Personal Computer Training of Small Language Models for Text Generation
Generated: 2026-07-11 · API: Gemini 2.5 Flash · Modes: Summary
Personal Computer Training of Small Language Models for Text Generation
Clip title: Train Your Own Tiny LLM on Your PC In Just A Few Hours Author / channel: Gary Explains URL: https://www.youtube.com/watch?v=T9egZA5ppQw
Summary
This video provides a practical guide on how to train custom Large Language Models (LLMs) on a personal computer, even without specialized high-end hardware. The main goal is to demystify the inner workings of LLMs and empower viewers to gain hands-on experience by building and training their own small-scale models. The presenter emphasizes that engaging directly with the training process is an excellent way to understand LLM mechanics, offering a “cheat” or shortcut to the complete code for those who prefer not to write it from scratch.
The video outlines two primary training exercises. First, viewers are guided to train a 10-million parameter model using the complete works of Shakespeare (a 1MB dataset). This process, based on the “LLM From Scratch” GitHub project by Angelos Perivolaropoulos, can take less than an hour with a GPU or run overnight on a CPU-only PC. After training, the model can generate text as a completion to prompts in a Shakespearean style, albeit often nonsensical. The presenter provides the necessary Python scripts and pre-trained models via a GitHub repository, along with instructions for installing prerequisites like PyTorch.
The second, more advanced experiment involves training a 24-million parameter model on the 2.1GB “TinyStories” dataset. This dataset is unique for containing short stories with words and sentence structures that a typical 3-4 year-old would understand, deliberately simplifying the language to facilitate training on smaller models. Training this larger model requires a GPU and typically takes a few hours. The custom train.py script for this project is more sophisticated, allowing for automatic model sizing based on data and the ability to interrupt and resume training, which is useful for longer training sessions. The resulting model can generate remarkably fluent and consistent short stories.
Ultimately, the video concludes by highlighting the educational value of these hands-on experiments. While the trained models are “tiny” compared to state-of-the-art LLMs, they effectively demonstrate the fundamental concepts and dynamics of how large language models learn to generate coherent text based on their training data. The presenter encourages further experimentation with different datasets available on platforms like Hugging Face, enabling viewers to explore the capabilities and limitations of LLMs by varying parameters and training data.
Video Description & Links
Description
How to train your own 10M parameter LLM | Get started with SerpApi using 250 free credits: https://serpapi.com/?utm_source=youtube&utm_campaign=garyexplains_july_2026
Here is everything you need to train your own 10 million parameter LLM using the complete works of Shakespeare. It works on a PC with or without a GPU.
LLM Links:
- https://github.com/angelos-p/llm-from-scratch/tree/main
- https://github.com/garyexplains/examples/tree/master/tinynanogpt_chartok
- https://github.com/garyexplains/examples/tree/master/tinystoriesgpt
- https://huggingface.co/datasets/roneneldan/TinyStories/tree/main
- https://huggingface.co/datasets/next-token/clean-PD-16000-books3
This video was sponsored by SerpApi.
00:00 Intro 02:07 10M parameter LLM trained on Shakespeare 07:40 24M parameter LLM trained on Tiny Stories 11:15 Wrap
X: https://twitter.com/garyexplains GitHub: https://github.com/garyexplains
Tags
Gary Explains, Tech, Explanation, Tutorial, LLM, Python, Pytorch, Tiny LLM, SLM, Small LLM, Train an LLM, Local LLM
URLs
- https://serpapi.com/?utm_source=youtube&utm_campaign=garyexplains_july_2026
- https://github.com/angelos-p/llm-from-scratch/tree/main
- https://github.com/garyexplains/examples/tree/master/tinynanogpt_chartok
- https://github.com/garyexplains/examples/tree/master/tinystoriesgpt
- https://huggingface.co/datasets/roneneldan/TinyStories/tree/main
- https://huggingface.co/datasets/next-token/clean-PD-16000-books3
- https://twitter.com/garyexplains
- https://github.com/garyexplains