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Build a Custom AI Tiny LLM from Scratch Using PyTorch Part-1
Rating: 3.5 out of 5(31 ratings)
1,531 students

Build a Custom AI Tiny LLM from Scratch Using PyTorch Part-1

A Complete Guide & Chatbot : Bootcamp to create your own ChatGPT from Scratch
Created byAshutosh Shashi
Last updated 3/2026
English

What you'll learn

  • Understand the fundamentals of PyTorch and how to use tensors, autograd, modules, and optimizers to build deep learning models from scratch.
  • Design and implement a word-level tokenizer from scratch, and prepare training data suitable for language modeling tasks.
  • Build a simplified transformer-based language model (TinyGPT) using PyTorch, including embedding layers, positional encoding, and multi-head attention.
  • Train your own Tiny LLM on CPU, visualize loss, and fine-tune model performance using techniques like dropout and temperature-based sampling.
  • Generate text using your trained model, and implement an interactive chatbot that responds to user input via command-line or browser interface.
  • Gain hands-on experience with language model architecture, training pipeline, text generation, and inference flow — with no need for pre-trained models or GPUs.

Course content

3 sections • 15 lectures • 1h 59m total length

  • Introduction3:05
  • Jupyter Notebook through ANACONDA3:38

Requirements

  • Basic understanding of Python programming (variables, loops, functions)
  • Curiosity to learn how AI and language models work from the ground up
  • A computer with Python 3.8+ installed (Windows, Mac, or Linux)
  • No prior experience with PyTorch, deep learning, or NLP required – everything will be explained from scratch
  • A willingness to follow along and build your own model step by step (no copy-paste magic!)

Description

Have you ever wondered how ChatGPT, BERT, or other powerful language models actually work? What if you could build your own Tiny Language Model (LLM) from scratch — without using any pre-trained weights, cloud GPUs, or giant datasets?

In this hands-on course, you will learn how to build a complete AI chatbot powered by a transformer-based language model using PyTorch, all from scratch and on your local machine. This course is designed for anyone who wants to truly understand how LLMs work — from data tokenization to training and inference.

You will begin with fundamentals of PyTorch, then build your own TinyGPT model (a simplified GPT-style transformer). You'll create your own word-level tokenizer, design a training dataset, implement a custom transformer model, train it on CPU, and finally deploy it as an interactive chatbot via CLI and (optionally) a browser UI.

This is not just about running code — you’ll understand the core concepts behind transformers, embeddings, attention, sampling, and text generation, empowering you to build your own AI applications in the future.

By the end of this course, you will have built a real, working AI chatbot powered entirely by your own model — and you’ll truly understand how it works.

Course features:

  • Beginner-friendly

  • Hands-on coding

  • Real-world project

  • Pure CPU-based

What Learners Will Build

  • A tiny LLM (language model)

  • Custom tokenizer (word-level)

  • PyTorch model architecture

  • Training and inference pipeline

  • Interactive chatbot (terminal + web)

  • Full understanding of attention, generation, and sampling

  • The confidence to build their own domain-specific chatbot

Who this course is for:

  • Aspiring AI and ML developers who want to go beyond pre-trained models and understand how language models are built from scratch
  • Developers and engineers looking to deepen their knowledge of PyTorch, NLP, and transformer architecture through practical, hands-on projects
  • Computer science and data science students who want to build their own language model and chatbot as a portfolio project
  • Educators and trainers who want to teach the principles behind GPT-like models in a lightweight and understandable way
  • Tech enthusiasts and hobbyists curious about how ChatGPT works and eager to create their own tiny version of it – fully offline, with no APIs
  • This course is not for those looking for drag-and-drop tools or pre-trained model fine-tuning only – here, you will build and train everything from the ground up!

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