

C Only update a small number of parameters to save compute

D Remove layers from the model

Key Insight

LoRA and QLoRA together allow training very large models efficiently by updating only small low-rank matrices and using low-bit quantization to reduce memory. This keeps training fast and affordable while maintaining good accuracy.