Have you ever wanted to peek under the hood of ChatGPT-like models and actually build one yourself? 🔧🧠
If yes, then you’ll love the GitHub repo 👉 LLMs-from-scratch 👈. It’s the official code companion to the book Build a Large Language Model (From Scratch) by Sebastian Raschka.
Unlike most tutorials that either drown you in theory or hide everything behind Hugging Face APIs, this repo strikes the sweet spot — you’ll implement each part of a GPT-like model step by step, from tokenization all the way to inference.
📚 What You’ll Learn in This Repo
The repo walks you through the end-to-end journey of building a GPT model, with clean, minimal PyTorch code. Here’s what you’ll find:
• Tokenization & Data Prep
◦ Learn how raw text becomes input tokens.
◦ Implement byte pair encoding (BPE) and batching.
• Transformer Foundations
◦ Build embeddings and positional encodings.
◦ Implement multi-head self-attention from scratch.
◦ Add feed-forward layers, residual connections, and normalization.
• The GPT Architecture
◦ Stack decoder blocks to create a full GPT-like network.
◦ Understand why scaling matters and how model depth affects performance.
• Training & Pretraining
◦ Train a “nanoGPT” on Shakespeare to see text generation in action.
◦ Scale up with AdamW optimizer, weight initialization tricks, and learning rate schedules.
◦ Explore mixed-precision training to handle bigger datasets efficiently.
• Finetuning
◦ Take a pretrained model and adapt it to new downstream tasks.
◦ Avoid catastrophic forgetting with clever training strategies.
• Inference & Sampling
◦ Generate text with greedy search, top-k sampling, and nucleus sampling.
◦ Compare your model outputs with Hugging Face baselines.
🛠️ Tech Stack
Everything is written in PyTorch 🐍, keeping things simple yet powerful:
• torch → model building & training
• numpy → matrix ops
• datasets → dataset loading
• tqdm → progress visualization
No bloated abstractions — just transparent code so you actually learn what makes LLMs tick.
⚡ A Quick Taste of the Code
Here’s how simple it feels to build and train your own mini GPT using this repo’s components:
from model import GPTModel from trainer import train_model # Define configuration config = { "vocab_size": 5000, "n_embd": 128, "n_head": 4, "n_layer": 4, "block_size": 128, } # Create model model = GPTModel(config) # Train the model train_model(model, dataset="tiny-shakespeare.txt", epochs=10, batch_size=64) # Generate text print(model.generate("To be, or not to be", max_new_tokens=50))
Boom 💥 — you’ve just built and trained a working GPT-style model that can generate Shakespearean-like text.
🌍 Why This Repo Matters
Most LLM projects focus on using models. This repo is about building them. That’s a game-changer if you’re:
• A student wanting to understand transformers inside out.
• A developer aiming to train lightweight domain-specific LLMs.
• A researcher experimenting with architectural tweaks.
• An AI hobbyist curious about building your own GPT clone.
It’s the AI equivalent of learning to build an engine instead of just driving the car 🚗💨.
🧩 Visual Overview
Here’s the big picture you’ll see unfold in the repo:
Raw Text → Tokenization → Embeddings → Transformer Blocks → Pretrained GPT → Finetuned GPT → Inference
This journey takes you from data to a working large language model.
🚀 Final Thoughts
The LLMs-from-scratch repository is not just code — it’s an educational roadmap for anyone who wants to master how LLMs really work.
So if you’ve ever thought, “Could I build my own GPT?” … the answer is yes. Clone this repo, follow the book, and start your journey into the heart of modern AI. 🧑💻✨
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