What are LLMS?

This is the 2nd blog from the series - "Learning GenAI".

In today's blog we are going to learn about LLMs (Large Language Model).

Large Language Models (LLMs)

LLMs, or "Large Language Models," are advanced AI models that are pre-trained on vast amounts of text data to perform a variety of language-related tasks. These models serve as the backbone of Generative AI, enabling machines to understand and generate human-like text.

While developing a GenAI application, we need to decide which LLM to use. There are different LLMs provider, and pricing is also differ.

Workflow of LLMs:

The basic workflow of an LLM can be summarized as:

Input (from user) → Context Window → LLM → Output

Let’s break down each step:

a. Input: The initial input provided by the user. This could be a question, a prompt, or any text that the user wants the AI to process.

b. Context Window: This is the portion of the input (and sometimes previous interactions) that the LLM considers when generating a response. The context window has a fixed size, meaning it can only hold a certain number of tokens (words or characters).

c. LLM: The Large Language Model processes the information within the context window, using its vast training to understand and generate a relevant response.

d. Output: The response generated by the LLM based on the provided context.

Types of LLMs:

There are different ways to use LLMs depending on your needs:

1. Use Pre-trained LLMs: Utilize models that have already been trained on large datasets by AI providers. These models are ready to use and can be implemented directly into applications.

2. Train Your Own Models: If you have specific requirements, you can train your own models using custom data to better suit your use case.

3. Hybrid Approach: Combine pre-trained models with custom-trained models to leverage the benefits of both general knowledge and specialized training.

Foundational Models

Foundational models are pre-trained LLMs provided by various AI providers. They are a great starting point for building applications:

1. Pre-Trained Models: These are ready-to-use models available through different providers.

2. APIs and SDKs: Foundational models often come with APIs and SDKs, making it easier to integrate them into your projects.

3. Application Development: You can use a foundational model’s API to build applications on top of it, utilizing the pre-trained capabilities for your specific needs.

How to Choose a Foundational Model

When selecting a foundational model, consider the following factors:

1. Use Cases: Choose a model that aligns with the specific tasks or applications you want to develop.

2. Pricing: Evaluate the cost associated with using the model, especially if your application requires extensive usage.

3. Token Limits: Be aware of the context window size and the number of tokens a model can process, as this can affect performance.

Common Foundational Models

Here are some popular foundational models used in the industry:

1. GPT: Developed by OpenAI, known for generating high-quality text.

2. BERT: Created by Google, excels in understanding the context of words in search queries.

3. T5: A text-to-text transfer transformer that can perform multiple NLP tasks.

4. Claude: A model by Anthropic, designed for safe and effective conversational AI.

5. Jurassic: Developed by AI21, known for its large-scale natural language processing capabilities.

Happy Learning!!