OpenAI snags $110 billion in investments from Amazon, Nvidia, and Softbank

Introduction

In this tutorial, we'll explore how to work with large language models (LLMs) similar to those powering ChatGPT, using the Hugging Face Transformers library. This tutorial will teach you how to load pre-trained models, generate text, and interact with AI models programmatically - techniques that are fundamental to understanding the technology behind companies like OpenAI that recently secured $110 billion in funding.

Prerequisites

• Basic Python knowledge
• Python 3.7 or higher installed
• Internet connection for downloading models
• Virtual environment recommended (optional but suggested)

Step-by-Step Instructions

1. Set up your Python environment

First, create a virtual environment and install the required packages. This ensures you don't interfere with other Python projects.

python -m venv ai_tutorial_env
source ai_tutorial_env/bin/activate  # On Windows: ai_tutorial_env\Scripts\activate
pip install transformers torch datasets

Why: We're installing the core libraries needed to work with transformers and large language models. The 'transformers' library provides pre-trained models, while 'torch' handles the underlying deep learning operations.

2. Load a pre-trained language model

Let's start by loading a small pre-trained model for text generation. We'll use the GPT-2 model, which is similar to the technology used in ChatGPT.

from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load the tokenizer and model
model_name = "gpt2"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

# Add padding token to GPT2 tokenizer
tokenizer.pad_token = tokenizer.eos_token

Why: This loads the GPT-2 model and tokenizer, which are foundational components for text generation. The tokenizer converts text to tokens that the model can understand, and the model processes these tokens to generate new text.

3. Prepare your input text

Now we'll create some input text that we want the model to continue or complete.

input_text = "The future of artificial intelligence is"
input_ids = tokenizer.encode(input_text, return_tensors='pt')
print(f"Input tokens: {input_ids}")

Why: We're converting our text input into tokens that the model can process. This is necessary because neural networks work with numbers, not raw text.

4. Generate text with the model

Let's generate text based on our input. We'll use different generation parameters to control the output.

# Generate text with different parameters
output = model.generate(
    input_ids,
    max_length=50,
    num_return_sequences=3,
    temperature=0.8,
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id
)

# Decode the generated tokens back to text
for i, generated_sequence in enumerate(output):
    text = tokenizer.decode(generated_sequence, skip_special_tokens=True)
    print(f"Generated text {i+1}: {text}")

Why: This generates multiple text sequences based on our input. The parameters control how creative or deterministic the output is. Temperature controls randomness, while max_length limits how long the output can be.

5. Experiment with different models

Let's try a larger model to see how it compares. We'll use a more powerful model called 'gpt2-medium'.

# Load a larger model
large_model_name = "gpt2-medium"
large_tokenizer = GPT2Tokenizer.from_pretrained(large_model_name)
large_model = GPT2LMHeadModel.from_pretrained(large_model_name)
large_tokenizer.pad_token = large_tokenizer.eos_token

# Generate with the larger model
large_input = "Artificial intelligence is transforming"
large_input_ids = large_tokenizer.encode(large_input, return_tensors='pt')

large_output = large_model.generate(
    large_input_ids,
    max_length=60,
    num_return_sequences=2,
    temperature=0.7,
    do_sample=True,
    pad_token_id=large_tokenizer.eos_token_id
)

for i, sequence in enumerate(large_output):
    text = large_tokenizer.decode(sequence, skip_special_tokens=True)
    print(f"Large model output {i+1}: {text}")

Why: Larger models typically produce better quality outputs but require more computational resources. This demonstrates how model size affects performance - similar to how companies like OpenAI invest heavily in scaling their infrastructure.

6. Create a simple chat interface

Let's build a basic chat-like interface that simulates conversation flow.

def chat_with_model(model, tokenizer, prompt, max_length=100):
    input_ids = tokenizer.encode(prompt, return_tensors='pt')
    
    output = model.generate(
        input_ids,
        max_length=max_length,
        num_return_sequences=1,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )
    
    response = tokenizer.decode(output[0], skip_special_tokens=True)
    return response

# Test our chat function
conversation = "User: Hello, how are you?"
response = chat_with_model(model, tokenizer, conversation)
print(f"Response: {response}")

Why: This simulates how the technology behind ChatGPT works - taking user input and generating appropriate responses. This is the core functionality that powers conversational AI systems.

7. Save and load your model

Finally, let's see how to save your model and tokenizer for later use.

# Save the model and tokenizer
model.save_pretrained('./my_chat_model')
tokenizer.save_pretrained('./my_chat_model')

# Load the saved model
loaded_model = GPT2LMHeadModel.from_pretrained('./my_chat_model')
loaded_tokenizer = GPT2Tokenizer.from_pretrained('./my_chat_model')

print("Model and tokenizer saved and loaded successfully!")

Why: Saving models allows you to reuse trained models without re-downloading them. This is important for production systems and research workflows, similar to how major tech companies maintain their AI infrastructure.

Summary

In this tutorial, you've learned how to work with large language models similar to those powering ChatGPT. You've installed the necessary libraries, loaded pre-trained models, generated text, experimented with different model sizes, created a chat interface, and learned how to save and load models. These are fundamental skills for anyone interested in working with AI technology, particularly the kind of technology that has attracted major investments like the $110 billion round that OpenAI recently secured.

While this tutorial uses smaller models for demonstration purposes, the techniques scale to much larger models used in production systems. Understanding these concepts is crucial as we see major tech companies investing heavily in AI infrastructure and development.