Hello everyone and welcome.
In today's session we'll be learning about models.
We'll go over the different kinds of models that are available as of today.
We'll start with foundation models, then move on to language models.
We'll cover large language models.
And finally we'll wrap up with multimodal models.
It's very important to understand models in general are a vast topic.
It's hard to cover all the intrinsic details in a single course.
However, in this section I've introduced all the essential jargon and terminology you'll need throughout
the course.
This will not only help you understand the content, it will also help you grasp how different models
work and how they perform across categories.
So having said that, let's begin.
Foundation models.
The first model we'll cover is the Foundation model.
Foundation models, also known as FMS, are large deep learning neural networks trained on massive data
sets.
The term foundation model was coined by researchers to describe ML models that are trained on broad,
generalized and unlabeled data and capable of performing a wide variety of general tasks like understanding
language, generating text, and even generating images.
The size and purpose of an FM is what sets it apart from traditional machine learning models.
You can use a foundation model as a base model for developing more specialized downstream applications.
How do foundation models work?
Foundation models are a form of generative AI.
They generate output based on one or more inputs, usually in the form of human language instructions.
These models learn patterns and relationships and use that learning to predict the next item in a sequence.
In the case of text, they predict the next word in a sentence based on context.
With image generation, they analyze image features and create clearer, sharper versions.
It's crucial to remember FM's use learned patterns to predict the next logical item in a sequence.
Examples of foundation models.
Some examples include Bert bidirectional encoder representations.
Stars Transformers released in 2018.
It analyzes the full context of a sentence bidirectionally and then makes predictions.
G.
Generative pre-trained transformer also released in 2018 by OpenAI.
It uses a 12 layer transformer decoder with a self-attention mechanism, and was trained on a dataset
of over 11,000 free novels.
Amazon Titan a powerful pre-trained foundation model designed for general purpose use.
There are many others too, from organizations like cohere, Anthropic, and Meta's Llama family.
Language models.
Now let's understand language models.
A language model is a type of machine learning model designed specifically to represent language.
It forms the basis for many language tasks like Q and a summarization or semantic search.
How are models trained?
The training process involves neural networks like Bert processing millions of data points.
This process is what we call training.
Once you train a model, the next phase is fine tuning.
There are many tasks that benefit from language model like sentiment analysis, question answering,
and others.
Adapting a general purpose model to such task is known as fine tuning.
Now let's understand diagrammatically how this looks like.
General purpose.
Purpose models like Bert or its bigger sister, like Roberta, require a huge amount of data to learn
a language's regularities.
NLP practitioners often use Wikipedia and other freely available collections of textual data to train
them.
Here are the general purpose model available.
As of now we have Bert code.
Roberta, Roberta and others that are listed here from the Bert family.
So once you have a general purpose model, you would want to fine tune a language model to make it like
Bert to a specific domain.
You'd fine tune it using domain specific data.
For example, Bert, plus scientific papers equals sy Bert Bert plus financial texts equals fin.
Bert plus legal documents equals legal.
Bert.
That's how fine tuning helps specialize a model.
Large language models llms with advancements and language models, researchers built even larger ones,
requiring massive computing power and huge training data sets.
That's how large language models or less were born.
Model size comparison.
Let's compare Bert Base Bert Large, bigger GPT three with 175 billion parameters, GPT four even larger.
That's why they're called large language models.
Training Llms the phases.
There are three key phases.
Pre-training the model learns by predicting the next word in a sequence.
Supervised fine tuning.
Researchers teach the model how to respond to prompts by using high quality Q and A datasets.
Reinforcement learning from human feedback, also known as RL.
This involves human rankings to help guide the model toward better answers.
You can imagine the model evolving like this.
At first it's like a wild dragon.
Supervised fine tuning tames it r f allows it to collaborate with humans effectively.
Multimodal models.
Now let's talk about multimodal models.
These models can understand and generate content across multiple modalities, not just text.
So instead of only processing and generating text, they can now process text, images, audio and video
and output text, images, audio and video.
This enables much richer, more context aware and versatile AI experiences.
I hope you now have a solid understanding of the different kinds of models and how they evolved over
time.
I'll see you in the next video.