How to build an LLM

Step 1: Choose a Model Architecture and Framework

Step 2: Prepare Your Training Dataset

Step 3: Implement Model and Training Loop

  • Framework: TensorFlow/Keras or PyTorch
  • Code Structure:
    • Define model architecture with chosen framework
    • Implement loss function (e.g., cross-entropy)
    • Choose optimizer (e.g., Adam)
    • Set up mini-batch training loop
  • Resources:

Step 4: Fine-tune and Evaluate

  • Training:
    • Monitor loss and adjust hyperparameters
    • Experiment with different learning rates and batch sizes
  • Evaluation:
    • Design test tasks for your LLM's functionality
    • Track performance metrics (e.g., accuracy, perplexity)

Step 5: Iterate and Improve

  • Experimentation:
    • Try different model architectures or hyperparameters
    • Explore diverse training data or techniques
  • Interpretability:
    • Understand model behavior using techniques like attention visualization
    • Address potential biases and limitations
  • Resources:

Additional Tips:

  • Utilize cloud platforms (Google Colab, Paperspace) for GPU/TPU access if needed.
  • Consult open-source LLM projects for inspiration and code examples.
  • Engage in online communities and forums for support and knowledge sharing.

See: How to build a large language model from scratch using python


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