Hyperparameters in large language models (LLMs) are settings that control how the model is trained and behaves during inference. Unlike model parameters (e.g., neural network weights), hyperparameters are not learned from data but are set manually before training. They directly influence training efficiency, model performance, and computational resource usage. For example, a poorly chosen learning rate might prevent the model from converging, while an optimal batch size can balance memory usage and gradient accuracy. Hyperparameters also affect the model’s ability to generalize to new data, making them critical for achieving high-quality results.
Key hyperparameters include the learning rate, batch size, number of training epochs, and model architecture choices like the number of layers or attention heads. The learning rate determines how much the model adjusts its weights in response to errors during training. A rate that’s too high can cause instability, while one that’s too low slows progress. Batch size affects memory requirements and gradient estimation—smaller batches introduce noise but update weights more frequently, while larger batches provide smoother gradients at the cost of higher memory use. Architectural hyperparameters, such as the number of transformer layers, dictate the model’s capacity to capture patterns. For instance, GPT-3 uses 96 layers, enabling complex reasoning but requiring significant computational power.
Tuning hyperparameters is often an empirical process. Developers might use grid search, random search, or automated tools like Bayesian optimization to find combinations that maximize validation accuracy. For inference, hyperparameters like temperature (controlling output randomness) or top-p sampling (limiting token selection) shape generated text. For example, setting a low temperature (e.g., 0.2) produces more predictable outputs, while a higher value (e.g., 1.0) encourages creativity. Balancing these settings requires understanding the trade-offs between coherence and diversity. Ultimately, hyperparameter choices are foundational to aligning LLMs with specific use cases, whether generating code, answering questions, or summarizing text.