๐๐๐ถ๐น๐ฑ ๐ฌ๐ผ๐๐ฟ ๐ข๐๐ป ๐ฆ๐ต๐ฎ๐ธ๐ฒ๐๐ฝ๐ฒ๐ฎ๐ฟ๐ฒ๐ฎ๐ป ๐๐๐
You know what Large Language Models do. But do you know how they work?
You can build your own in 15 minutes using a standard laptop. You will train a model on the complete works of Shakespeare. It will not be perfect, but it will learn his rhythm and style.
This project follows the same steps used by the biggest AI companies. The only difference is the scale.
Here is how to do it:
- Setup your environment
- Install Python 3.10 or later.
- Clone the nanoGPT repository.
- Create a virtual environment and activate it.
- Install PyTorch and required libraries like numpy and transformers.
- Prepare the data
- Run the preparation script to fetch the Shakespeare dataset.
- The script builds a vocabulary of 65 unique characters.
- It turns every character into a number called a token.
- It splits the data into 90% for training and 10% for validation.
- Start training
- Run the training script using your GPU (use --device=mps for Mac or --device=cuda for NVIDIA).
- Watch the loss value. If the loss goes down, your model is learning.
- A small 4-layer transformer can finish this in about 10 minutes.
- Generate text
- Run the sample script to see your results.
- You will see text that looks like a play, even if it is not fully coherent.
Want to try something else? Replace the Shakespeare file with text from Charles Darwin or Jane Austen. The model will adapt to their specific writing patterns.
Why does this look different from ChatGPT?
The principles are the same. Commercial models simply scale three things:
โข Tokenization: They use sub-word fragments instead of single characters. โข Context Window: They look at thousands of tokens at once instead of 64 characters. โข Scale: They use hundreds of layers instead of four.
By building this, you move from a user to a creator.
Source: https://dev.to/micmath/build-your-own-shakespearean-llm-49oa
Optional learning community: https://t.me/GyaanSetuAi