Training

Overview

Training an LLM like GPT-2 for fine-tuning involves several steps. Let’s look at the general process for training the model:

• Initializing a pre-trained model: A pre-trained model, such as GPT-2, has a wide range of language patterns, and we’ll now look to improve its performance on a specific task.

• Defining the hyperparameters:

  • Learning rate: This determines how much the model’s weights should be updated during training. A higher rate speeds up learning but can overshoot optimal solutions.

  • Batch size: This is the number of training examples used in one iteration. A larger batch size provides a more accurate estimate of the gradient but requires more memory.

  • Epochs: One epoch is when the entire dataset is passed forward and backward through the neural network once. More epochs mean more iterations over the entire dataset.

• Setting up the optimizer and learning rate scheduler:

  • We use an optimizer to adjust the model’s parameters to minimize the loss function, guiding the model to learn effectively from the training data.

  • We employ a learning rate scheduler that starts with a warmup phase, gradually increasing the learning rate, followed by a linear decay.

• Training process: A typical training process will include the following operations.

  • Batch processing: We divide the training data into smaller sets known as batches.

    • For each batch, we process and pack data into tensors. Tensors are multidimensional arrays used by neural networks to process and store data.

    • We move tensors to the GPU for processing.

    • We perform a forward pass where the model makes predictions and calculates loss.

    • We perform backpropagation, which is a fundamental process in neural network training where gradients (partial derivatives) of the loss function are computed with respect to each model parameter. These gradients are used to update the model’s parameters to minimize loss.

  • Gradient accumulation: We accumulate gradients over several steps before updating model weights. This simulates larger batches and is useful when working with limited memory resources.

  • Weight update:

    • After accumulating gradients over several steps equivalent to the specified batch size, we update the model weights using the optimizer.

    • We reset the gradients to zero after each update to prevent accumulation across batches.

We’ll be fine-tuning GPT-2 for lyrics generation.