ChatGPT implementation

Machine Learning Ai

ChatGPT implementation

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Last updated May 4, 2025

About This Course

This course provides an in-depth, hands-on journey through the complete implementation of a GPT-style language model, similar to OpenAI’s GPT-2. Built entirely using PyTorch, this codebase shows you how to tokenize data, construct Transformer-based models (including causal self-attention and MLP blocks), train efficiently with distributed training (DDP + gradient accumulation), evaluate with loss and accuracy metrics (including HellaSwag tasks), and generate text in an autoregressive fashion.

You will not just use Hugging Face tools—you will replicate how GPT works at the core. This means building positional embeddings, attention heads, model layers, training loops, learning rate schedulers, validation steps, and generation logic—all from scratch.

Whether you're an AI researcher, developer, or enthusiast, this course will give you an insider's view of what powers ChatGPT and how you can create your own scaled-down version for specific domains or experiments.

What You'll Learn

By the end of this course, learners will be able to: Understand the architecture of GPT models, including embedding layers, attention, and MLPs.

Implement causal self-attention using scaled dot-product attention (FlashAttention).

Use PyTorch to construct Transformer blocks with residual connections and layer normalization.

Build and understand the GPT class that wraps the transformer architecture.

Initialize GPT model parameters with custom scaling rules.

Load and adapt Hugging Face pretrained GPT-2 weights into your custom model.

Course Features

Lifetime Access
Mobile & Desktop Access
Certificate of Completion
Downloadable Resources

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4 Sections

Introduction and Setup

This chapter lays the foundation for building a ChatGPT-style model from scratch. You will set up the development environment, install all necessary dependencies, and understand the overall project structure. Additionally, you'll configure device detection (CPU/GPU), set consistent random seeds for reproducibility, and understand the environment variables that are crucial for distributed training using PyTorch's DDP (Distributed Data Parallel) framework. This setup ensures your model can scale across multiple GPUs or run efficiently on a single GPU/CPU during prototyping. By the end of this chapter, you’ll be fully prepared to dive into building and training your own GPT-style model.

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8 Sections

Model Architecture

In this chapter, you'll dive into the heart of the GPT model—its architecture. Starting with a configuration blueprint via the GPTConfig dataclass, you’ll incrementally build and understand the components that form a GPT-style transformer. This includes multi-head causal self-attention using FlashAttention (via scaled_dot_product_attention), a GELU-based MLP block, residual connections, and layer normalization. You'll construct a full GPT model by combining these components, initialize the parameters with custom scaling logic for deep networks, and wrap everything in a clean, modular class structure. The forward pass will stitch together embeddings, transformer layers, and output projection to complete the autoregressive pipeline. By the end of this chapter, you’ll understand how to implement a functional transformer model from scratch using PyTorch.

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4 Sections

Dataset and Tokenization

This chapter focuses on preparing and feeding data efficiently into the GPT model for training. It begins by explaining how to work with pre-tokenized datasets stored in .npy format, allowing for fast loading and reduced preprocessing overhead. You’ll explore the custom DataLoaderLite class, which handles token batching, buffer slicing, and dataset iteration in a memory-efficient manner. You’ll also learn how to manage large datasets split into multiple shards and implement automatic shard rotation to ensure continuous and seamless training. The chapter concludes with a look at tiktoken, the highly optimized tokenizer used in OpenAI models, and how it's leveraged for encoding and decoding text in GPT-compatible formats. By mastering these tools and techniques, you'll be equipped to handle large-scale dataset processing for autoregressive language modeling tasks.

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4 Sections

Optimizer and Training Strategy

This chapter covers the core strategies used to optimize and stabilize training in large language models like GPT. You’ll learn how model parameters are grouped into decay and non-decay sets to apply selective weight regularization—a best practice for training transformers. You will then configure the AdamW optimizer, including using its fused variant for faster performance on supported hardware. The chapter dives deep into advanced training techniques like gradient clipping to avoid exploding gradients and gradient accumulation to simulate large batch sizes without exceeding GPU memory limits. Finally, you’ll implement a cosine learning rate schedule with warmup steps, enabling smoother convergence and improved generalization. By the end of this chapter, you’ll be equipped with powerful optimization strategies tailored for transformer training at scale.

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4 Sections

Distributed Training with DDP

This chapter equips you with the tools to scale your GPT training across multiple GPUs using PyTorch's DistributedDataParallel (DDP). You’ll start by initializing the distributed environment via init_process_group, which synchronizes processes across devices. Then, you'll learn how to assign each process to a specific GPU using torch.cuda.set_device, ensuring that multiple processes can coexist without device conflicts. The chapter explains the critical environment variables RANK, LOCAL_RANK, and WORLD_SIZE—which determine the global identity of a process, its local GPU assignment, and the total number of training processes. Finally, you’ll wrap your GPT model with DistributedDataParallel, enabling automatic gradient synchronization across GPUs, significantly improving training throughput and efficiency. By mastering DDP, you’ll be ready to train large models across multiple GPUs and even nodes—just like in modern large-scale AI labs.

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4 Sections

Evaluation & Validation (Loss + HellaSwag)

his chapter focuses on evaluating model performance during training through both standard validation loss and task-specific benchmarks like HellaSwag. You’ll begin by computing the autoregressive cross-entropy loss on validation batches to monitor how well the model is learning language patterns. Then, using torch.distributed.all_reduce, you’ll average loss values across all distributed processes to get a unified view of performance in multi-GPU training. You’ll also explore the HellaSwag benchmark, which tests the model's ability to perform commonsense reasoning by choosing the most coherent sentence continuation. You'll learn how to use loss masking to focus evaluation only on completion tokens and compute accuracy from normalized losses across multiple choices. The final step ensures these metrics are aggregated correctly across all GPUs for consistent reporting. This chapter gives you the tools to measure both general language modeling performance and task-specific reasoning ability in a distributed setup.

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4 Sections

Text Generation

In this chapter, you’ll learn how to use the trained GPT model to generate coherent and creative text in an autoregressive manner. Beginning with the extraction of logits from the final token position, you'll apply top-k sampling to control diversity and temperature scaling to adjust the model’s confidence during generation. These techniques help strike a balance between repetition and creativity. You’ll then implement logic to grow the input context step by step, feeding each new token back into the model to extend the sequence—mimicking the autoregressive decoding strategy used in production LLMs like ChatGPT. Finally, you’ll generate multiple return sequences in parallel and decode them back into human-readable text using the tokenizer. By the end of this chapter, you’ll be able to prompt your model with text and generate convincing natural language completions.

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3 Sections

Logging and Checkpointing

This chapter teaches you how to track and preserve model progress through systematic logging and checkpointing. You’ll start by creating structured log files to record important training metrics such as training loss, validation loss, and HellaSwag accuracy at regular intervals. This enables real-time monitoring and post-training analysis. Next, you’ll implement a checkpointing mechanism that saves the model’s weights, configuration, and training step every few thousand iterations—or at the final step. These checkpoints allow you to resume training, run evaluations on earlier model states, or deploy the model at specific milestones. By the end of this chapter, you’ll have a robust system to ensure reproducibility, debugging capability, and reliable progress tracking during long training runs.

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5 Sections

Training Loop Integration

This chapter brings all previously developed components together into a coherent and efficient training loop. You’ll begin by understanding how to toggle the model between training and evaluation modes to ensure proper behavior of layers like dropout and layer normalization. Then, you’ll implement gradient accumulation, enabling training with large effective batch sizes while staying within GPU memory constraints. You’ll also learn how to track per-step training loss and ensure gradient synchronization in distributed setups. The chapter further explains how to dynamically update the learning rate using a scheduler and how to perform GPU synchronization to ensure accurate timing and throughput measurements. By the end of this chapter, you’ll have a fully operational, performance-optimized training loop that can scale across multiple devices and support robust training of GPT models.

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4 Sections

Putting it All Together

This final chapter focuses on full-stack integration and practical deployment of your GPT training pipeline. You’ll learn how to adapt the training script to different hardware configurations—whether you're using a CPU, a single GPU, or multiple GPUs with PyTorch’s torchrun. Then, you'll see how to bootstrap your model with pretrained GPT-2 weights for either fine-tuning or evaluation using Hugging Face compatibility. You’ll also learn how to evaluate your trained model on custom datasets, enabling experimentation and deployment in real-world tasks. This chapter ensures you're not only capable of training a GPT model but also deploying and customizing it for your own use cases.

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