The learning rate is a scalar that determines the step size at each iteration while moving toward a minimum of the loss function, and its value depends on the optimizer chosen. The default for the most popular Adam optimizer is 0.001, but it can be adjusted to balance speed with stability. A learning rate scheduler dynamically adjusts the learning rate during fine-tuning, with common choices including linear decay and cosine annealing. Optimizers like AdamW are used for deep learning training and fine-tuning due to their simplicity, efficiency, and robustness, and come in various versions such as 32-bit, 8-bit, and paged. The batch size determines the number of training examples used in one iteration, with larger sizes leading to faster training but requiring more memory, while the number of epochs determines how many times the model sees the entire dataset during training. Warmup steps gradually increase the learning rate from a small value to the initial learning rate, and weight decay adds a penalty term to prevent overfitting by keeping weights small. Packing combines multiple small samples in one batch to increase efficiency, and hyperparameter tuning techniques like grid search, random search, and Bayesian optimization can be used to find optimal combinations.