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https://github.com/hadiuzzaman524/deep-dive-on-machine-learning

Explore the world of advanced AI by building powerful systems like ChatGPT and beyond. This repository covers foundational concepts, such as Large Language Models, and dives into future topics like Retrieval-Augmented Generation (RAG) and intelligent Agents.
https://github.com/hadiuzzaman524/deep-dive-on-machine-learning

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Explore the world of advanced AI by building powerful systems like ChatGPT and beyond. This repository covers foundational concepts, such as Large Language Models, and dives into future topics like Retrieval-Augmented Generation (RAG) and intelligent Agents.

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# How do you build an LLM like ChatGPT using HuggingFace Transformers?



Large Language Models (LLMs) like ChatGPT have revolutionized how we interact with technology, enabling applications like customer support bots, content generation tools, and even creative writing assistants. Imagine asking ChatGPT to help you brainstorm ideas for a blog or debug your code, and it responds intelligently and contextually. Have you ever wondered how such a system is built? Let’s explore this step by step, like a story, to understand the fascinating journey of creating an LLM.



---



## The Hero of Our Story: Large Language Models



Large Language Models are trained to predict and generate text. They work by learning patterns, structures, and contextual relationships within language. Think of them as a highly knowledgeable assistant that can finish your sentences, answer your questions, or even write essays. ChatGPT, for example, is built on top of the GPT (Generative Pre-trained Transformer) architecture, making it capable of holding conversations, summarizing information, and more.



### Real-Life Example



Imagine a book club discussing novels. Every member has read thousands of books and remembers the patterns of storytelling, sentence structures, and character dialogues. An LLM is like the most experienced member of this club, trained on a massive collection of books and documents, capable of contributing thoughtfully to the discussion. 



---



## Setting the Stage: Key Components of an LLM



Building an LLM involves several critical steps, each playing a specific role in the story:



1. **Dataset**: The source of knowledge, akin to a library that feeds the model with information.

2. **Tokenizer**: The interpreter, breaking down text into digestible pieces for the model.

3. **Transformer Architecture**: The brain, understanding and generating coherent text.

4. **Fine-Tuning**: The personalized training that tailors the model for specific tasks.

5. **Chat Interface**: The conversational front-end where users interact with the model.



Let’s dive into each step and uncover its significance.



---



## Step 1: Acquiring Knowledge from the Dataset



### What is a Dataset?



A dataset is the collection of text that trains the model. It’s like a treasure trove of knowledge, containing books, articles, and conversations. For our story, we’ll use **Wikitext-2**, a dataset comprising raw Wikipedia articles, ideal for training a language model.



```python

from datasets import load_dataset



# Load the Wikitext-2 dataset

dataset = load_dataset("wikitext", "wikitext-2-raw-v1")



# Inspect the dataset structure

print(dataset)

```



The dataset is divided into training, validation, and test sets, ensuring the model learns effectively and generalizes well to unseen data.



---



## Step 2: Tokenization – Translating Text into Numbers



### What is a Tokenizer?



Imagine reading a complex sentence. Before understanding it, you break it down into words or phrases. Similarly, a tokenizer splits text into smaller units, called tokens, which could be words, subwords, or characters. The model processes these tokens as numbers.



```python

from transformers import AutoTokenizer



# Load the GPT-2 tokenizer

tokenizer = AutoTokenizer.from_pretrained("gpt2")

tokenizer.pad_token = tokenizer.eos_token  # Use EOS token for padding



# Tokenize the dataset

def preprocess_function(examples):

    tokens = tokenizer(

        examples["text"], truncation=True, padding="max_length", max_length=128

    )

    tokens["labels"] = tokens["input_ids"].copy()  # Labels are the same as input_ids

    return tokens



# Apply preprocessing

tokenized_dataset = dataset.map(preprocess_function, batched=True)

```



### Why Tokenization Matters



Tokenization bridges the gap between raw text and the numerical input that neural networks require. It ensures the model can process diverse text efficiently.



---



## Step 3: Transformer Architecture – The Brain Behind LLMs



### What is a Transformer?



A transformer is a neural network architecture designed for understanding sequences of data. It uses mechanisms like **self-attention** to identify relationships between words in a sentence, enabling it to grasp context effectively.



### Why GPT-2?



GPT-2 (Generative Pre-trained Transformer 2) is a popular transformer model known for its versatility in generating coherent and contextually relevant text. It’s the backbone of many LLMs.



```python

from transformers import GPT2LMHeadModel, GPT2Config



# Define the GPT-2 configuration

config = GPT2Config(

    vocab_size=len(tokenizer),  # Match tokenizer’s vocabulary size

    n_embd=128,                # Embedding size

    n_layer=6,                 # Number of layers

    n_head=8                  # Number of attention heads

)



# Initialize the model

model = GPT2LMHeadModel(config)

```



### The Role of GPT2LMHeadModel



This model is specifically designed for language tasks. It predicts the next token in a sequence, making it ideal for text generation and conversation systems.



---



## Step 4: Training – Teaching the Model



### What is Training?



Training is the process of feeding data into the model, calculating its predictions, and adjusting its parameters to improve performance. Think of it as teaching a student by providing practice examples and correcting mistakes.



### Setting Training Parameters



HuggingFace’s `TrainingArguments` simplifies configuring the training process.



```python

from transformers import TrainingArguments, Trainer



# Define training arguments

training_args = TrainingArguments(

    output_dir="./small-gpt2-model",  # Save the model here

    overwrite_output_dir=True,

    num_train_epochs=3,               # Number of epochs

    per_device_train_batch_size=8,    # Batch size per device

    save_steps=500,                   # Save model every 500 steps

    save_total_limit=2,               # Limit the number of saved checkpoints

    logging_dir="./logs",            # Logging directory

    logging_steps=100

)



# Initialize the Trainer

trainer = Trainer(

    model=model,

    args=training_args,

    train_dataset=tokenized_dataset["train"],

    eval_dataset=tokenized_dataset["validation"]

)

```



### Fine-Tuning the Model



Fine-tuning adapts the pre-trained model to specific data, enhancing its ability to generate relevant and accurate text.



```python

trainer.train()

```



---



## Step 5: Creating a Chat Interface



### Bringing the Model to Life



A chat interface allows users to interact with the model. It’s where the magic of conversation happens.



```python

import torch



def chat_with_model(model, tokenizer):

    # Ensure the model is on the correct device

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    model = model.to(device)



    while True:

        user_input = input("You: ")

        if user_input.lower() in ["exit", "quit"]:

            break



        # Tokenize input and move it to the same device as the model

        input_ids = tokenizer.encode(user_input, return_tensors="pt").to(device)



        # Generate response

        response = model.generate(

            input_ids,

            max_length=50,

            num_return_sequences=1,

            pad_token_id=tokenizer.eos_token_id,

        )



        # Decode and print response

        decoded_response = tokenizer.decode(response[0], skip_special_tokens=True)

        print(f"ChatGPT: {decoded_response}")



# Ensure `model` and `tokenizer` are properly initialized before calling this function

chat_with_model(model, tokenizer)

```



---



## Final Thoughts



Building an LLM like ChatGPT involves combining powerful tools and techniques. From preprocessing datasets to fine-tuning models, every step contributes to creating a conversational agent. With frameworks like HuggingFace’s `transformers`, this complex process becomes more accessible, enabling developers to experiment and innovate.



Start your journey today and create your own LLM to unlock endless possibilities in AI-driven applications. Happy coding!