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TL;DR 🚀

We’re going to explore what LLMs are, how they work, and build a small AI text generator using Python and Hugging Face.

You’ll learn how to talk to a machine and watch it talk back (kind of like magic)!

Ready? Let’s go!

What Are LLMs? 🤔

LLMs, the brains behind powerful AI like ChatGPT, are AI models that understand and generate human-like text based on patterns they've learned from tons of data.

Think of them as advanced “auto-complete” systems that can carry on conversations, answer questions, or even write entire essays.

Here’s how they work in a nutshell:

• Training: They are trained on huge datasets of text, learning the context of words and sentences.

• Prediction: After training, they can predict the next word or phrase based on the input given.

Popular LLMs include OpenAI’s GPT (like the one used here!), which powers many chatbots, virtual assistants, and other AI-driven applications that work with natural language.

How Does an LLM Work?

At its core, an LLM takes in text input and uses statistical models to predict the most likely next word or sentence.

Over time, these models are trained on billions of examples, allowing them to "learn" the patterns and nuances of human language.

Here’s a step-by-step breakdown of how they work:

1. Tokenization: The input text is split into smaller units (tokens) that the model can process.

2. Context: The model considers the surrounding words to generate contextually relevant responses.

3. Prediction: Based on the context, the model predicts the next token.

4. Decoding: These tokens are then decoded back into human-readable text.

Let’s Build a Mini LLM in Python 🐍

We’ll use a pre-trained LLM from Hugging Face’s transformers library (so we don’t have to build one from scratch).

In just a few lines of code, you’ll see how LLMs can generate text based on the input you provide.

Step-by-Step Code Explanation

1. Install the Necessary Libraries

Before we dive into the code, we need to install a couple of libraries:

pip install transformers torch

• `transformers`: A Python library that gives access to pre-trained language models.

• `torch`: The deep learning library used to power many AI models.

2. Import the Libraries

Once installed, let’s start by importing the necessary components:

from transformers import AutoModelForCausalLM, AutoTokenizer

• AutoModelForCausalLM: This is a type of language model that can predict the next word in a sequence.

• AutoTokenizer: This converts text into a format the model can understand (and back into text for output).

3. Load a Pre-trained Model and Tokenizer

We’re going to use GPT-2, a famous LLM, for this example. It’s pre-trained and ready to go!

model_name = "gpt2"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForCausalLM.from_pretrained(model_name)

• gpt2: This is the model we’ll be using. It’s like the brain of our LLM.

• Tokenizer: This breaks down the input text into tokens (tiny pieces of the sentence).

• Model: This is the brain! It predicts what comes next based on the input text.

4. Tokenize the Input

Before the model can work its magic, we need to convert the text into tokens (numbers the model understands).

Let’s give the AI a prompt and see what it generates.

input_text = "AI is the future because"

input_ids = tokenizer.encode(input_text, return_tensors='pt')

• encode: This converts the text into a list of numbers (tokens).

• return_tensors='pt': We return the tokens as PyTorch tensors (required for the model).

5. Generate Text 🚀

Now for the fun part: getting the model to generate some text!

output = model.generate(

    input_ids,

    max_length=50,

    do_sample=True,

    top_p=0.9,

    temperature=0.8

)

• generate: This function asks the model to predict what comes next based on the input text.

• max_length=50: The maximum number of tokens (words) the model will generate.

• do_sample=True: Ensures the output is sampled randomly, not always the same.

• top_p and temperature: These control the creativity and randomness of the output. Play with these for fun results!

6. Decode the Output

Finally, we need to convert the model's output (tokens) back into readable text:

generated_text = tokenizer.decode(output[0], skip_special_tokens=True)

print(generated_text)

• decode: This turns the tokens back into human-readable text.

• skip_special_tokens=True: This removes any special tokens the model might add.

Sample Output

AI is the future because of its new technology," he said.

"I think we're going to see a lot of innovation in the next few years. It will bring more and more people to our space and make the world a better place

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Full Code

Here’s the full code in one place:

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the pre-trained model and tokenizer

model_name = "gpt2"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForCausalLM.from_pretrained(model_name)

# Input text

input_text = "AI is the future because"

# Convert the input text to tokens

input_ids = tokenizer.encode(input_text, return_tensors='pt')

# Generate text based on the input

output = model.generate(

    input_ids,

    max_length=50,

    do_sample=True,

    top_p=0.9,

    temperature=0.8

)

# Decode the generated tokens to readable text

generated_text = tokenizer.decode(output[0], skip_special_tokens=True)

# Print the generated text

print(generated_text)

Here’s What Just Happened 💡

• You Gave a Prompt: The AI was given an initial sentence to start with.

• AI Predicted What Comes Next: Based on the input, the LLM generated the most probable sequence of words to follow.

• You Got AI-Generated Text: Just like that, the AI came up with something new and unique! 🎉

Final Thoughts 💡

Congrats! 🎉

You’ve just created your own AI text generator using LLMs! 🎊 You now understand how machines can generate text by predicting what comes next, making them super useful for chatbots, content generation, and more. With just a few lines of Python code, you’ve stepped into the world of Artificial Intelligence! 🤖

LLMs are an exciting field of AI, and you’ve just taken your first step into building intelligent systems that can “talk” with humans!

TL;DR Recap

• LLMs, like GPT-2, are trained on massive amounts of data to generate human-like text.

• You learned how to use Python’s transformers library to generate text using an LLM.

• With a few lines of code, you created a simple AI text generator that can continue a sentence.

Coding with a Smile 🤣 😂

Virtual Environment Victory:

Setting up a virtual environment feels like building a secret lair. It’s your personal coding space, safe from the chaos of the outside world.

Recommended Resources 📚

💥 Use AI to 10X your productivity & efficiency at work (free bonus) 🤯

Still struggling to achieve work-life balance and manage your time efficiently?

Join this 3 hour Intensive Workshop on AI & ChatGPT tools (usually $399) but FREE for first 100 readers.

Save your free spot here (seats are filling fast!) ⏰

Want to be one of these people & be a smart worker?
Free up 3 hours of your time to learn AI strategies & hacks that less than 1% people know! 

🗓️ Tomorrow | ⏱️ 10 AM EST

In this workshop, you will learn how to:

✅ Make smarter decisions based on data in seconds using AI 
✅ Automate daily tasks and increase productivity & creativity
✅ Skyrocket your business growth by leveraging the power of AI
✅ Save 1000s of dollars by using ChatGPT to simplify complex problems 

👉 Hurry! Click here to register (FREE for First 100 people only) 🎁

Let’s Inspire Future AI Coders Together! ☕

I’m excited to continue sharing my passion for Python programming and AI with you all. If you’ve enjoyed the content and found it helpful, do consider supporting my work with a small gift. Just click the link below to make a difference – it’s quick, easy, and every bit helps and motivates me to keep creating awesome contents for you.

Thank you for being amazing!

Want to Explore More? 🌟

Try changing the temperature or top_p values to make the AI’s responses more creative or structured.

Experiment with different prompts and see how the AI’s personality changes! 🎨

🌟 Keep experimenting and see where your AI journey takes you!

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Enjoy your journey into artificial intelligence, machine learning, data analytics, data science and more with Python!

Stay tuned for our next exciting project in the following edition!

Happy coding!🚀📊✨