Fine-Tuning Progress: Monitoring Loss, Entropy, and Training Metrics

When monitoring a model training run, keeping an eye on the core metrics helps ensure the optimization process is stabilizing correctly. In our current session running inside Jupyter, we are tracking several key indicators:

• Loss & Evaluation Loss: Observing a steady progression through the epochs to ensure the model is generalizing well and minimizing error without overfitting.

• Gradient Norm (grad_norm): Tracking gradient magnitudes to keep stability checks in place and prevent exploding or vanishing gradients during backpropagation.

• Learning Rate Schedule: Utilizing dynamic learning rate adjustments to fine-tune convergence velocity as the training steps advance.

• Token Accuracy (mean_token_accuracy): Maintaining high token-level precision across sequential training phases.

  
{

   "loss": "8.0115",
   "grad_norm": "0.8307",
   "learning_rate": "7.822e-06",
   "entropy": "8.0283",
   "epoch": "3.481"
}

Above: A sample snapshot of the active training log metrics.