Ever wondered how ChatGPT can discuss philosophy one moment and write code the next? Or how DALL-E creates stunning images from mere text descriptions? The answer lies in a groundbreaking AI technology that's changing the game: foundation models. I'm Abdul Samad, known in tech circles as samadpls, and I'm here to unveil the secret behind these AI powerhouses. In this article, we'll explore what foundation models are, why they're so versatile, and how they're driving the most impressive AI applications we see today. Let's uncover the driving force behind the AI revolution and confidently explore the potential of these hidden giants.

So What Are Foundation Models?

Foundation models are like big collections of data that cover lots of different topics and ways of doing things, like language, audio, and vision. They go through a ton of training to understand and create all kinds of content in different areas. Because of this, foundation models can be used for all sorts of things, which makes them super valuable in the world of AI.

The Power of Foundation Models

One of the most powerful types of foundation models is the Large Language Model (LLM). LLMs are trained on extensive text data and can understand and generate human-like text. This makes them great for tasks such as text generation, translation, and more.

How can we use Foundation Models then?

So, there are two primary ways to use foundation models:

1. Prompts: You can pass prompts (input queries) to the foundation model and receive responses. This is a straightforward way to get the model to perform tasks, just like we do with ChatGPT.

2. Inference Parameters: This approach involves configuring parameters such as top P, top K, and temperature. Though it sounds technical, it significantly influences the model’s output. Here is a breakdown of these parameters:

• Top P: This controls the selection of tokens based on their combined probability. A higher top P value (like 0.9) means the output will be more varied but might be a bit random. Lower top P values make the output more predictable.

• Top K: Similar to the k-nearest neighbours (KNN) algorithm, top K limits the selection to the top K probable tokens. Typical values range from 10 to 100. A value of 1 is called a greedy strategy, as it always chooses the most probable token.

• Temperature: This parameter affects the randomness of the output. Think of this as setting the creativity level. Higher temperatures lead to more creative and random outputs, while lower temperatures make the output more predictable and steady.

Then How do LLMs Work?

LLMs operate on tokens, which can be words, letters, or parts of words. The model takes a sequence of input tokens and predicts the next token. By using effective inference parameters, you can guide the LLM to produce relevant and coherent outputs for your specific use case.

In the next article, we will go deeper into why vectors and vector databases are so important in LLMs. Stay tuned!