What are Parameters in LLMs?

Parameters for LLMs: A Simple Explanation

November 9, 2023

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Large language models (LLMs) are a type of artificial intelligence that can generate and understand human language. They are trained on massive datasets of text and code, and they can be used for a variety of tasks, such as translation, summarization, and writing different kinds of creative content.

LLMs are complex systems with many different parameters. These parameters govern how the model learns and generates text. Some of the most important parameters for LLMs include:

• Model size: The model size is the number of parameters in the LLM. The more parameters a model has, the more complex it is and the more data it can process. However, larger models are also more computationally expensive to train and deploy.

• Training data: The training data is the dataset that the LLM is trained on. The quality and quantity of the training data has a significant impact on the performance of the model.

• Hyperparameters: Hyperparameters are settings that control how the LLM is trained. These settings can be fine-tuned to improve the performance of the model on specific tasks.

Here is a simple analogy to help you understand how LLM parameters work:

Imagine that you are training a dog to sit. You can think of the dog's behavior as the output of the model. The input to the model is your commands and rewards. The parameters of the model are the dog's experiences and memories.

As you train the dog, you are adjusting the parameters of the model. For example, if the dog doesn't sit when you command it, you might give it a treat when it finally does sit. This reward will reinforce the behavior and make it more likely that the dog will sit next time you give the command.

LLMs work in a similar way. The parameters of the model are adjusted during training to minimize the error between the predicted output and the actual output.

How to Choose the Right Parameters for Your LLM Model

The best parameters for your LLM model will depend on the specific task that you want to use it for. If you need a model that can generate text in a variety of different styles, then you will need a model with a large number of parameters. However, if you need a model that can perform a specific task, such as translation, then you may be able to get away with a smaller model.

It is also important to consider your computational resources when choosing the parameters for your LLM model. Larger models require more computational resources to train and deploy. If you are on a tight budget, then you may need to choose a smaller model.

What does it mean to have 70B parameters

When someone says that an LLM has 70B parameters, it means that the model has 70 billion adjustable parameters. These parameters are used to learn the relationship between words and phrases in the training data. The more parameters a model has, the more complex it can be and the more data it can process. However, larger models are also more computationally expensive to train and deploy.

70B parameters is a very large number, and it is one of the reasons why LLMs are so powerful. LLMs with 70B parameters can generate text that is indistinguishable from human-written text, and they can also perform complex tasks such as translation and summarization.

Here is a simple analogy to help you understand what 70B parameters means:

Imagine that you are building a house. The parameters of the house are the different features of the house, such as the number of rooms, the size of the rooms, and the layout of the house. The more parameters you have, the more complex the house can be.

LLMs are similar to houses. The parameters of the LLM are the different features of the language model, such as the ability to generate different types of text, the ability to translate languages, and the ability to summarize text. The more parameters an LLM has, the more complex it can be and the more tasks it can perform.

However, new models does not just rely on parameters but has better algorithms to improve/learn abilities at lower parameter value. We will talk about that in next post