🚀 Training a GPT Model from Scratch with PyTorch (Tokenizer + Transformer + Inference)

After working for several years on state-of-the-art models and deploying them in real-world applications, I wanted to revisit the fundamentals.

So I built a GPT-like model completely from scratch — including the tokenizer and transformer architecture — using pure PyTorch.

⚙️ This post walks through my approach, architecture, training, and inference pipeline using a custom Shakespeare dataset.

The goal: Understand how GPTs really work under the hood.

🔧 Highlights
📜 Trained on a cleaned corpus of Shakespeare plays
🔤 Built a Byte-Pair Encoding (BPE) tokenizer from scratch
🧠 Implemented a transformer model using PyTorch (no HF/Transformers)

📈 Achieved a strong loss curve through tuning and debugging
🔁 Built an end-to-end training + inference pipeline
☁️ Hosted the model + tokenizer on Hugging Face for public use

🧠 Why Build from Scratch?
While Hugging Face and pretrained models are excellent for real-world use, understanding the nuts and bolts of how LLMs work is essential for:

Customizing architectures
Optimizing memory/performance
Working on low-resource or domain-specific tasks
Research and experimentation

📊 Training Loss Graph
The model was trained for ~15 epochs. You can clearly see how the loss drops — especially after tuning hyperparameters.

📁 Code & Resources
📘 Full Article on Medium – includes deep dives on each part
💻 GitHub Repo – notebooks, training script, model loading, etc.

🚀 How to Use
🔹 Option 1: Direct Python Script (model download + inference)
python saved_models/load_model.py
Downloads model + tokenizer from Hugging Face Loads them into memory. Ready for predictions

🔹 Option 2: Notebook Execution
Use the end_to_end folder:

1_train_custom_gpt.ipynb — training pipeline
2_predict_with_trained_gpt.ipynb — inference and generation

🔍 Example Output
Input: ROMEO:
Generated: What hast thou done? My love is gone too soon...
The output retains Shakespearean style thanks to custom training.

🙌 Let’s Connect!
If you're working on LLMs, transformers, or AI engineering — I’d love to connect and collaborate.

💬 Drop your thoughts or questions in the comments!