What is Grokking and Over-Fitting of an LLM

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AI Summary

Grocking and Grock Fast Summary

• Grocking:
  • A process where a model generalizes after overfitting to training data.
  • Initially observed in a two-layer Transformer.
  • Occurs across various architectures and data types (images, languages, graphs).
  • Impractical due to high computational costs.
• Overfitting:
  • A model is excessively trained on specific data, leading to poor performance on new data.
  • Results from a model learning noise instead of underlying patterns.
  • Limits real-world application due to lack of generalization.
• Grock Fast Algorithm:
  • Accelerates grocking by 50%.
  • Utilizes low pass filters: Gro Fast MA (Moving Average) or Gro Fast EMA (Exponential Moving Average).
  • Implemented with a few lines of code during the optimizer call in training.
  • Reduces training time, cost, and resources.
  • Effective across diverse tasks and data types.
• Implementation:
  • Download gro f.p from the provided repository.
  • Import Gro Fast helper function.
  • Insert specific lines of code before the training loop and between loss.backward and optimizer step.
  • Two options for implementation with arguments for training adjustments.
• Conclusion:
  • Grock Fast helps mitigate overfitting effects.
  • Enhances model training efficiency.
  • Encourages subscribing and sharing the channel for more content.