LLM / SLM Parameters

What do we understand by LLM or SLM Parameters?

**Parameters** in deep learning, including language models, are adjustable values that control the behavior of neural networks. These parameters are learned during training and determine how the model processes input data.

In LLMs and SLMs, parameters typically include:

1. **Weight matrices**: These matrices contain the numerical values that are multiplied by input vectors to produce output activations.

2. **Bias terms**: These are additive constants added to the weighted sum of inputs to adjust the activation function's output.

3. **Learned embeddings**: These are fixed-size vector representations of words, phrases, or tokens learned during training.

The number and complexity of these parameters directly impact the model's performance, accuracy, and computational requirements. More parameters often allow for more nuanced learning and better representation of complex linguistic patterns, but also increase the risk of overfitting and computational costs.

In the context of LLMs, having **billions** of parameters means that the model has an enormous number of adjustable values, allowing it to capture subtle relationships between words, contexts, and meanings. This complexity enables LLMs to achieve impressive results in tasks like language translation, question answering, and text generation.

Conversely, SLMs typically have fewer parameters (often in the tens or hundreds of thousands), which makes them more efficient but also less capable of capturing complex linguistic patterns.