An interactive guide to Large Language & Vision Models

on
The LLM & VLM Ecosystem (Sept 2025)

The AI Model Ecosystem

Created by Dr. Sharad Maheshwari, imagingsimplified@gmail.com

An interactive guide to Large Language & Vision Models (as of Sept 2025)

What are LLMs and VLMs?

This section breaks down the fundamental building blocks of the modern AI landscape. We'll define what these models are, what they do, and explore the crucial difference between open and closed-source approaches to building them.

🤖 Large Language Models (LLMs)

Think of an LLM as a very advanced autocomplete for ideas, not just words. It's an AI that has been trained on a massive amount of text data (like books, articles, and websites). This training allows it to understand and generate human-like text, answer questions, translate languages, summarize long documents, and even write code.

Analogy: It's like a universal intern who has read almost the entire internet and can help you with any task involving language.

👁️ Vision-Language Models (VLMs)

VLMs are a step beyond LLMs. They can understand and process both text and images. You can give a VLM an image and ask questions about it, have it describe what's happening, or even identify objects. They connect what they "see" with what they "know" from text.

Analogy: If an LLM is a knowledgeable librarian, a VLM is a librarian who can also look at your photos and tell you the story behind them.

Open Source vs. Closed Source

The development approach behind a model determines who can use it, how they can use it, and how much control they have. This is one of the most important distinctions in the AI ecosystem.

🔒 Closed Source

These models are proprietary. The code, data, and model weights are kept secret by the company that created them. Users typically access them through a paid API (Application Programming Interface).

Analogy: Using a cloud service like Google Docs. You can use its powerful features, but you can't see or change its underlying code.

Pros:

  • Often the most powerful models available.
  • Easy to use, no hardware setup needed.
  • Professionally maintained and updated.

Cons:

  • Can be expensive to use at scale.
  • Less control and no ability to customize.
  • Data privacy can be a concern.

🌍 Open Source

Here, the model's architecture, code, and often its trained weights are publicly released. Anyone can download, modify, and use them for their own purposes, even commercially.

Analogy: A community recipe book. Anyone can use the recipes, tweak them, or add their own for others to see.

Pros:

  • Full control and transparency.
  • Can be fine-tuned for specific tasks.
  • Can be run on local hardware for privacy.
  • Promotes innovation and collaboration.

Cons:

  • Requires significant technical expertise.
  • Needs powerful (and expensive) hardware.
  • May not be as capable as top closed models.

Comments

Post a Comment