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🧠 Key Elements of Training a Foundation Model

Training a foundation model involves three main stages:

1. Pre-training​

  • Purpose: Teach the model general capabilities and understanding of human language and multimodal data.
  • Method:
    • Uses self-supervised learning
    • Trained on vast amounts of unstructured data (text, images, audio, etc.)
  • Requirements:
    • Trillions of tokens
    • Petabytes of data
    • Millions of GPU compute hours
    • Significant trial and error
  • Outcome:
    The model learns fundamental patterns and representations but is not specialized for specific tasks or domains.

2. Fine-tuning​

  • Purpose: Adapt the pre-trained model to specific tasks or domains.
  • Method: Uses supervised learning with labeled datasets to update the model's weights.
  • Key Variants: Instruction tuning, domain adaptation, parameter-efficient fine-tuning (PEFT)
  • Consideration: Risk of catastrophic forgetting when training on single tasks.

3. Continuous Pre-training​

  • Purpose: Extend the model’s training with new general data to improve generalization or keep it up to date.
  • Method:
    Continues the original pre-training process, often on a broader or refreshed dataset.
  • Benefit:
    Enhances the model’s foundational knowledge without focusing on a specific task or domain.

Each stage builds upon the previous one — from general understanding (Pre-training) to task specialization (Fine-tuning) to refinement and updates (Continuous Pre-training).