Welcome to the
thrilling
world of LLMs!
Today, you will embark on a journey to
train a LLM to perform tasks such as text generation, summarization, etc.
But wait, what are LLMs?
Think of LLM as a super-smart computer friend that understands
and talks like a human, helping us with all sorts of tasks using language.
Developing an LLM model may seem complex, but let's break down the steps.
START WITH DATA COLLECTION
Gather a large amount of data from various sources like books, articles, websites, and conversations to train the model.
Model Training
This is where the magic happens.
Model training teaches AI to predict and perform tasks.
An LLM uses a neural network architecture which acts as
a virtual brain, learning language by
recognizing word patterns and relationships.
Neural Network Components
It has 3 building blocks:
Input node: provides data to model
Function node: processes data
Output node: generates result
LINEAR REGRESSION
We can use the linear regression for input processing. It helps us find patterns and make predictions from data.
MODEL OUTPUTS
As anticipated, this model is generating predictions (yellow line) based on the input data.
LOGISTIC REGRESSION
We'll transition to logistic regression by substituting the function
node.
Now, observe how the model's output (depicted by the decision region background) adapts to the new
approach.
NO LIMITS
Neural networks link artificial neurons, creating depth and width for optimal performance and function.
Fine Tuning & Testing
Post training, the LLM is fine-tuned and tested
for improving model's performance.
TEXT GENERATION
Through testing and fine-tuning, an LLM learns to create meaningful text, akin to coherent teaching.
LANGUAGE TRANSLATION
LLMs excel in language translation through testing, connecting elements to generate accurate, coherent translated text.
QUESTION ANSWERING
LLMs develop proficiency question answering through testing, connecting elements to provide accurate, coherent contextual answers.
SUMMARIZATION
Through testing and fine-tuning, LLMs excel in summarization, connecting elements for coherent and accurate summaries.
