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LLM Parameters and Hyperparameters Explained
Introduction:
Large Language Models (LLMs) are complex neural networks trained on vast amounts of data. Their performance relies on a combination of parameters (learned during training) and hyperparameters (defined by the user). In this blog, we will explore the key parameters and hyperparameters in LLMs, explaining their roles and how adjusting them can lead to better results.
1. Model Parameters
Parameters are values learned by the model during training. They are often part of the model’s architecture and help make predictions based on the input data. In LLMs, model parameters include:
- Weights and Biases: These are adjusted during training to minimize the error between the predicted output and the actual label.
- Number of Layers: Determines the depth of the model (e.g., how many stacked layers of neural networks).
- Example: GPT-3 has 96 layers.
2. Hyperparameters
Hyperparameters are configurations set before training, affecting how the model learns. Below, we list key hyperparameters and explain how they influence the model’s performance:
