What are Active and Total Parameters in LLMs?2 min read·Just now--Active vs TotalWhen working with large language models (LLMs), you might come across terms like “active parameters” and “total parameters.” While they may sound technical, understanding the difference can give you valuable insights into how these models function.First, What Are Parameters?In simple terms, parameters are the internal values-like weights and biases-that a model learns during training. They’re what allow the model to make sense of input and generate output.What Are Total Parameters?Total parameters refer to all the weights and biases that a model learns during training. Think of these as the model’s memory: they store the information the model has absorbed from its training data. The more parameters a model has, the more nuanced and complex its understanding can be — like having a larger library of knowledge.What Are Active Parameters?Active parameters, on the other hand, are the subset of parameters that are currently being updated or fine-tuned. For instance, if you’re fine-tuning a pre-trained model on a specific task, you might only adjust certain layers or parameters while keeping the rest frozen. These adjustable parameters are your active parameters.Why Does This Distinction Matter?Understanding the difference is crucial for various reasons:1. Resource Efficiency: When finetuning a model, updating only a subset of parameters (the active ones) can be more computationally efficient and faster than retraining the entire model.2. Specialization: By focusing on a subset of parameters, you can tailor a general model to perform exceptionally well on a specific task without altering its broader knowledge base.3. Interpretability: Knowing which parameters are active can help researchers and developers better understand how and where the model is adapting to new information.A Simple AnalogyImagine you’re a chef with a vast collection of recipes (your total parameters). When you decide to specialize in a particular cuisine, you focus on refining a subset of those recipes (your active parameters) to perfection. This way, you become a master of that cuisine without forgetting everything else you know.Impact on the Inference PipelineWhen it comes to inference — using the model to generate predictions or responses — the distinction between active and total parameters fades away. During inference, the model uses all of its parameters — those meticulously learned during training — to make predictions. This means the entire set of parameters is engaged, ensuring the model leverages all its knowledge to deliver accurate and comprehensive results.ConclusionWhile total parameters represent the full capacity of a model, active parameters are like the fine-tuning knobs that help adapt it to specific tasks. Understanding this distinction can help you make informed decisions when working with or deploying these powerful models.