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Low Rank Adaptation (LoRA) and QLoRA

Jul 23, 2024

Lecture on Low Rank Adaptation (LoRA) and QLoRA

Introduction

  • Speaker: Mark Hennings, Founder of Entrypoint AI
  • Topic: Parameter-efficient fine-tuning methods for large language models: LoRA & QLoRA (LoRA 2.0)

Importance of Fine-Tuning

  1. Pre-training
    • Involves processing a huge amount of text (~2 trillion tokens)
    • Model learns to predict the next word based on context
  2. Fine-tuning
    • After pre-training, the base model fine-tuned further for various tasks
    • Instruct tuning: E.g., generating chat models like ChatGPT
    • Safety tuning: Prevents inappropriate behaviors
    • Domain fine-tuning: Specialize a model in specific fields like law or finance

Challenges with Full Parameter Fine-Tuning

  • Updates all model weights (parameters)
  • Requires substantial memory and computational resources
  • Limited by hardware constraints

LoRA: Low Rank Adaptation

  1. Objectives
    • Solve memory and resource constraints during fine-tuning
  2. Method
    • Track Changes Instead of Updating Weights Directly
      • Use two smaller matrices to represent changes
      • These matrices get multiplied to form a matrix of the same size as the model’s weight matrix
      • Increases efficiency by reducing the number of trainable parameters
    • Matrix Decomposition
      • Smaller matrices are size 1 (rank 1); few numbers multiplied result in a larger matrix
      • Precision sacrificed for efficiency
      • Higher ranks (e.g., rank 512) still significantly reduce the number of trainable parameters compared to full model size (e.g., fine-tuning 86 million out of 7 billion parameters)

Choosing Rank

  • Determining the Right Rank
    • Low rank: Sufficient for most tasks, especially if the task is within the model's prior knowledge
    • Higher rank: Needed for complex behaviors or tasks contradicting model's initial training
  1. Empirical Results
    • Rank 8 to 256: Final performance isn’t widely affected
    • QLoRA: Even smaller memory usage by quantizing parameters while maintaining performance

QLoRA: Quantized LoRA

  1. Method
    • Parameters reduced to smaller bit sizes (e.g., from 16-bit to 4-bit)
    • Clever method to recover original precision by using normal distribution characteristics
  2. Advantages
    • Lesser memory usage compared to LoRA
    • Performance comparable to full-precision models

Best Practices

  1. Hyperparameters
    • Alpha: Scaling factor of weight changes (Variable by rank)
    • Dropout: Prevents overfitting
      • 10% for 7B & 13B models
      • 5% for 33B & 65B models
    • Learning rate & Batch size: Detailed in papers
  2. Implementation
    • Training all network layers essential for matching full-parameter fine-tuning performance
    • Consult empirical studies for hyperparameter tuning

Conclusion

  • LoRA and QLoRA provide efficient and effective methods for fine-tuning large language models
  • Explore Entrypoint AI for practical implementations

Full transcript